Q3 2024 eXp World Holdings Inc Earnings Call
In our meta versus on the web frame.
Speaker Change: My name is Denise Garcia and I manage Investor Relations for ESP World Holdings today, we will begin our earnings Fireside chat with prepared remarks from Glen Sanford founder Chairman and CEO of ESP World Holdings, Leo Pereira CEO of VX P Realty, Wendy foresight CMO of at EXL.
Speaker Change: T Realty, Seth Seigler, Chief Innovation Officer of EXE reality, and Ken Cheng Principal financial Officer, and Chief Accounting Officer of ESP World Holdings. Following our prepared remarks, we will open the call to a Q&A session with our speakers, let's begin with a review of the forward looking statements.
Speaker Change: There'll be a number of forward looking statements made today that should be considered in conjunction with the cautionary statements contained in the company's SEC filings forward looking statements are subject to various risks and uncertainties that could cause our actual results to differ materially from these statements. Please see our filings with the SEC, including our most.
Speaker Change: Recently filed annual report on Form 10-K, and quarterly reports on Form 10-Q for a discussion of specific risks that may affect our business performance and financial condition.
Speaker Change: We assume no obligation to update or revise any forward looking statements or information.
Speaker Change: As a reminder, today's call is being recorded and a replay will also be made available on ESP World Holdings Dot Com now for a few logistics and we'll get started.
Speaker Change: For those of you joining and frame today welcome to our meta versus on the web.
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Speaker Change: Should you wish to ask a question during our presentation you can enter your questions by scanning the QR code presented on the screen with your mobile phone or go to <unk> Dot com and typing the event code E X Ti from there you can submit a question or vote up an existing question by giving.
Speaker Change: A thumbs up if you'd like to have that question asked this screen, we will remain up on the on the right hand side of the stage.
Now I'll turn the fireside chat over to our speakers before opening the call to questions.
Speaker Change: <unk> you may begin.
Speaker Change: Alright, Thanks, Denise and thanks, everyone for joining us today.
Speaker Change: It was a busy Q3.
Speaker Change: As you guys are aware, obviously, we just came off of our big event.
Speaker Change: In Miami <unk> 2024, I know there were some investors and some analysts that were attending.
Speaker Change: It was pretty cool to get some notes even in the background from the event I think one of our analysts actually sent me a note.
Speaker Change: About our our fund and so you want to see something viral go check that out anyway.
<unk>.
Speaker Change: This this quarter.
Speaker Change: Wanted to just continue to refocus us on what we're working on obviously, we've got DXP.
Speaker Change: North America, and Leo is going to really talk about things that are going on.
Speaker Change: And Wendy as well on ESP North America on international.
Speaker Change: That's where I have been focusing on a ton of my time since July and we really turn things around in a major way obviously, we also have.
Speaker Change: Success Enterprises, and then of course, where you're actually seeing right now which is frame yard.
Speaker Change: Which is truly our digital workplace.
Initially we've got E X P E.
Speaker Change: I'm going to invite those of you who are interested to come.
Leo: North America, and Leo is going to really talk about things that are going on.
Speaker Change: Come by our international offices at some point at DXP.
Leo: And Wendy as well on ESP North America on international.
Speaker Change: World Slash International So just keep that in the back of your mind, but you can come in and see how we operate and see how we work we're working 24, 7% around the globe.
Speaker Change: That's where I have been focusing on a ton of my time since July and we've really turned things around in a major way.
But we've really just built this amazing platform where.
Speaker Change: Obviously, we also have.
Speaker Change: Success Enterprises, and then of course, where you're actually seeing right now which is frame <unk> Io.
Speaker Change: Agents are our singular focus.
Speaker Change: We've spent more than 15 years building out the <unk> XP platform. We just went past our 15 year anniversary at the beginning of October.
Speaker Change: Which is truly our digital workplace.
Speaker Change: I'm going to invite those of you who are interested to come.
Speaker Change: Come by our international offices at some point at DXP.
We are we are doing some amazing things.
Speaker Change: Obviously, we think about things like our NPS.
Speaker Change: World Slash International So just keep that in the back of your mind, but you can come in and see how we operate and see how we work we're working 24, 7% around the globe.
Speaker Change: We've got phenomenal scores on that.
Speaker Change: We like to think about the idea that if we are above a 70 honor agent NPS. We're in good shape and we're in great shape right now.
Speaker Change: But we really just built this amazing platform, where our agents our singular focus.
Speaker Change: We actually increased to 76.
Speaker Change: We've spent more than 15 years building out the <unk> platform.
Speaker Change: But thats going to fluctuate up and down so the fact that it's above 70 is an amazing number because we know that if we're above 70, we're really attracting agents and we're growing our market share.
Speaker Change: We just went past our 15 year anniversary at the beginning of October.
Speaker Change: We are we are doing some amazing things.
Speaker Change: Obviously, we think about things like our NPS.
And really we think about what we're here to do we're really here to build the most agent centric real estate brokerage on the planet.
Speaker Change: And we've got phenomenal scores on that.
Speaker Change: We like to think about the idea that if we are above the 70 on our agent NPS.
Speaker Change: Again, we will touch on North America.
Speaker Change: But we did purchase a few things we did a few things.
Speaker Change: In good shape and we're in great shape right now.
<unk> again, we will touch on that and when they may as well.
Speaker Change: We actually increased to 76.
Speaker Change: But we're also doing a lot of stuff with with AI with Ziegler will we'll talk about that so a lot of really.
Speaker Change: But thats going to fluctuate up and down so the fact that it's above 70 is an amazing number because we know that if we're above 70, we're really attracting agents and we're growing our market share.
Speaker Change: Really great stuff.
Speaker Change: International as I mentioned, that's really where we're focused.
Speaker Change: And really if we think about what we're here to do we're really here to build the most agent centric real estate brokers on the planet.
Speaker Change: In July.
Speaker Change: I jumped in with the international team, we have an amazing team on the international side of the business.
Again, we will touch on North America.
Speaker Change: And.
Speaker Change: We've now grown our revenue just in the last 12 months, we've grown it by 63%.
Speaker Change: But we did purchase a few things we did a few things.
Speaker Change: <unk> again, we'll touch on that and Wyndham as well.
Speaker Change: And and I mentioned that.
Speaker Change: But we're also doing a lot of stuff with with AI with Seth Seigler will we'll talk about that so a lot of really really great stuff.
Speaker Change: It's a lagging indicator, but it really speaks to the fact that we've got quality agents in the international domain, and we're focusing quite a bit on what we need to do to grow that.
Speaker Change: International as I mentioned, that's really where we're focused.
Speaker Change: In the prior.
Speaker Change: In July.
Speaker Change: 12 months to 18 months prior.
Speaker Change: Yes.
Speaker Change: I jumped in with the international team, we have an amazing team on the international side of the business.
Speaker Change: Prior to jumping in we hadn't opened up a single country. We now have three countries that were going to be opening up in.
Speaker Change: And we.
Speaker Change: We have now grown our revenue just in the last 12 months, we've grown it by 63%.
Speaker Change: In early 2025, we have made great progress on.
Speaker Change: Turkey.
Speaker Change: Peru.
Speaker Change: And and I mentioned that.
And now Egypt, and we've got just an amazing team there working on helping us grow out.
Speaker Change: It's a lagging indicator, but it really speaks to the fact that we've got quality agents in the international domain, and we're focusing quite a bit on what we need to do to grow that in.
Youll see this home Hunter dock global.
Speaker Change: And one of the things that we've learned over the last.
Speaker Change: In the prior.
Speaker Change: 12 months to 18 months.
Speaker Change: Number of years.
Speaker Change: And especially <unk>.
Speaker Change: Prior to jumping in we hadn't opened up a single country.
Speaker Change: Getting involved and I remember Leo and I, we flew over sport DXP Con.
Speaker Change: Now have three countries that were going to be opening up.
International and we sat with some of the portals.
Speaker Change: In early 2025, we have made great progress on.
Speaker Change: That that basically control the real estate industry outside of North America.
Speaker Change: Turkey.
Speaker Change: Peru, and and and now Egypt, and we've got just an amazing team there working on helping us grow out.
Speaker Change: In North America, we.
We have the benefit of the multiple listing services all basically keeping the portals.
Speaker Change: Youll see this home Hunter Dot global.
Speaker Change: One of the things that we've learned over the last.
Speaker Change: Check.
Speaker Change: Meaning that no single portal has it.
Speaker Change: A number of years.
Speaker Change: Has a dominant market share of listings.
Speaker Change: And especially <unk>.
Speaker Change: Getting involved and I remember Leo and I, we flew over for <unk>.
And certainly there are is money to be made in the portal space. You can certainly look at the market cap of Zillow and realtor com and how important these are homes dot com as well.
Speaker Change: International we sat with some of the portals.
Speaker Change: That that basically control the real estate industry outside of North America.
Speaker Change: <unk>.
Speaker Change: Businesses of agents.
Speaker Change: However internationally.
Speaker Change: In North America, we.
Speaker Change: Portals, there is no MLS to basically keep.
Speaker Change: We have the benefit of the.
Speaker Change: Multiple listing services, all basically keeping the portals.
Speaker Change: Them in check you have to actually go and search multiple real estate portals as a consumer.
Speaker Change: In check.
Speaker Change: Meaning that no single portal has.
Speaker Change: And so one of the things that we're really excited about is our partnership with a platform called home Hunter Dock Global and you can go check it out, but if you're searching for properties outside of North America, especially in the markets that DXP is doing business and youre going to want to go into the chrome.
Speaker Change: As a dominant market share of listings.
Speaker Change: And certainly there are is money to be made in the portal space. You can certainly look at the market cap of Zillow and realtor com and how important these are homes dotcom as well in the <unk>.
Speaker Change: Appstore.
Speaker Change: Businesses of agents however.
And the browser extension store.
Speaker Change: However internationally.
Speaker Change: Download the home Hunter Dock Global extension and the reason being is if you've ever search internationally. One you don't know all the websites that listing that you are looking for might be on so you need a tool to keep track of all the different websites and then on top of that you need to be able to keep track of the listings that you've actually.
Speaker Change: Portals, there is no MLS to basically keep.
Speaker Change: Them in check you have to actually go and search multiple real estate portals as a consumer.
Speaker Change: And so one of the things that we're really excited about is our partnership with a platform called home Hunter Dock Global and you can go check it out, but if you're searching for property outside of North America, especially in the markets that DXP is doing business and youre going to want to go into the chrome.
Speaker Change: <unk> seen on the various different websites and home hundred Doc Global is actually the first tool of its kind that I'm aware of where you as a consumer can keep track of all of that important information, but it also will connect you with an ESP agent and also help.
Speaker Change: App store.
Speaker Change: And the browser extension store and download the home Hunter Dock Global extension and the reason being is if you've ever search internationally. One you don't know all the websites that listing that you are looking for might be on so you need a tool to keep track of all of the different websites and then on top of that you need to be able to.
Speaker Change: Promote <unk>.
Speaker Change: <unk> listings.
Speaker Change: To those individuals who actually use the home Hunter Dot global extension. So it's a great consumer tool to great agent engagement tool and again.
Speaker Change: Real excited about a lot of the things that we're doing we do have more countries in the wings.
Speaker Change: Keep track of the listings that you've actually seen on the various different websites.
Speaker Change: And home hundred Doc Global was actually the first tool of its kind that I'm aware of where you as a consumer can keep track of all of that important information, but it also will connect you with an ESP agent and also help.
Speaker Change: We're in active conversations with two to three.
Speaker Change: Countries that we are excited about it.
Speaker Change: <unk> very shortly hopefully before the end of the year, we'll have at least one if not.
Speaker Change: A lot more countries that we'll be announcing but theres a lot of good stuff going on on the on the international front.
Speaker Change: Promote.
Speaker Change: DXP listings.
Speaker Change: To those individuals who actually use the home Hunter Dot global extension. So it's a great consumer tool to great agent engagement tool and again.
Speaker Change: And so that with that let me go ahead and turn it over to Leo and he can walk you through some of the highlights of North America wheel.
Speaker Change: Real excited about a lot of the things that we're doing we do have more countries in the wings.
Leo Pereira: Thanks, Glenn and thanks to everyone, who is joining us today I'm really thrilled with the number of quality independent brokers and teams. We brought on during this last quarter. This is success is a direct results of a lot of the strategic growth that we've been focusing on the XP.
Speaker Change: We're in active conversations with two to three.
Speaker Change: Countries that we are excited about announcing very shortly hopefully before the end of the year, we'll have at least one if not more countries that we'll be announcing but theres a lot of good stuff going on on the on the international front.
Leo Pereira: We're very effectively align with our growth team and our marketing efforts to attract some of the most respected and highest performing teams in the industry.
These additions are not just a testament to our strategy, but also are significantly strengthening our DXP market position.
Speaker Change: And so that with that let me go ahead and turn it over to Leo and he can walk you through some of the highlights of North America.
Leo Pereira: Earlier this year I spoke about key initiatives that are driving this launch this growth.
Leo: Thanks, Glenn and thanks to everyone, who is joining us today I'm really thrilled with the number of quality independent brokers and teams. We brought on during this last quarter. This is success is a direct results of a lot of the strategic growth that we've been focusing on at DXP were very effectively align with our growth team and our marketing efforts to attract some of the most respected.
Leo Pereira: We talked about booth thrive revenue share to know and I'm happy to say these programs are making a notable impact on July one we launched fast start attraction bonus and I'm proud to announce that in Q3 alone we paid over $5 million to agents, who are part of this initiative.
Leo Pereira: We also announced more incentives that he speak on last week to further support our icons in our productive agents the icon incentive program and revenue share capping incentive program are as follows the icon incentive program will credit icon agents up to 30 frontline qualifying agents for 13 months maximizing the revenue share potential for all seven level.
Speaker Change: Highest performing teams in the industry.
Speaker Change: These additions are not just a testament to our strategy, but also are significantly strengthening our ESP market position.
Speaker Change: Earlier this year I spoke about key initiatives that are driving this launch this growth.
Speaker Change: We talked about booth thrive revenue share too and I'm happy to say these programs are making a notable impact on July one we launched fast start attraction bonus and I'm proud to announce that in Q3 alone we paid over $5 million to agents, who are part of this initiative. We also announce more incentives at DXP Con last week to further support our.
Leo Pereira: <unk>.
Leo Pereira: And the revenue share capping incentive program provides an additional revenue stream for agents that have capped by creating them with 10 <unk> to maximize their any potentials through levels five to 13 months both of those programs will start started November one.
Leo Pereira: On the first quarter call I mentioned.
Speaker Change: Icons in our productive agents the icon incentive program and revenue share capping incentive program are as follows.
Leo Pereira: That I promised conversations we're having with small to midsize independents and anticipate an influx of new groups, joining ESP with dozens or even hundreds of agents at a time I am pleased to report that we delivered on those promises during the third quarter, we welcomed several independent brokerages with contributing hundreds of agents and represented one hundreds of millions of volume at the <unk>.
Speaker Change: The icon incentive program will credit icon agents up to 30 frontline qualifying agents for 13 months maximizing the revenue share potential for all seven levels in there.
Speaker Change: The revenue share capping incentive program provides an additional revenue stream for agents that have capped by creating them with 10 <unk> to maximize their any potentials through levels five to 13 months both of those programs will start starting November one.
Leo Pereira: Time. These brokerages include award winning teams that have recognized that are recognized powerhouses in their regions to further elevate our profile.
Leo Pereira: Brandon in Britain him in the Maryland, Delaware Group sold 900 homes last year and are on their joined our company last quarter.
Speaker Change: On the first quarter call I mentioned.
Speaker Change: That I promised conversations we're having with small to midsize independents and anticipate an influx of new groups, joining ESP with dozens or even hundreds of agents at a time I am pleased to report that we delivered on those promises during the third quarter, we welcomed several independent brokerages with contributing hundreds of agents and representing hundreds of millions of volume at.
Leo Pereira: Shanteau Ray and Kinzel royalty, which was one of the original cloud based brokerage competitors, we saw pop up about five years ago license in over 20 states came over as an entire company.
Leo Pereira: And Michael Levy with the Grand Lux Realty team in New York came over with just over 200 agents.
Speaker Change: The same time. These brokerages include award winning teams that have recognized that are recognized powerhouses in their regions to further elevate our profile.
That's really exciting for us we continue to see this momentum and continue to say that there are many many conversations like that still in process coming over so.
Speaker Change: Brandon Britain him in the Maryland, Delaware Group sold 900 homes last year and are on their joined our company last quarter.
Speaker Change: Going into 225, we're really excited for the quality and production we continue to attract to the platform with that I'll pass it over to our CMO Wendy foresight.
Speaker Change: <unk> and Kinzel Realty, which was one of the original cloud based brokerage competitors, we saw pop up about five years ago license in over 20 States came over as an entire company and Michael Levy with the Grand Lux Realty team in New York came over with just over 200 agents.
Wendy Foresight: We'll share some updates and exciting improvements in marketing and programs to enhance our agent value proposition and making ESP so attractive to agents Wendy.
Wendy Foresight: Thanks layout and Hello, everyone. Thanks for joining us today.
Speaker Change: That's really exciting for us we continue to see this momentum and continue to say that there are many many conversations like that still in process coming over so.
Speaker Change: And ESP, a little over six months ago, having a background in real estate, what DXP grow and innovate and I'm excited to be part of the team building. This next chapter of this amazing company I'll start with my first mandate evolving the ESP brand.
Speaker Change: 225, we're really excited for the quality and production we continue to attract to the platform with that I'll pass it over to our CMO, Wendy <unk>, who will share some updates and exciting improvements in marketing and programs to enhance our agent value proposition and making ESP so attractive to agents.
The next slide I'll walk you through the key focus areas of the ESP to point, our brand strategy to further align <unk> and edge with its leadership position in innovation technology and agent empowerment.
<unk>.
Wendy <unk>: Thanks layout and Hello, everyone. Thanks for joining us today I joined ESP, a little over six months ago, having a background in real estate, what DXP grow and innovate and I'm excited to be part of the team building. The next chapter of this amazing company.
Speaker Change: We focus on updating the brand aesthetic towards a more modern look and feel and we call. This our brand polo App, we shifted away from the Royal Blue primary brand color to a dark Navy renews, the outdated box slowdown treatment and aligned all supporting.
Wendy <unk>: I'll start with my first mandate involving the ESP brand on.
Wendy <unk>: On the next slide I'll walk you through the key focus areas.
Speaker Change: <unk> and program logos for an overall brand consistency.
Wendy <unk>: <unk> two point, our brand strategy to further align <unk> and edge with its leadership position in innovation technology and agent empowerment.
Speaker Change: We focused on increasing brand awareness of the ESP brand on.
On the next slide I'll walk you through some recent press coverage, we secured as pilot increasing awareness of ESP initiatives.
Wendy <unk>: We focused on updating the brand.
Wendy <unk>: Towards a more modern look and feel and we call. This our brand allow app.
Speaker Change: As part of our brand as the latest thing we've made tremendous strides in boosting our thought leadership and increasing media visibility and thrilled to share that we've appeared in over 7700 articles across major media outlets such as the Wall Street Journal CNBC <unk> CNN along with <unk>.
Shifting away from the Royal Blue Pinery brand color to a dark Navy renews, the outdated backflow, though treatment and aligned all supporting product and program allowed us for an overall brand consistency.
Wendy <unk>: Second we focused on increasing brand awareness I think DXP brand.
Speaker Change: Top industry publications like housing wire in the news and RIS media. These.
Wendy <unk>: On the next slide I'll walk you through some recent press coverage, we secured as pilot increasing awareness of ESP initiatives.
Speaker Change: These appearances have generated over 14, 7 billion views and impressions, giving us an incredible share of voice in the industry.
Wendy <unk>: As part of our brand and the latest thing we've made tremendous strides in boosting our thought leadership and increasing media visibility and thrilled to share that we have appeared in over 7700 articles across major media outlets such as the Wall Street Journal CNBC <unk> CNN along with <unk>.
This level of exposure is a clear reflection on the growing recognition of ESP leadership and the ESP brand <unk>.
Speaker Change: Establishing a strong brand voice is key as we continue to enhance our value proposition and create even more opportunities for our agents.
Top industry publications like housing wire in the news and RIS media. These.
Speaker Change: One exciting opportunity we have been focused on is empowering our agents to compete in the luxury market, which I'll discuss more in the next slide.
Wendy <unk>: These appearances has generated over 14 7 billion views and impressions, giving us an incredible share of voice in the industry.
Speaker Change: We launched ESP luxury just two years ago, and it's been very well received we've expanded into all international markets during 2024 and compared to the third quarter 2023, we grew our agent membership by 94%.
Wendy <unk>: This level of exposure is a clear reflection on the growing recognition of ESP leadership and the ESP brand.
Wendy <unk>: <unk> has strong brand voice is key as we continue to enhance our value proposition and create even more opportunities for our agents.
Speaker Change: Luxury division offers agents and our lead suite of integrated solutions to maximize their earnings potential sign and sound more high priced listings and not just grow but thrive in the luxury space.
Wendy <unk>: One exciting opportunity we've been focused on is empowering our agents to compete in the luxury market, which I'll discuss more on the next slide.
Speaker Change: I'm delighted to announce that on October 18th ESP royalty acquired Luxe VP of marketing technology platform that helps agents promote their luxury listings are luxury agents have been using this platform to improve their marketing efficiency build their brand identity and provide successful strategy.
Wendy <unk>: We launched ESP luxury just two years ago, and it's been very well received we've expanded into all international markets during 2024 and compared to the third quarter 2023, we grew our agent membership by 94%.
Wendy <unk>: The luxury division offers agents and our lead suite of integrated solutions to maximize the earnings potential signing sound more high priced listings and not just grow but thrive in the luxury space and.
Speaker Change: <unk> for client acquisition and management.
Speaker Change: Owning this suite of solutions built for the luxury space is an important strategic move that gives ESP agents distinct competitive advantages, allowing our agents and ESP brand to stand out in the luxury market.
Wendy <unk>: I am delighted to announce that on October 18.
Wendy <unk>: <unk> acquired <unk>, a marketing technology platform that helps agents promote their luxury.
Speaker Change: Moving on to the next slide.
Speaker Change: Wrap up by sharing some groundbreaking announcements and exciting new initiatives, we unveiled last week at DXP time.
Wendy <unk>: Our luxury agents have been using this platform to improve their marketing efficiency build their brand identity and provide successful strategies for client acquisition and management.
Speaker Change: This year's event with hosted for the first time in Miami and brought together 4300 attendees.
Wendy <unk>: Owning this suite of solution built for the luxury space is an important strategic move that good DXP agents distinct competitive advantages, allowing our agents and DXP brand to stand out in the luxury market.
Speaker Change: Energy was electric.
Speaker Change: During the XP Econ, we made several major announcements enhanced features to <unk> to point out that take agent collaboration and growth to the next level.
Speaker Change: Global partnership with Camber, the world's leading digital design platform, bringing powerful creative design tools to our agents.
Wendy <unk>: Moving on to the next slide.
Wendy <unk>: Wrap up by sharing some ground breaking announcements and exciting new initiatives, we unveiled last week at DXP time.
Speaker Change: <unk> shipped the CCU to supercharge agent and team productivity the acquisition of <unk>, which I just talked about the.
Wendy <unk>: This year's event with hosted for the first time in Miami and brought together 4300 attendees.
Speaker Change: Launch of a fully designed and re imagined ESP Realty dot com and ESP royalty dot CA websites.
Wendy <unk>: Energy was electric.
Wendy <unk>: During the XP Con we made several major announcements enhanced features to <unk> to point out that take agent collaboration and growth to the next level.
Speaker Change: Expansion into three international countries that Glenn mentioned to further grow our global print footprint.
Wendy <unk>: Global partnership with Canada, the world's leading digital design platform, bringing powerful creative design tools to our agents.
And if that wasn't enough we kicked off <unk> first ever hackathon and innovative event powered by open AI cutting edge technology, where talented participants tackled unique challenges facing ESP realty and the broader real estate industry.
Wendy <unk>: <unk> shipped the CCU to supercharge agent and team productivity the acquisition of <unk>, which I just talked about the.
Wendy <unk>: The launch of a fully designed and re imagined ESP royalty dot com and ESP royalty dot CA websites.
Speaker Change: Now I'm going to hand, it over to Seth Seigler ESP, Chief Innovation officer to dive into the exciting AI based innovations, we're bringing to life.
Wendy <unk>: Expansion into three international countries that Glenn mentioned to further grow our global print footprint.
Seth Seigler: Thanks, Wendy and thanks, everybody Super excited to be here today to get into our latest tech and innovation, we're really pushing the boundaries and adopting AI based technology across the entire business, we're really trying to redefine what's possible in real estate tech setting new standards for the industry itself.
Wendy <unk>: And if that wasn't enough we kicked off <unk> first ever hackathon and innovative event powered by open AI is cutting edge technology, where talented participants tackled unique challenges facing ESP realty and the broader real estate industry now.
Seth Seigler: <unk>, we've always been on a relentless journey of innovation.
Speaker Change: Now I'm going to hand, it over to Seth Seigler Dxp's, Chief Innovation officer to dive into the exciting AI based innovations, we're bringing to life.
Seth Seigler: Ali it's using technology to really empower agents and transform the way that we do things in the business.
Seth Seigler: And we're really focused on building a future where agents are equipped to.
Seth Seigler: Thanks, Linda and thanks, everybody Super excited to be here today to get into our latest tech and innovation, we're really pushing the boundaries and adopting AI based technology across the entire business, we really kind of redefine what's possible in real estate tax setting you finish an industry itself.
Seth Seigler: To be transformative and what they do giving them the tools that they need to make that possible, enabling enabling them to thrive and succeed at unmatchable levels. That's always our goal. So part of this journey is our unwavering focus on leveraging today as most cutting edge AI solutions to really revolution.
Seth Seigler: On <unk>, we've always been on a relentless journey of innovation.
Seth Seigler: The business in four key areas that we think hold the most opportunity for growth.
Ali it's using technology to really empower agents and transform the way that we do things in the business.
Seth Seigler: And opportunity.
That's where areas are agent productivity operational efficiency inaccuracy.
Seth Seigler: And we're really focused on building a future where agents are equipped to.
Seth Seigler: Internal empowerment, and agility and lastly, future applications, so to get into it starting with agent productivity.
To be transformative and what they do giving them the tools that they need to make that possible, enabling enabling them to thrive and succeed at unmatchable levels Thats always our goal. So part of this journey is our unwavering focus on leveraging today as most cutting edge AI solutions to really revelation.
Our commitment to our agents means that we always need to be there for them whenever they need us.
Seth Seigler: With AI now integrated into our existing expert care desk and the omni lunar two point out we've really created an always on support system that offers agents around the clock assistance and really empowers them with instant answers and support whenever they need it wherever they are located.
Seth Seigler: The business in four key areas that we think hold the most opportunity for growth and.
Seth Seigler: And opportunity.
Seth Seigler: That's where areas are agent productivity operational efficiency inaccuracy inter.
Seth Seigler: Agent count to get real time, info and interaction with their own business via natural language chat using Luna, which is using NLP AI to deeply integrate into several of our existing DXP systems.
Seth Seigler: Internal empowerment, and agility and lastly, future applications, so to get into it starting with agent productivity.
Seth Seigler: Our commitment to our agents means that we always need to be there for them whenever they need us.
Seth Seigler: In terms of operational efficiency and accuracy.
Seth Seigler: AI is now integrated into our existing expert care desk and the omni Luna to point out we've really created an always on support system that offers agents around the clock assistance and really empowers them with instant answers and support whenever they need it wherever they are located.
Seth Seigler: Leaning into the power of AI to create tools and workflows that augment simplify streamline and add a lot of scalability to everything that we're doing from document processing to transaction processing software development.
Our in House Task Center, a project that we built here is only accelerating these workflows.
Seth Seigler: <unk> counts against real time, info and interaction with their own business.
Seth Seigler: Routing tasks, ensuring projects remain on task.
Seth Seigler: Via natural language chat using Luna, which is using NLP AI to deeply integrate into several of our existing ESP systems.
And ultimately making operations smoother <unk>.
Seth Seigler: <unk> and more effective.
Seth Seigler: So in terms of.
Seth Seigler: In terms of operational efficiency and accuracy, we're really leaning into the power of AI to create tools and workflows that augment simplify streamline and add a lot of scalability to everything that we're doing from document processing to transaction processing software development.
Seth Seigler: Internal empowerment and agility for the SaaS.
Yes.
Seth Seigler: At only about helping agents AI can help us in all sorts of different areas, we're using and also to empower our whole team internally where.
Seth Seigler: We're enabling our staff to tackle challenges and manage tasks independently supported by AI, driven augmentation and assistance in part.
Seth Seigler: Our in House Task Center, a project that we built here is only accelerating these workflows by smartly routing tasks ensuring projects remain on task.
Seth Seigler: Thanks to our ever deepening partnership with opening.
Here in this space hundreds of our staff members now have access to our enterprise open AI platform and have created custom GPT assistance that help them with their daily tasks, enabling them to work smarter faster and more effectively than ever before without having to write any code or or wait on any solutions to be built.
Seth Seigler: And ultimately, making operations smoother more measurable and more effective.
Seth Seigler: In terms of.
Seth Seigler: Internal empowerment and agility for the SaaS.
Seth Seigler: Yes.
Seth Seigler: At only about helping agents AI can help us in all sorts of different areas. We're using it also to empower our whole team internally are enabling our staff to tackle challenges and manage tasks independently supported by AI, driven augmentation and assistance in part.
Seth Seigler: For them, they can really take that into their own hands and then lastly, future applications. So it's still early days for some of this technology, but we see incredible opportunity for the future, including innovation and education mentoring as well as rapid prototyping of new ideas and software and enables us to move faster.
Seth Seigler: Thanks to our ever deepening partnership with opening at least here in this space hundreds of our staff members now have access to our enterprise open AI platform and have created custom GPT assistance that help them with their daily tasks, enabling them to work smarter faster and more effectively than ever before without having to <unk>.
Seth Seigler: And more efficiently some of these initiatives are already actually in flight in the early stages.
Seth Seigler: Massively excited about the possibilities that they can bring to transform what we're doing here at DXP and revolutionize even the industry as a whole.
Seth Seigler: Any code or or wait on any solutions to be built for them. They can really take that into their own hands and then lastly, future applications. So it's.
Seth Seigler: Just as the XP pioneered the industry as the first cloud based brokerage.
Speaker Change: We're taking a leading position in AI adoption is Rob and I couldnt be more pumped up to be here, helping to drive this initiative forward and ride the wave of innovation.
Seth Seigler: Still early days for some of this technology, but we see incredible opportunity for the future, including innovation and education mentoring as well as rapid prototyping of new ideas and software and enables us to move faster and more efficiently. Some of these initiatives are already actually in flight in the early stages and I'm massively excited.
Speaker Change: Same thing the way that all industries are working in real estate is no exception there so.
Speaker Change: We're just looking to continue to transform the way that we serve our agents our clients and our communities.
Seth Seigler: About the possibilities that they can bring to transform what we're doing here at DXP and revolutionize even the industry as a whole.
No.
Speaker Change: That brings us to something exciting as part of this commitment we are launching and Investor Relations GPT feature on our IR website. So investors can get a small taste of how game changing this technology really is and take some time and researching ESP.
Seth Seigler: So just as DXP pioneered the industry as the first cloud based brokerage.
Seth Seigler: We're taking a leading position in AI adoption as well and I couldnt be more pumped up to be here.
Seth Seigler: Going to drive this initiative forward and ride the wave of innovation. It is same thing the way that all industries are working in real estate is no exception there so.
Speaker Change: Anyone interested in researching and learning more about DXP can find the expert GPT feature on the bottom right hand side of each page across our IR website.
Expert GPT will search Dxp's SEC filings press releases earnings calls transcripts and IR related materials to find one.
Seth Seigler: We're just looking to continue to transform the way that we serve our agents our clients and our communities.
Seth Seigler: No.
Seth Seigler: That brings us to something exciting as part of this commitment, we're launching and Investor Relations GPT feature on our IR website. So investors can get a small taste of how game changing this technology really is and taking the time and researching ESP.
Speaker Change: Whatever the answer maybe to your IR related question.
Speaker Change: That's that and now I'll turn the call over to Ken to walk you through <unk> third quarter financial highlights.
Ken Cheng: Thank you Seth.
Ken Cheng: Next slide I will highlight several key metrics during the fourth quarter of 'twenty, one before to underscore our progress and our strategic initiatives.
Anyone interested in researching and learning more about DXP can find the expert GPT feature on the bottom right hand side of each page across our IR web site expert GPT will search Dxp's SEC filings press releases earnings calls transcripts and IR related materials to find whatever the answer may be <unk>.
Thus far with our Asia net promoter score.
Ken Cheng: This quarter, we achieved an <unk> of 76, which is a two point improvement compared to the third quarter of last year. This number continues to be strong as we continue to listen to our agents and invest in programs that drive their productivity through compensation incentive.
Speaker Change: A related question.
That's that and now I'll turn the call over to Ken to walk you through <unk> third quarter financial highlights.
Speaker Change: Thank you with that one.
Ken Cheng: Sales and marketing and 10 knowledge as the team discussed previously.
Speaker Change: Next slide I will highlight several key metrics during the fourth quarter of 'twenty, one before to underscore our progress and strategic initiatives.
Ken Cheng: Moving on to our agent network, our agent count decreased 4% on a year over year basis, reflecting both challenging market condition in the U S and our strategic decision to all pull unproductive agents. This move is aligned with our focus on enhancing overall productivity and efficiency.
Speaker Change: Thus far with our Asia net promoter score.
Speaker Change: This quarter, we achieved an <unk> of 76, which is a two point improvement compared to the third quarter of last year. This number continues to be strong as we continue to listen to our agents and to invest in programs that drive their productivity through compensation incentive.
Ken Cheng: Turning to our auto operating metrics real estate sales transaction unit declined 1% year over year, while real estate sales volume increased by 5% royalty cost per transaction decreased 1% as we focus on operating efficiency, while continuing to invest in our AGM. We believe we are among the most.
Speaker Change: Sales and marketing and technology as the team discussed previously.
Moving on to our agent network, our agent count decreased 4% on a year over year basis, reflecting both challenging market condition in the U S and our strategic decision to offer or unproductive agents. This move is aligned with our focus on enhancing overall productivity and efficiency.
Ken Cheng: Efficient company in our industry.
We remain focus on reducing our cost per transaction moving forward.
Ken Cheng: Now, let's discuss our financial metrics revenue for the third quarter was $1 billion $231 million, a 2% increase year over year.
Speaker Change: Turning to our other operating metrics real estate transaction unit declined 1% year over year, while real estate sales volume increased by 5% royalty cost per transaction decreased 1% as we focus on operating efficiency, while continuing to invest in our agent. We believe we are among the most.
Our Q3 revenue growth was due to higher real estate sales volume and an increase in agent productivity, which are detailed in the next slide.
Ken Cheng: Fourth quarter, adjusted EBITDA was $23 9 million up 15% year over year, driven by higher revenue and lower SG&A expenses relative to prior year quarter.
Speaker Change: Efficient company in our industry.
Speaker Change: And we remain focus on reducing our cost per transaction moving forward.
Ken Cheng: Drilling down into our Q3 expenses general and administrative expenses were $61 $4 million.
Speaker Change: Now, let's discuss our financial metrics revenue for the third quarter was $1 billion $231 million, a 2% increase year over year.
Ken Cheng: Up 2% compared to the third quarter of 2000, and the fleet due to higher employee related expenses and increased legal costs related to the antitrust lawsuit.
Speaker Change: Our Q3 revenue growth was due to higher real estate sales volume and an increase in agent productivity, which I will detail in the next slide.
Ken Cheng: We provided an additional $18 million antitrust lawsuit settlement contingency provision in Q3, if you recall in Q1. This year, we provided a $60 million provision for this matter.
Speaker Change: Fourth quarter, adjusted EBITDA was $23 $9 million up 15% year over year, driven by higher revenue and lower SG&A expenses relative to prior year quarter.
Ken Cheng: After end of Q3, we have provided a total of $34 million.
Speaker Change: Drilling down into our Q3 expenses general and administrative expenses were $61 4 million.
Cost contingent provision, which was consistent with the current settlement agreement.
Speaker Change: Up 2% compared to the third quarter of 2000, and the fleet due to higher employee related expenses and increased legal costs related to the antitrust lawsuit.
Ken Cheng: Our GAAP net loss was $8 5 million.
Ken Cheng: Driven by $18 million anti trop contingency provision, excluding this $18 million convention supervision third quarter adjusted net income was $7 $8 million.
Speaker Change: We provided an additional $18 million antitrust lawsuit settlement contingency provision in Q3, if you recall in Q1. This year, we provided a $60 million provision for this matter.
Ken Cheng: Compared to $2 3 million in 2010 this week two.
Ken Cheng: 239% increase year over year.
Moving onto cash flow and capital allocation Q3, adjusted operating cash flow was $55 million.
Speaker Change: After end of Q3, we have provided a total 34 million antitrust contingent provision, which was consistent with the current settlement agreement.
Ken Cheng: Really purchased $35 million of share during the quarter. The number of share repurchase in Q3 was more than offset a number of share issue through our agent and employee stock compensation programs on.
Speaker Change: Our GAAP net loss was $8 5 million.
Speaker Change: Driven by $18 million antitrust contingency provision, excluding this $18 million convention supervision third quarter. Adjusted net income was $7 8 million.
Ken Cheng: On the next slide I will provide more detail about the driver of our revenue increase for the third quarter.
Speaker Change: Compared to $2 3 million in 2010 this week two.
Ken Cheng: This chart shows the driver behind the increase in revenue for the third quarter of <unk> to the third quarter of 2024, and the Q3 2020, if we Ah revenues too at $1.230 billion as shown by the bar on the left for the full quarter of <unk>.
Speaker Change: 239% increase year over year.
Speaker Change: Moving onto cash flow and capital allocation Q.
Speaker Change: Q3, adjusted operating cash flow was $55 million.
Speaker Change: Really purchased $35 million of shares during the quarter. The number of share repurchase in Q3 was more than offset a number of share issue through our agent and employee stock compensation programs.
For revenue increased $18 million or 2% to $1 billion and $231 million as.
Ken Cheng: As indicated by the bar on the right.
Speaker Change: Next slide I will provide more detail about the driver of our revenue increase for the third quarter.
Ken Cheng: This increase was driven by both North America royalty settlement, which including U S and Canada and our international <unk> segment, both contributed about $9 million in additional revenue during the quarter.
Speaker Change: This chart shows the driver behind the increase in revenue for the third quarter of <unk> to the third quarter of 2024 in the Q3, 25%. We are revenues too at $1 billion $230 million.
Ken Cheng: Let's delve deeper into the North America Iot business market condition in the U S led to a lower agent base, which negatively impact our impacted our revenue by approximately $75 million.
Speaker Change: Show by the bar on the left for the third quarter of 204 revenue increased $18 million or 2% to $1 $231 million.
Ken Cheng: U S home sales in third quarter, 2004 declined one 3% year over year, which pressure our agent production. We estimated the decrease in the North America royalty market reduced our revenue by about $10 million.
Speaker Change: As indicated by the bar on the right.
Speaker Change: This increase was driven by both North America royalty settlement, which including U S and Canada and our international <unk> segment, both contributed about $9 million in additional revenue during the quarter left.
Ken Cheng: However, this market decline were more than offset by game from several area in our business.
Speaker Change: <unk> deeper into the North America royalty business market condition in the U S led to a lower agent base, which negatively impact our impacted our revenue by approximately $75 million.
Ken Cheng: Give to the performance of the real estate market and the increase in our agent productivity over prior year added $55 million of revenue.
U S homes sales in third quarter, 2024 declined one 3% year over year, which pressure our agent production. We estimated the decrease in the North America royalty market reduce our revenue by about $10 million. However, this market decline.
Ken Cheng: Wholesale prices contributed incremental revenue of $32 million.
Additionally, our strategic focus on expanding our leaf referral and other ancillary services brought in an $8 million potline grow.
Ken Cheng: On the next slide I will provide an overview of the financial for the <unk>.
Speaker Change: Were more than offset by game from several area in our business.
Ken Cheng: North America royalty settlement continue to be the primary driver of both revenue and profit for the company.
Speaker Change: Relative to the performance of the real estate market and the increase in our agent productivity over prior year added $55 million of revenue higher home sales prices contributed incremental revenue of $32 million.
Ken Cheng: Despite the challenging U S real estate market condition, seven revenue was $1 $207 million a slight decrease from prior year due to higher home sales price and improve ESP agent productivity.
Speaker Change: Additionally, our strategic focus on expanding our leaf referral and other ancillary services brought in an $8 million hotline growth.
Ken Cheng: Adjusted EBITDA was $28 9 million.
A 6% increase year over year due to improve business efficiency.
Speaker Change: On the next slide I will provide an overview of the financial for the.
Ken Cheng: International segment revenue was $24 2 million.
Speaker Change: North America royalty settlement continue to be the primary driver of both revenue and profit for the company.
Ken Cheng: An increase of 63% primarily due to increased real estate transaction driven by by improve agent productivity.
Speaker Change: Despite the challenging U S real estate market condition, seven revenue was $1 $207 million a slight decrease from prior year due to higher home sales price and improve ESP agent productivity.
Adjusted EBITDA loss was $1 7 million.
Ken Cheng: <unk>, 7% improvement from prior year due to increased revenue and improve business efficiency auto related services, including frame and success contributed modest revenue and adjusted EBITDA loss.
Speaker Change: Adjusted EBITDA was $28 9 million.
Speaker Change: A 6% increase year over year due to improve business efficiency.
Ken Cheng: This slide highlights our solid Q3 performance across our key operational and financial metrics, which I have discussed in detail in previous slide looking ahead to the fourth quarter. We continue to anticipate downward pressure on U S existing home sales, which is consistent with third quarter commentary.
Speaker Change: International segment revenue was $24 2 million.
Speaker Change: An increase of 63% primarily due to increased real estate transaction driven by by improved agent productivity.
Speaker Change: Adjusted EBITDA loss was $1 7 million.
Speaker Change: 37% improvement from prior year due to increased revenue and improve business efficiency auto related services, including frame and success contributed modest revenue and adjusted EBITDA loss.
Ken Cheng: Barring any significant macroeconomic shifts our gross margin percentage for the full quarter expected to be generally consistent with our typical seasonal pattern in the last year's performance. We plan to continue investing in our international market and agent growth initiatives to boost productivity.
Speaker Change: This slide highlights our solid Q3 performance across our key operational and financial metrics, which I have discussed in detail in previous slide looking ahead to the fourth quarter. We continue to anticipate downward pressure on U S existing home sales, which is consistent with third quarter commentary.
Ken Cheng: I also noted that fourth quarter is seasonally our slowest quarter in terms of real estate transaction.
Ken Cheng: G&A expenses will increase sequentially from Q3 due to the annual ESG Con <unk> expenses to be recorded in Q4 <unk>.
Speaker Change: Barring any significant macroeconomic shifts our gross margin percentage for the fourth quarter expected to be generally consistent with our typical seasonal pattern in the last year's performance. We plan to continue investing in our international market and agent grow initiatives to boost productivity.
Ken Cheng: In conclusion, I am happy to report another quarter of solid execution, which leaves us well positioned to capitalize the upcoming marketable opportunity one of the real estate market turns undercover positively.
Speaker Change: With that I'd like to turn the presentation back to <unk>, who will facilitate the Q&A session. Thank you.
Speaker Change: I also noted that full quarter is seasonally our slowest quarter in terms of real estate transaction.
Speaker Change: Great. Thank.
Speaker Change: Thanks, Denise and thanks, everyone for joining us today.
Speaker Change: G&A expenses will increase sequentially from Q3 due to the annual ESG Con <unk> expenses to be recorded in Q4.
Speaker Change: Yeah.
Speaker Change: Great Alright, Thank you I'll kick it off with a question for everyone on the team.
Speaker Change: In conclusion, I am happy to report another quarter of solid execution, which leaves us well positioned to capitalize the upcoming marketable opportunity one of the real estate market turns and recover possibly.
Speaker Change: Before we open up to the general audience for questions first blend can you can you talk about why you decided to expand into Turkey, Peru, and Egypt in particular.
Yeah. So.
Speaker Change: With that I'd like to turn the presentation back to Denise who will facilitate the Q&A session. Thank you.
Speaker Change: Some of these were conversations that were going on.
Speaker Change: Peru.
Speaker Change: Great. Thank.
Speaker Change: Specifically.
Speaker Change: Thanks, Denise and thanks, everyone for joining us today.
Speaker Change: It was one that had been going on for a while and.
Speaker Change: Yeah.
Speaker Change: I think the team was really looking to open up some new countries. So obviously I jumped in in July.
Speaker Change: Great Alright, Thank you I'll kick it off with a question for everyone on the team.
Speaker Change: Before we open up to the general audience for questions. Our first planned can you can you talk about why you decided to expand into Turkey, Peru, and Egypt in particular.
Speaker Change: We've got a great partner.
Speaker Change: In Peru with Ricardo.
Speaker Change: Yeah.
Speaker Change: And then we had opportunities to look at a couple of other markets Turkey.
Speaker Change: Yeah. So.
Speaker Change: Some of these were conversations that were going on.
I don't know for sure that our social media, but.
Uh huh.
Speaker Change: Peru.
Speaker Change: As the gentlemen, opening up in Turkey.
Speaker Change: Specifically.
Speaker Change: It was one that had been going on for a while and.
Speaker Change: Large social media following.
Really strong real estate sales background.
Speaker Change: I think the team was really looking to open up some new countries. So obviously I jumped in in July.
Speaker Change: Really kind of <unk>, what we're looking for.
Speaker Change: We've got a great partner.
Speaker Change: And in Turkey.
Speaker Change: In Peru with Ricardo.
Speaker Change: And so pretty excited about about him and then.
Speaker Change: And.
Speaker Change: With the with Ahmad.
Speaker Change: And then we had opportunities to look at a couple of other markets Turkey.
Speaker Change: In Egypt.
Speaker Change: Very well connected buildup Dolby.
Speaker Change: Dolby MLS and Egypt comes with a strong background in organized real estate and is really enthusiastic about opening it up so we're really building around strong leaders.
Speaker Change: I don't know for certain of our social media, but.
Speaker Change: Jude.
Speaker Change: As the gentlemen, opening up in Turkey.
Speaker Change: Large social media following.
Speaker Change: And so these were three individuals who represented really the best of the best in these markets and we always say that we really focus on formerly leadership rather than focusing on the market I think previously we.
Speaker Change: Really strong real estate sales background.
Speaker Change: Really kind of <unk>, what we're looking for.
Speaker Change: And in Turkey.
Speaker Change: And so pretty excited about about him and then.
Speaker Change: With the with Ahmad.
Speaker Change: We may have taken approach, which was let's open up markets because they are good markets.
Speaker Change: In Egypt.
Speaker Change: Very well connected buildup with.
Speaker Change: Historically.
Speaker Change: Dolby MLS and Egypt comes with a strong background in organized real estate and is really enthusiastic about opening it up so we're really building around strong leaders.
In the U S and Canada, we opened up different states because we had good leaders.
Speaker Change: And so we're going back to sort of that approach and.
Speaker Change: And we've got a lot of other great conversations going on as well so as we made great leaders and they can put together the core groups and we can help them sort of dialogue and the value propositions that make sense.
Speaker Change: And so these were the three individuals who represented really the best of the best in these markets and we always say that we really focus on formerly leadership rather than focusing on the market I think previously.
We are simplifying the.
The opening up of new markets. So we're going to really are two two different cap system for international so that will.
We may have taken approach, which was let's open up markets because they are good markets.
Speaker Change: Also plan to sort of rapid rapid growth model.
Speaker Change: Historically.
Speaker Change: In the U S and Canada, we opened up different states because we had good leaders.
And we're doing some standardization.
Speaker Change: Around that.
Speaker Change: Again, we'll play out well, we just announced last week, which is probably worth noting our international sponsorship program where agents internationally can.
Speaker Change: And so we're going back to sort of that approach and.
Speaker Change: We've got a lot of other great conversations going on as well so as we meet great leaders and they can put together the core groups and we can help them sort of dial in the value propositions that make sense.
Speaker Change: Assist agents anywhere in the world to recruit in their country, even if they're not in their rupture group. So pretty excited about that so anyway, just a lot of things that we're doing on the international front that is pretty pretty exciting.
Speaker Change: We are simplifying.
Speaker Change: The opening up of new markets. So we're going to really are two two different cap system for international so that will.
Speaker Change: Thanks, Alright next question is for Leo Leo can you discuss what's driving the decrease in agent count.
Speaker Change: Also plan to sort of rapid rapid growth model.
Speaker Change: We're doing some standardization.
Speaker Change: Around that.
Leo Pereira: Yes so.
Speaker Change: Again, we'll play out well, we just announced last week, which was probably worth noting our international sponsorship program where agents internationally can.
Leo Pereira: At our size and scale, we're very susceptible to larger macroeconomic behavior from agent count so more than half of the agents who've left the XP. This year have left the industry.
Speaker Change: Assist agents anywhere in the world to recruit in their country, even if they're not in their rupture group. So pretty excited about that so anyway, just a lot of things that we're doing on the international front that is pretty exciting.
Leo Pereira: In totality, 62% of Nonproductive agents are left DXP left the industry. So if you segmented out.
Leo Pereira: Anyone curious goes to the first page of the appendices.
Leo Pereira: Close to 70 778.
Speaker Change: Thanks, Alright next question is from Leo Leo can you discuss what's driving the decrease in agent count.
Leo Pereira: Percent of the people, who left us zero to two sales, but our retention on the more productive cohort of 21, plus sales has actually decreased so our retention has increased so we're roughly 2%.
Yeah. So.
Leo: At our size and scale, we're very susceptible to larger macroeconomic behavior from agent count so more than half of the agents who have left the XP. This year have left the industry.
Leo Pereira: Attrition in the more productive part of the.
Leo Pereira: Cohorts so.
Leo Pereira: We've been really focused on that the rallying cry for this year has been where the pro is going to grow and it's really starting to continue to show in the level of talent we're attracting.
Leo: In totality, 62% of Nonproductive agents are left DXP left the industry. So if you segmented out.
Leo: Anyone curious goes to the first page of the appendices.
Leo Pereira: And a lot of the programs so.
Leo: Close to 70, 778%.
Leo Pereira: The one of the programs I'm. Most excited about is called fast cap that we rolled out and announced that DXP com.
Leo: Percent of the people who left.
Leo: Zero to two sales, but our retention on the more productive cohort.
Leo Pereira: Early signs on brand new agents getting them into productivity a six week Accountability program and then we've continued to rollout our fast track attraction bonus that we launched July one.
Leo: 21, plus sales has actually decreased so our retention has increased so we're roughly 2%.
Leo: Attrition in the more productive part of the.
Leo Pereira: Since we've launched the program, we paid out $5 million by.
Leo: Cohorts so.
Leo: We've been really focused on that the rallying cry for this year has been where the pro is going to grow and it's really starting to continue to show in the level of talent we're attracting.
Leo Pereira: By the way has no EBITDA.
They've been an impact this is all part of the Russia buckets. So it still maintains the margins were enjoying and so we're very focused on growth throughout attraction.
Leo: And a lot of the programs so.
Leo Pereira: At <unk>, we announced the icon incentive program, where.
Leo: One of the programs I'm. Most excited about is called fast cap that we rolled out and announced the DXP com.
Leo Pereira: Where we open up all seven levels to give them credit for the 30 frontline qualifiers.
Leo: Early signs on brand new agents getting them into productivity is six week Accountability program and then we've continued to rollout our fast track attraction bonus that we launched July 1st week.
Leo Pereira: And then the capping one for the first five so they're all designed to incentivize and attract production.
Leo Pereira: So.
Leo Pereira: At DXP kind of gave a keynote where based on the economic data that we pull from Fannie and several other.
Leo: Got since we've launched the program, we paid out $5 million by the way has no.
Economists were more bullish based on where rates cuts have been from starting the year to ending the year.
Leo: EBIT impact. This is all part of the rupture bucket. So it still maintains the margins were enjoying and so we're very focused on growth throughout attraction.
Leo Pereira: So we're bullish that in 2025 will have an increased transactional account over 'twenty three 'twenty, four which will probably end the year, either flat or lower than 2003. So we're bullish on all that fun stuff.
Leo: At <unk>, we announced the icon incentive program, where.
Leo: Where we open up all seven levels to give them credit for the 30 frontline qualifiers.
Leo: And then the capping one for the first five so they're all designed to incentivize and attract production.
Leo Pereira: Yes.
Back to you Wendy alright, thanks. Thanks.
Leo: So.
Speaker Change: Wendy I'll, let you ask your question. Thank you for joining US today can you discuss the timeline and budget for resolving the <unk> brand and how you measure success.
Leo: At DXP kind of gave a keynote where based on the economic data that we pull from Fannie and several other.
Leo: Economists were more bullish based on where rates cuts have been from starting the year to ending the year.
Speaker Change: Yeah, absolutely. Thanks, Liana Thanks Denise.
Speaker Change: The good news is that we've completed the brand update that I talked about talked about we call that our allow up.
So we're bullish that in 2025 will have an increased transactional count over 23, and 24, which will probably end the year, either flat or lower than 23. So we're bullish on all that fun stuff.
Speaker Change: But our brand is a living breathing thing it will continue to evolve in order to meet market needs in order to meet our agents' needs. So we'll always be looking at how we can continue to evolve and modernize the brand.
Speaker Change: Back to you Wendy alright, thanks. Thanks.
Speaker Change: Wendy I'll, let you ask your question. Thank you for joining US today can you just give us the timeline and budget for resolving the ESP brand and how you measure success.
Speaker Change: The second part of your question Denise was how do we monitor success and what measures do we use.
Speaker Change: Yeah, absolutely. Thanks, Liana Thanks Denise.
Speaker Change: I think of marketing as having both elements of art and science. There is the art part of marketing that is very subjective, but what we really lean into is the science part of marketing and that's the numbers and looking at how are the marketing.
Speaker Change: The good news is that we've completed the brand update that I talked about talked about we call that our allow up.
Speaker Change: But our brand is a living breathing thing it will continue to evolve in order to meet market needs in order to meet our agents' needs. So well always be looking at how we can continue to evolve and modernize the brand.
Speaker Change: Things that we're doing.
Speaker Change: Performing and we look at things like share of voice, we look at all of the analytics that are available to us from social media to really monitor is kind of that science that science and that orange part together and so we always continue to do that and report those numbers.
Speaker Change: The second part of your question Denise was how do we monitor our success and what measures do we use in <unk>.
Speaker Change: Think of marketing as having both elements of art and science. There is the art part of marketing that is very subjective, but what we really lean into is the science part of marketing and that's the numbers and looking at how are the marketing.
Speaker Change: But as I said, the the beauty of ESP to point out is our branding evolution is ongoing and will continue to choose add and improve as we move forward.
Things that were performing.
Speaker Change: Great. Thank you I also want to remind everybody that you can ask a question on slide <unk> you can scan the.
Speaker Change: Performing and we look at things like share of voice, we look at all of the analytics that are available to us from social media to really monitor is kind of that science that science and that orange part together and so we always continue to do that and report those numbers.
Speaker Change: That's behind me and ask your question there or you can go to <unk> dot com and enter the code <unk> and ask a question there.
Speaker Change: I'll open the mic to Jonathan bass are covering analyst at Stephens, Jonathan if you'd like to ask a question you can go ahead.
Speaker Change: But as I said, the the beauty of ESP to point out is our branding evolution is ongoing and will continue to choose add and improve as we move forward.
Speaker Change: Yes.
Speaker Change: Perfect. Thank you yes. This is Jonathan on for John Thanks for taking my questions. So.
Speaker Change: In recent weeks and months.
Speaker Change: Great. Thank you I also want to remind everybody that you can ask a question on slide <unk> you can scan the Cowen that's behind me and ask your question there or you can go to <unk> dot com and enter the code <unk> and ask a question there.
Speaker Change: Clear cooperation policy has become a big talking point within the industry.
Speaker Change: You have some brokerages and industry participants have come out calling for the policies and.
Speaker Change: And then you have others, who have come out in definitive so I'm interested to get your debt to take on it.
Leo Pereira: Yes, Thanks, I will jump in here, it's Leo I'm actually probably one of the loudest executives on the defending side.
Speaker Change: I'll open the mic.
Speaker Change: Two Jonathan bass are covering analyst at Stephens, Jonathan if you'd like to ask a question you can go ahead.
Speaker Change: And this is extremely top of mind to Glenn and I as we are battling portals in other countries that can kind of union laterally change the pricing structure and really affect our bottom line.
Speaker Change: Perfect. Thank you yes. This is Jonathan on for John Thanks for taking my questions. So.
Speaker Change: In recent weeks and months.
Speaker Change: Clear cooperation policies become a big talking point within the industry.
Speaker Change: The U S system as it stands.
Speaker Change: The North American system, just Canada system works the same way is unique unto itself on.
Speaker Change: You have some brokerages and industry participants have come out calling for the policies and.
Speaker Change: And then you have others, who have come out in definitive so I'm interested to get your take on it.
On the global stage, we have the most complete the most liquid most accurate.
Speaker Change: Yes, Thanks, I will jump in here, it's Leo I'm actually probably one of the allowed us executives on the defending side.
System that exists and I think for US who practice real estate in this part of the World. We take it for granted sometimes like things like comps and if something is active it means active with definition of our bedroom and bath counts. So.
Speaker Change: And this is extremely top of mind to Glenn and I as we are battling portals in other countries that can kind of union laterally change the pricing structure and really affect our bottom line.
Speaker Change: We are extremely strong proponents it is the best thing for the consumer specifically because it has a total.
Speaker Change: The U S system as it stands.
Speaker Change: Marketplace with accurate data and it also keeps the price of marketing and a very reasonable price.
Speaker Change: Im sorry, the North American system, just Canada system works the same way.
Speaker Change: Is unique unto itself.
Speaker Change: Because youre not having to pay per impression and per month, and how we get build in other countries. So I am a very loud proponent of it wrote an op Ed piece, probably in the last 30 days about it and I'm wheels up to Boston.
Speaker Change: On the global stage.
Speaker Change: We have the most complete the most liquid most accurate.
Speaker Change: <unk> system that exists and I think for US who practice real estate in this part of the world. We take it for granted sometimes like things like comps and if something is active it means active with definition of our bedroom and Bath counts.
Speaker Change: Tomorrow, the following day to speak at our conference to a couple of thousand agents about my opinions on the subject. So I think it's very important that we defend and maintain the way we do business in North America.
Speaker Change: No.
Speaker Change: We are extremely strong proponents it is the best thing for the consumer specifically because it has a total.
Speaker Change: Alright, Thank you Leo.
Speaker Change: Marketplace with accurate data and it also keeps the price of marketing and a very reasonable price.
Speaker Change: Alright, we have another question from Tom why our covering analysts at D. A davidson who couldn't be with us on the stage today.
Speaker Change: Because youre not having to pay per impression and per month, and how we get build in other countries. So I am a very loud proponent of it a rope wrote an op Ed piece, probably in the last 30 days about it and I'm wheels up to Boston.
Speaker Change: But he wanted to to ask if you could Glenn maybe give us a general update on international what are the agent trends and what do you expect for 2025.
Speaker Change: Yes, so one.
Tomorrow, the following day to speak at our conference to a couple of thousand agents about my opinions on the subject. So I think it's very important that we defend and maintain the way we do business in North America.
Glenn: What youll see obviously is.
Glenn: The revenue was.
Dramatically up year over year internationally, even though our agent count is fairly flat.
Speaker Change: Alright, Thank you Leo.
Glenn: On an international basis, and the reason the reason being is that.
Speaker Change: Alright, we have another question from Tom White, our covering analysts at D. A davidson who couldn't be with us on the stage today.
Glenn: During our initial years.
Glenn: Under previous leadership it was really focused on agent count and we're now we've changed our approach to productive.
Speaker Change: But he wanted to to ask if you could Glenn maybe give us a general update on international what are the agent trends and what do you expect for 2025.
Glenn: Agent Count so we internally are now tracking.
Speaker Change: Yeah. So one.
Glenn: Our productive agents.
Speaker Change: What youll see obviously is.
Glenn: It might have even mentioned even on the last call that we are.
Speaker Change: The.
Speaker Change: The revenue was.
Glenn: We're working on.
Speaker Change: Dramatically up year over year internationally, even though our agent count is fairly flat.
Glenn: Eventually, creating some more transparent metrics around that specific one because it's an important one for from our perspective.
Speaker Change: On an international basis, and the reason the reason being is that <unk>.
But what we do know is like one of the things that we're doing is in order to be in Asia ESP internationally, you need to have.
Speaker Change: During our initial years.
Speaker Change: Under previous leadership it was really focused on agent count and we're now we've changed our approach to productive.
Glenn: At least really it's at least one year, we say two years of experience in the industry.
Speaker Change: Agent Count so we internally are now tracking.
Glenn: Partially because we're new into the market, but it also gives us a great reputation when we open up in a market that we're not just attracting brand new to the industry people.
Speaker Change: Our productive agents.
Speaker Change: I think I might have even mentioned even on the last call that we are.
Speaker Change: We're working on.
Speaker Change: Eventually, creating some more transparent metrics around that specific one because it's an important one from our perspective.
Glenn: As.
Glenn: Can be a little bit damaging to our reputation.
Glenn: If you if you have new agents.
Speaker Change: But what we do know is like one of the things that we're doing is in order to be in Asia ESP internationally, you need to have.
Glenn: Don't know what Theyre doing and given that internationally. There is very few rules and a lot of countries on how to actually operate.
Speaker Change: At least really it's at least one year, we say two years of experience in the.
Glenn: And for Us.
Glenn: Attracting a professional agent is an important piece to the equation.
Speaker Change: Industry.
Speaker Change: Partially because we're new into the market, but it also gives us a great reputation when we open up in a market that we're not sure.
I think what youre going to see is youre going to see us announcing more countries.
Glenn: Youre going to see.
Speaker Change: Just attracting brand new to the industry people.
Glenn: More investment in international expansion.
Speaker Change: Which is.
<unk>.
Speaker Change: Can be a little bit damaging to our reputation.
Glenn: In reality will translate to higher expense, even though internally we've got a number of countries that are now profitable in.
Speaker Change: If you if you have two agents that.
Speaker Change: I don't know, what they're doing and given the internationally. There is very few rules and a lot of countries on how to actually operate.
Glenn: Inside of international So we know that even though we're opening up countries. These from an IRR perspective are actually good.
Speaker Change: For us.
Speaker Change: Attracting a professional agent is an important piece to the equation.
Glenn: Good investments as we continue to kind of move forward we're rebuilding.
Speaker Change: I think what youre going to see is youre going to see us announcing more countries.
Glenn: <unk>.
Glenn: Last night, we are watching us very much in beta, but we launched our new <unk> International website, we're starting to build out our funnels, we're starting to drive traffic into our new DXP International.
Speaker Change: Youre going to see.
Speaker Change: More investment in international expansion, which in.
In reality will translate to higher expense, even though internally we've got a number of countries that are now profitable inside of international. So we know that even though we're opening up countries. These it from an IRR perspective are actually.
Glenn: Headquarters, which I talked about earlier on the call.
Glenn: Where you can come in and visit but Youll see a link right on our expedite international site, where you can click on it and come in in <unk>.
Glenn: Alert about help us find more agents help us open up new markets.
Speaker Change: Good investments as we continue to kind of move forward we're rebuilding.
And that's being staffed really 24 by five.
Speaker Change: Fact.
Speaker Change: Last night, we are watching it very much in beta, but we launched our new <unk> International website, we're starting to build out our funnels, we're starting to drive traffic into our new DXP International.
Glenn: Eventually will be 24 by seven so excited about how that's kind of.
Glenn: It's coming together.
Glenn: We just launched just a couple.
Glenn: A couple of months ago, we announced that Regis has now an available opportunity for all agents in all markets in the last week of DXP Con we announced.
Speaker Change: Headquarters, which I talked about earlier in the call.
Speaker Change: Where you can come in and visit but Youll see a link right on our XP Dod International site, where you can click on it will come in and.
Glenn: Camber is now available to all agents in all markets that DXP operation I think technically it's being rolled out here in the <unk>.
Speaker Change: Learn about help us find more agents help us open up new new markets.
Glenn: <unk>.
Glenn: But we keep on working on the agent value proposition so it becomes.
Speaker Change: And that's being staffed really 24 by five.
Glenn: Eventually it's going to become a natural that you're going to want to XP, because we rollout.
Speaker Change: Eventually it will be 24 by seven so excited about how that's kind of.
Speaker Change: It's coming together.
Speaker Change: We just launched just a little.
Glenn: So many benefits to agents, we're investing heavily in our relationship with home Hunter Dock global So we're going to see a lot of investment.
Speaker Change: A couple of months ago, we announced that Regis has now an available opportunity for all agents in all markets in the last week of DXP Con we announced.
Glenn: Going in and breaking what I consider to be the stranglehold on the industry.
Speaker Change: Camber is now available to all agents in all markets that DXP operates and I think technically it's being rolled out here in the <unk>.
Glenn: We think theres a lot of brokerages that will that are struggling just to stay in business because of the portal costs and so we.
Speaker Change: <unk>.
Glenn: We will be re directing some of the revenues from North America, some of the profits into helping us really established.
Speaker Change: But we keep on working on the agent value proposition so it becomes.
Speaker Change: Eventually it's going to become a natural that you're going to want to XP, because we rollout.
Glenn: Chad into various countries and as those get established we will work on building more beachheads from a portal perspective, so a lot of lot of things that we're working on.
Speaker Change: So many benefits to agents, we're investing heavily in our relationship with home Hunter Dock global So we're going to see a lot of investment.
Glenn: On the international front.
<unk> in and breaking what I consider to be the stranglehold on the industry.
Glenn: Great and Tom had an additional question you wanted to ask with the two initiatives announced the ESP com the icon incentives and the revenue share Catherine can you give some color as to how you expect those to impact gross margins going forward.
Speaker Change: We think theres a lot of brokerages that will that are struggling just to stay in business because of the portal costs and so.
Speaker Change: We will be re directing some of the revenues from North America. Some of the profits into helping us really establish beachhead into various countries and as those get established we will work on building more beachheads from a portal perspective, so a lot of lot of things that we're working on.
Speaker Change: So generally speaking they shouldn't affect gross margins going forward.
Speaker Change: We've always been a company that has really focused on being the most agent centric real estate brokerage on the planet.
Speaker Change: And when we say agents, we're talking about the agents that are out there listening and selling real estate.
Speaker Change: And so this will make it more sticky.
Speaker Change: On the international front.
Speaker Change: Great and Tom had an additional question you wanted to ask with the two initiatives announced the ESP com the icon incentive and the revenue share Catherine can you give some color as to how you expect those to impact gross margins going forward.
Speaker Change: And so on and so forth.
Speaker Change: We.
Speaker Change: These programs are.
Speaker Change: Specifically geared and Theres some epic use out there, but there are specifically geared to agents who are actually writing production.
Speaker Change: Yes, so generally speaking they shouldn't affect gross margins going forward.
Speaker Change: And are actually running actual real estate teams.
Speaker Change: We've always been a company that has really focused on being the most agent centric real estate brokerage on the planet.
Speaker Change: And so those are the ones that it's geared toward and then.
Speaker Change: Our revenue share.
Speaker Change: And when we say agents were talking about the agents that are out there listening and selling real estate.
Speaker Change: Platform pays out 50% of company dollar, regardless domestically or 10% internationally and Tom agent caps.
Speaker Change: And so this will make it more sticky.
Speaker Change: And so on so forth.
Speaker Change: And when I say, 10%, 10% of the gross commission income internationally.
We.
Speaker Change: These programs are.
Speaker Change: It's 50% or 10% of the gross commission income.
Speaker Change: Specifically geared and Theres some epic use out there, but they are specifically geared to agents who are actually writing production.
Speaker Change: <unk> caps domestically in the entire GCI.
Speaker Change: And.
Speaker Change: And are actually running actual real estate teams.
Speaker Change: So it won't it won't actually.
Speaker Change: And so those are the ones that it's geared towards and then.
Impact what we payout so gross margins won't change, but we do know what's going to impact.
Speaker Change: Our revenue share.
Speaker Change: Platform pays out 50% of company dollar, regardless domestically or 10% internationally and home agent caps.
Agents, we've already I've already got lots of feedback from productive agents, who are really happy about this we also announced something at <unk>, which was the international sponsorship program I talked about that earlier, but that's I think going to be something we're going to look at and how we can use that even more domestically too.
Speaker Change: And when I say, 10%, 10% of the gross commission income internationally.
Speaker Change: It's 50% or 10% of the gross commission income.
Speaker Change: Until an age of caps domestically in the entire GCI.
Speaker Change: To create more more growth as well.
Speaker Change: And.
Speaker Change: Great. We have a couple of questions on slide for either you or Leo.
Speaker Change: So it won't it won't actually.
Speaker Change: Impact what we payout so gross margins won't change, but we do know what's going to impact.
Speaker Change: As agents our business planning for 2025, how do you see the housing market in the U S changing and what should agents take into account as they look at 2025.
Speaker Change: Agents, we've already I've already got lots of feedback from productive agents, who are really happy about this we also announced something at <unk>, which was the international sponsorship program I talked about that earlier, but that's I think going to be something we're going to look at and how we can use that even more domestically too.
Speaker Change: I'll jump into that one that's one have been answered.
Speaker Change: Great go ahead can you hear me.
Okay.
That's the one I've been answering for the media quite a bit.
Speaker Change: Create more more growth as well.
Speaker Change: Look at where rates started in Jan one to today.
Speaker Change: Great. We have a couple of questions on slide <unk> for either you or Leo.
Speaker Change: Come down enough, where if and again, we just had a fed cut a couple of hours ago. So.
Speaker Change: And as agents our business planning for 2025, how do you see the housing market in the U S changing and what should agents take into account as they look at 2025 I'll jump into that one that's one I've been answering.
Speaker Change: Right hold and there is no increases.
The typical spread between the 10 year Treasury and mortgage rates is somewhere between 150 to 200 basis points and they've been trending to 250 to 300 basis points. So we have some hope that rates can continue to normalize now that we have the election behind us.
Speaker Change: Great go ahead can you hear me.
So the earliest predictions I've heard it or have made based on the economists suggestions as that we could see about a 10% bump in total transaction count. So if we end the year somewhere between $3 eight.
Speaker Change: Okay, Yes.
Speaker Change: That's what I've been answering for the media quite a bit.
We look at where rates started in Jan one to today.
Speaker Change: They've come down enough, where if and again, we just had a fed cut a couple of hours ago, So assuming rates hold and there is no increases.
Resale $3 8 million Resales were 700000, new construction, we could hopefully see somewhere in that four.
Speaker Change: The typical <unk>.
Speaker Change: $4 5 million transactional range for next year, and if we play a finite game of transactions and with continued market share that could be greater.
Speaker Change: Fred between the 10 year Treasury and mortgage rates is somewhere between 150 to 200 basis points and they've been trending to 250 to 300 basis points. So we have some hope that rates can continue to normalize now that we have the election behind us.
Speaker Change: Great across the platform.
Speaker Change: So what I'm, saying to agents quite a bit is actually to continue to focus on the basics as the rates come down it brings people back into the market that just couldnt get there. So there is a portion of consumers that.
Speaker Change: So the earliest predictions I've heard it or have made based on the economists suggestions as that we could see about a 10% bump in total transaction count. So if we end the year somewhere between $3 eight.
We're always making the choice between continuing to rent oriented.
Speaker Change: Resale $3 8 million Resales were 700000, new construction, we could hopefully see somewhere in that four four.
The property ladder versus there are sellers, who are still rate locked.
$4 5 million transactional arrangement next year, and if we play a finite game of transactions and with continued market share that could be.
Speaker Change: Think a stat I shared of DXP gone is something.
Speaker Change: As high as 84% of all rates on mortgages in this country are probably below 555%. So there is that rate lock phenomenon that we've been experiencing that exacerbates the market, but the lower the rates come down.
Speaker Change: Great across the platform.
Speaker Change: So what I'm, saying to agents quite a bit is actually to continue to focus on the basics as the rates come down it brings people back into the market that just couldnt get there. So there is a portion of consumers that.
Speaker Change: The more it'll bring buyers back into the market and hopefully kind of defrost synthol some of the sellers at a rate locked, but we are bullish going into 2025 versus the feeling we had this time last year from 'twenty three to 'twenty four.
Speaker Change: We're always making the choice between continuing to rent oriented.
Speaker Change: The property ladder versus there are sellers, who are still rate locked.
Speaker Change: Alright, Thanks, Leo and one clarifying question. This is our last question on slide out does the agent count included the 2900 <unk> from the acquisition in Q2.
Speaker Change: Think a stat I shared a dx peak on us.
Speaker Change: As high as 84% of all rates on mortgages in this country are probably below 555%. So there is that rate lock phenomenon that we've been experiencing that exacerbates the market, but the lower the rates come down.
Speaker Change: Yes. It was included.
Speaker Change: Great. Okay, Alright, well this concludes our earnings call for the third quarter. Thank you everyone for joining.
Speaker Change: The more it'll bring buyers back into the market and hopefully kind of defrost and saw some of the sellers that are rate locked, but we are bullish going into 2025 versus the feeling we had this time last year from 'twenty three to 'twenty four.
Speaker Change: As always please stay connected by visiting ESP World Holdings for the latest updates on <unk> results and events. Additionally, you will find a recording of this call and our latest investor presentation on the investors section of the site. This concludes the <unk> World Holdings' third quarter 2024 earnings Fireside chat.
Speaker Change: Alright, Thanks, Leo and one clarifying question. This is our last question on slide out does the agent count included the 2900 <unk> from the acquisition in Q2.
Speaker Change: Yes. It was included.
Speaker Change: Hello.
Speaker Change: Okay, Alright, well this concludes our earnings call for the third quarter. Thank you everyone for joining.
Speaker Change: [music]. So welcome I'm very excited today to talk about effective speaking in spontaneous situations I. Thank you all for joining us even though the title of my talk is grammatically incorrect I thought that might scare a few of your way, but I learned teaching here at the business school catching people's attention.
Speaker Change: As always please stay connected by visiting ESP World Holdings for the latest updates on <unk> results and events. Additionally, you will find a recording of this call and our latest investor presentation on the investors section of the site. This concludes the <unk> World Holdings' third quarter 2024 earnings Fireside chat.
As hard so something as simple as that I thought might draw a few of you here. So this is going to be a highly interactive and participative workshop today. If you don't feel comfortable participating that's completely fine, but do you know I'm going to ask you to talk to people next you there'll be opportunities to standup and practice some things because I believe that.
Speaker Change: Thank you.
Speaker Change: Way, we become effective communicators as by actually communicating so let's get started right away I would like to ask you all to read this sentence and as you read this sentence. What's most important to me is that you count the number of apps that you find in this sentence. Please count the number of apps keep it quiet to yourself.
Speaker Change: Hi, everyone I welcome you all to the life session on data analytics political buy into any bad.
Speaker Change: This session is conducted by multiple exports will be talking to you about data analytics, some basics so Atlanta seven.
Speaker Change: But before we begin the session mission to him this upside but it also hit on the <unk>. So that you will never Miss an update somewhat.
Give you just another couple of seconds here.
321 raise your hand, please if you've found three and only three yes.
Speaker Change: Now, let's see that agenda. Firstly, we will begin with introduction good data analysis in that we would be.
Excellent great did anybody find for anybody.
Speaker Change: Then you Watson data analysis, why do the QUADRA and types of data analysis. Once you finish that we will extend you about the lifecycle of data analysis.
Anybody find only five fs and anybody find six.
Speaker Change: Their success.
What two letter word ending in EF did many of us mess.
Speaker Change: Moving to the new how do become data analyst what are the skills required job and good.
Speaker Change: Oh.
Speaker Change: We will make sure to get this to you. So you can terminate your friends and family at a later date.
Prospects all data analyst or does that go and learn about bond us and data analysis using part of that and later on live and learn about exploratory data analysis and also lumpy and later on we'll be doing a very quick hands on demo on how to create and buy at it.
When I first was exposed to this over 12 years ago I only found three and I felt really stupid. So I'd like to start every workshop every class I teach with this the past that feeling and whatnot.
That's not why I do this.
Speaker Change: After that we'll be seeing the difference between a data analysts and data scientists and finally will be company data analysts in W questions and answers.
Speaker Change: I do this because this is a perfect analogy for what we're going to be talking about today. The vast majority of us in this room very smart people in this room were not as effective as we could have been in this activity we didn't get it right.
Speaker Change: So this is the agenda that we do without any further delay let's get started we want her to the storm some that pretty much. It rings a bell that every time, we think about it right. So analytics with respect to data well what are we trying to analyze what we need to know here. So the formal definition of our <unk> basically.
Speaker Change: And the same is true when it comes to speaking in public, particularly when spontaneous speaking, it's little things that make a big difference and being effective. So today, we're going to talk about little things in terms of your approach. Your attitude. Your practice that can change how you feel when you speak in public.
Speaker Change: Pressing a meaningful information from raw data, it's as simple as that so we have data so consider anything davita if that does not useful to you at that point. It is considered a data, but then if you perform some operation on this data and make sure that it is useful for your organization. This is when the data will come to infer.
Speaker Change: And we're gonna be talking primarily about one type of public speaking.
Speaker Change: Not the type that you planned for in advance the type that you actually spend time thinking about you might even create slides for these are the keynotes the conference presentation the formal toasts.
Speaker Change: Omission and this information is what is usable for you right. So the pluses are pretty much gone worthy raw data into information.
Speaker Change: That's not what we're talking about today, we're talking about spontaneous speaking.
Speaker Change: When youre in a situation that you're asked to speak off the cuff and in the moment.
Speaker Change: Further analysis on it and we don't does data analytics guys. I mean this is just a lady luck definition of what goes in the industry to give you a better insight of what that means basically it is the pursuit of extracting meaningful minority of die using specialized computer systems guys and then these systems to the platform the organized and the model of the Ddos.
Speaker Change: What we're going through today is actually the result of a workshop I created here for the business School.
Speaker Change: Several years ago, a survey was taken among the students and they said what's one of the what are the things. We can do to help make you more successful here and at the top of that list was this notion of responding to cold calls as everybody know what a cold call is toward the mean professor like me looks at some students is what do you think.
Speaker Change: We have witnessed to basically draw the conclusions from all of the data. So you will have again as I've mentioned, we did all example, olive.
Speaker Change: Cologuard is to basically draw conclusions to identify <unk> to make use of this information in a better way I mean shut data can just be used in its raw form, but then not with every case right and to give you. An example, so think of the data will be you understand so let's say you are looking at a huge dataset, which contains thousands of values those of this <unk>.
Speaker Change: And there was a lot of panic and a lot of silence.
Speaker Change: So as a result of that this workshop was created in a vast majority of first year students here at the GSV go through this workshop, so I'm going to walk you through sort of a hybrid version of what they do.
Speaker Change: The reality is that spontaneous speaking is actually more prevalent than planned speaking, perhaps it's giving introductions you were at a dinner and somebody says you know so and so would you mind introducing them.
Speaker Change: Number Tonight, so having these numbers show it as a data analyst you will understand what are you doing lebon listen you need to expand into a person doesn't know or data analytics is all you need to explain to someone <unk>.
Speaker Change: Maybe it's giving feedback in the moment your boss turns to you and says would you tell me what you think.
Speaker Change: It could be a surprise toast or finally, it could be during the Q&A session and by the way we will leave plenty of time at the end of our day today for Q&A I'd love to hear the questions you have about this topic or other topics related to communicating.
Speaker Change: At a somewhat above you of the lithium business meeting with <unk>, giving the numbers will not really be that great lawmaker presentation. So converting all of these numerical leader, making it into graphs, making it in a way that everyone understands the data and it's showing on graphically automate or insights that you can generate from the data that can be canceled.
Speaker Change: So our agenda is simple in order to be an effective communicator, regardless of if its planned or spontaneous you need to have your anxiety under control.
Speaker Change: Third is data analytics is early days again today and basically the feel of it all it does is ever still growing rapidly and why even growing so rapidly well because the market demand, Florida does that much. So we have obviously much every startup company. These days have a requirement for big data as well so big data too.
Speaker Change: I will start there.
Speaker Change: Second what we're going to talk about is some ground rules for the interactivity will have today and then finally, we're going to get into the heart of what we will be covering and again as I said lots of activity and I invite you to participate.
Speaker Change: Dumb down the definition that pretty much means a huge amount of data that cannot be handled by just one machine if you're stretching audio data goes might've been knowledge of data coming in from various sources.
So, let's get started with anxiety management.
85% of people tell us that they're nervous when speaking in public and I think the other 15% are line hey, we could create a situation where we could make them nervous too in fact, just this past week.
Speaker Change: Then you need a method to handle all of this big data that's coming in to process. The data and then to perform analysis on it.
Speaker Change: Ah study from Chapman University asked Americans, what are the things you fear, most and among being caught in a surprise terrorist attack having identity your identity stolen what's public speaking among the top five was speaking in front of others.
Speaker Change: So this has been in demand in the market for a while and for the last couple of years data analytics has been in the boom and then its providing a lady stray adult onto the all of the questions being raised about how all of these can be handled days and of course, they didn't need the people who have the skills, which is needed and it's because we needed to mine.
Speaker Change: This is a ubiquitous fear and one that I believe we can learn to manage and I use that word manage very carefully.
Speaker Change: He played all the <unk>.
Speaker Change: Cleaned all the numbers and do Grafman to Wink insightful analysis on it right. So that's what is the main key role of data analytic version or what are the need for data analytics. If you have to break it down into three steps guys. So again, we already know that it has on the raise rates. So I can pretty much global to.
Speaker Change: Because I don't think we ever want to overcome it anxiety actually helps us. It gives us energy helps us focus tells us what we're doing is important but we want to learn to manage it so.
Speaker Change: So I'd like to introduce you to a few techniques that can work in all of these techniques are based on academic research.
But before we get there I'd love to ask you.
Speaker Change: Ill step out into the open and then it does not soon that it'll be an integral part of an organization. It is all to be an integral part of every organization that is gas.
Speaker Change: What does it feel like when you're sitting in the audience watching a nervous speaker present, how do you feel just shout out a few things how do you feel.
Speaker Change: Uncomfortable I heard many of you going yes uncomfortable it feels very awkward doesn't it so what do we do now a couple of you'll probably like watching somebody suffer okay.
Speaker Change: And it wasn't bonding need as again, we already know that it is adult priority for all of these organizations ranging from the smaller than my visions all the way to the big guys. And then this is needed to make weighty good decision, making live with respect to you being confused to let's say take a decision for you then looking at this analytics will make your hard disk.
Speaker Change: But most of US don't so what do we do we.
We sit there and we're not and we smile or we disengage.
Speaker Change: And so the nervous speaker looking out at his or her audience seeing a bunch of people nodding her disengaged that does not help okay.
Speaker Change: Isn't making skills and decision, making skills, a little weak easier and it'll be more valuable as well, Dave and it'll be more validated is mitigated.
So we need to learn to manage our anxiety because fundamentally your job as a communicated rather regardless of if its planned or spontaneous is to make your audience comfortable because if they are comfortable they can receive your message and when I say comfortable I am not referring to the fact that that your message has to be some sugar coated and nice for them to hear it can be a <unk>.
Speaker Change: Second thing is do we require that all new revenue right. So, let's say you have the data, which pretty much only a couple of people can make sense of understand let's say your market is very niche and that gave us shortly will be still in making it ever used to be making money on it but then at the end of the day. If you have to reach out to a broader audience you need to make sure you are.
Speaker Change: <unk> message, but they have to be in a place where they can receive it. So it's incumbent on you as a communicator to help your audience feel comfortable and we do that by managing our anxiety. So let me introduce you to a few techniques that I think you can use right away to help you feel more comfortable.
Speaker Change: <unk> D. That's understood by this broad audience sites that forms the second.
Speaker Change: So very important need for data analytics. It is and the third one is to obviously the could you give me the operation cost for every organization. So this can be a very demanding task. If you have to convert raw data into information process, the data and make a very good with realizations out of it is that but then if you have any workforce wanted then.
Speaker Change: The first has to do with when you begin to feel those anxiety symptoms for most people. This happens within the initial minutes prior to speaking.
Speaker Change: In this situation what happens as many of US begin to feel whatever it is that happens to you maybe your stomach gets a little gurgle Lee maybe your legs begin to shake maybe you began to Perspire and then we start to say to myself Oh, my goodness I'm nervous.
Speaker Change: Show you endured, but if not again since it's probably a manual task on until a couple of years ago. It surely was a minor doesn't these days you can implement machine learning and data analytics to pretty much automated self and make your job easier of those events.
Speaker Change: Oh, they're gonna tell I'm nervous this is not going to go well and we start spiraling out of control.
Speaker Change: It speeds up your award it GPU Lady efficient in such a way where your data is being visualized process very effectively so again famous money when it comes to big organizations right. So on that note. When you launch it helps degrees all of the operations costs with respect to either the reading and the data pipeline are all waiting to process.
Speaker Change: So research ion mindful attention tells us that if when we begin to feel those anxiety symptoms, we simply greet our anxiety and say Hey. This is me feeling nervous I'm about to do something of consequence.
Speaker Change: And simply by greeting your anxiety and acknowledging it that it's normal and natural heck, 85% of people tell us they have it.
Speaker Change: Waiting to publish waiting to do some analytics waiting for regulators and all of these lead.
Speaker Change: You actually can stem the tide of that anxiety spiraling out of control, it's not necessarily going to reduce the anxiety, but it will stop it from spinning up. So the next time you begin to feel those things anxiety signs.
Speaker Change: You need to consider them anymore, because they will pretty much be they've used to nowadays.
Speaker Change: On that note you might be wondering who did the analysts to St.
Speaker Change: So data analysts or the people who sit at the end of the chain I'll just explain the churn in the next slide.
Speaker Change: Take a deep breath and say this is me feeling anxious.
Speaker Change: I noticed a few of you taking some notes there's a handout that will come at the end that has everything that I'm supposed to say okay.
Speaker Change: These guys form of aged goods.
Speaker Change: Workforce for the company that they basically you know Danny what are the values by baking in all the data that data scientists might give it to them a data engineer might give it to them and then he uses of this person data analysts uses all of these to answer in a couple of questions and then coming to get all of the results back.
Speaker Change: Can't guarantee I'm going to say it but you'll have it there.
Speaker Change: In addition to this approach a technique that works very well and this is a technique that I help do some research on way back when I was in graduate school has to do with re framing how you see the speaking situations.
Speaker Change: Most of US when we are up presenting planned or spontaneous we feel that we have to do it right and we feel like we are performing how many of you have ever acted than singing or dancing I'm not going to ask for performances now. Okay. Many of you have we should note that we could do next year, maybe a talent show of the lumps it looks like we got the <unk>.
Speaker Change: And all of these the results that he just gives back is basically used to be very good.
Speaker Change: Business decision scale.
Speaker Change: Might include analytics of past trends it may be a prediction of what the future looks like and so much more and then the Goldman <unk> done by data analysts out of our data cleaning.
Speaker Change: Out there that's great.
Speaker Change: Performing in Alabama, and then creating Visualizations, we'll be leaning to come about it is basically simple. So again, neither is Ron for raw information right to make sense out of like to pick out information that is only required and to make sure that again efficiency of the game here, if you're pretty much breathing data that we pretty much will not recur.
Speaker Change: So when you perform you know that there's a right way and a wrong way to do it.
Speaker Change: If you don't hit your the right note or you're right line at the right time at the right place you've made a mistake it messes up the audience. It messes up the people on stage.
Speaker Change: But when you present, there is no right way theres, certainly better than worst ways, but there is no one right way. So we need to look at presenting as something other than performance and what I'd like to suggest is what we need to see this as a conversation.
Speaker Change: Quiet and then it's just a waste of time and resources, so cleaning the data and making sure that your data is just <unk> enough.
Speaker Change: For processing, that's that's a very important first step. So this is the spearhead offer data analyst slowly and then the second one is obviously as soon as your data is clean need to perform some very good analytics on it and create very good visualization scale. So these florida designation used please see on the screen right now is basically the designation as a data analyst.
Speaker Change: Right now I'm, having a conversation with 100 plus people.
Speaker Change: Rather than saying I'm performing for you.
Speaker Change: But it's not enough just to say this is a conversation I want to give you some concrete things you can do.
Speaker Change: First start with questions questions by their very nature are dialogic, they're two way what was one of the very first things I did here for you.
Speaker Change: It is also known as.
Speaker Change: So the further the business analyst so data and this is also a business analyst.
Speaker Change: He or she can be an operations analyst a business intelligence analysts and database analyst as well gas still coming to the dos that they put it in much the first important pathogen and he spoke about the scheming and organizing a draw on structured data right. So this farms. This is again a very overlooked concept.
Speaker Change: Did you count the number of <unk> and raise your hands I asked you a question that gets your audience involved it makes it feel to me is the presenter as if we are in conversation. So us questions. They can be rhetorical there can be polling perhaps I actually want to hear information from you.
Speaker Change: In fact, I use questions when I create an outline for my presentations, rather than writing bullet points I list questions that I'm going to answer and that puts me in that conversational mode. If you were to look at my notes for today's top you'll see it's just a series of questions right now I'm answering the question how do we manage our anxiety.
Speaker Change: Today's wood unless you get your hands dirty with the data itself.
Speaker Change: When you start to or to go about doing that you will realize that cleaning and organizing meet all data is extremely important because at the end of video data mind me unstructured data might be semi structured or it might be a stop to data. This will not matter. If the data you're processing is of no use of the NDA, so cleaning and organizing the raw data.
Speaker Change: Beyond questions, another very useful technique for making as conversational.
Speaker Change: <unk> is very buoyant and the second thing is the analysis of all of the hidden trends found in the data so making sense of something again, maybe predictions in the future are looking at past trends.
Speaker Change: Is to use conversational language.
Speaker Change: Many nervous speakers distance themselves physically if you've ever seen a nervous speaker present, he or she will say something like this welcome I am really excited to be here with you.
Speaker Change: We'll adjourn anything to sort of an information, which you cannot figure out upfront just by looking at the data. This is pretty much the umbrella under which the hidden trend function of our data analysts work. So you'll be looking at the data you would find something late interesting. So let's say you find oil blending your data.
Speaker Change: They pull as far away from you as possible because you threaten US speakers you make us nervous so we Wanna get away from you. We do the same thing linguistically we use language that distances ourselves, it's not unusual to hear a nervous speakers say something like one must consider the ramifications.
Speaker Change: You can access our news out of the market with respect to USA is in the next five year span years, and then you were not doing this but then your data was telling you. This so you making sense of the data in which this trend was found which was not upfront and this is again, a very important skill of data.
Speaker Change: Or today, we're going to cover step one step two step three that's very distancing language.
Speaker Change: To be more conversational use conversational language instead of one must consider say this is important to you we all need to be concerned with do you hear that inclusive conversational language has to do with the pronouns.
Speaker Change: And this does well and the third important thing thats pretty much the big picture view using descriptive statistics at the end of it sure we know what the Boston or the company at I mean, all of the companies going on now, but then if you need to just summarize all of this and the even add some predictions over the next 510 years getting this big picture view.
Speaker Change: Instead of step one step two step three first what we need to do is this the second thing you should consider is here.
Speaker Change: Use conversational language, so being conversational can also help you manage your anxiety.
Speaker Change: The third technique I'd like to share his research that I actually started when I was an undergraduate here I was very fortunate to study with Phil Zimbardo of the Stanford prison experiment Fame.
Speaker Change: What's happened, what's happening now and what will happen in the future and not just using a very simplistic mistakes by using descriptive statistics that we'd be picking up each aspect of what's going on and then perform analytics on it and find out what's going on if we are right. At this place. If you are wrong in this space. How can this be include how can this concert.
Speaker Change: Many people don't know that Zimmer actually was instrumental in starting one of the very first China's institutes in the world and especially in the country and I did some research with him that looked at how your orientation to time influences how you react.
Speaker Change: Let me take how can we reach the client better and so much more so getting this big picture view is again, a very important dusk do download. This goes about billing days and the fourth one is probably the most important thing our data analysts does is pretty much the creation of dashboards and visualizations guys. So as I already dorland introduction part of the deal that.
Speaker Change: And what we learned is if you can bring yourself into the present moment, rather than being worried about the future consequences you can actually be less nervous most of us. When we present are worried about the future consequences. My students are worried they're not going to get the right grade. Some of you were worried you might not get the funding you might not get the support you might not get the labs that you were.
Speaker Change: We are starting out with the law information again, a lot of numbers, but if you have to show. These numbers at a business meeting yeah, I mean number of odd number some people like them, but the majority of the people who they might not understand the numbers. So I'm, making these numbers of your input, creating very good realization, giving them oh user interface and giving yard.
Speaker Change: All of those are future states. So if we can bring ourselves into the present moment we're.
Speaker Change: We're not going to be as concerned about those future states and therefore will be less nervous.
There are lots of ways to become present oriented.
Speaker Change: And all customers your pillars European cereal business meeting members are giving them a very good user experience with all of this data again at the end of it will add up to very good business methodologies and then it will help you make better business seller driven decisions again and again the same goes for our presentation of results to your clients annual and Donuts as well guys.
Speaker Change: I know a professional speaker he's paid $10000 an hour to speak it's a good gig he.
Speaker Change: He gets very nervous he is up in front of crowds of thousands behind the stage. What he does is 100 pushups right before he comes out.
You can't be that physically active and not be in the present moment now I'm not recommending all of US go to that level of exertion, because he starts out of breath and sweaty.
Speaker Change: So coming to the channels, how the data moves around in a phone.
But a walk around the building before you speak that can do it there are other ways, if you've ever watch athletes perform and get ready to do their event they listen to music. They focus on a song or a playlist that helps get them in the moment.
Speaker Change: Three important things that I just want to walk you through days in a quick just as the.
Speaker Change: First person who is the spearhead of the data for an organization by Spirit had me. He's the first one could look at the data or you will be responsible for bringing in all of the data from various sources guests. So data coming in from the video sources say so think about all the data you can get from Tudor all performing any sentiment analysis think of all the data that can come from.
Speaker Change: You can do things as simple as counting backwards from 100 by tough numbers like 17.
Speaker Change: I'm going to pause because I know people in the room are trying yeah.
Speaker Change: It's hard after that third or fourth one I know.
All of the Big data sources, you can have data coming from all your radius notes from Hadoop. So much more you can have a data coming from your own network. We have from your network and so much more invasive the nida engineered as a spirit, who handles bringing in the data.
Speaker Change: My favorite way to get present oriented is to say tongue twisters.
Speaker Change: Saying, a tongue twister forces you to be in the moment, otherwise, you'll say it wrong and it has the added benefit of warming up your voice most nervous speakers don't warm up the voice the retreat inside themselves and start saying all these bad things to themselves. So saying a tongue twister can help you be both present oriented and warm up your voice.
Speaker Change: And making it understandable by the organization and then as soon as the data engineers finishes his part of the data the data moves onto the data scientist data scientist he's responsible for walking on all of these already dog converting it into wallet information, but then you're already doing this for the data analyst has been whether the data scientists pretty much uses machine.
Speaker Change: Remember I said today, we're going to have a lot of participation I'm going to ask you to repeat after me my favorite tongue Twister and I liked this tongue twister, because if you say it wrong, you say, a naughty word and I'm going to be listening to see if I hear any naughty words. This morning, okay.
Speaker Change: Learning algorithm to use deep learning he uses let's say might be classifications naive buyers. He makes us. So many concepts Europe that are data analysts might not know Andy uses a lot of machine learning and deep learning as I've already mentioned to convert this rolling it out into what an information and 99% of the time, the information which is being done.
Speaker Change: Repeat after me, it's only three phrases I slit a sheet.
Speaker Change: A sheet isolate.
Speaker Change: And on that slipped into the sheet I sit.
Speaker Change: Oh very good no shifts excellent.
Water into our numbers. So after our data scientists pretty much goes about doing his magic onto the data and then we have the data analyst who steps in this person again as we've already been mentioning he is responsible for the prediction of what happens in the future that are finding our trends and in the presentation of all these all information to you or not.
Speaker Change: Very good.
Speaker Change: In that moment in that moment.
Speaker Change: Aren't worried about I'm in front of all of these people. This is weird. This guy is having to do it you were so focused on saying it right and trying to figure out what the Naughty word was that you were in the present moment, that's how easy it is.
Speaker Change: So it's very possible for us to manage our anxiety.
Speaker Change: PR steel blind.
Speaker Change: Superior and so much more just the basic gist of how data moves around until the data is foreseen by the data engineered he does his job comes to the data scientists the data scientists walks his magic. We all all these fancy algorithms and whatnot and then pushing the data the data analyst data analysts perform spread agency spends visualizes the data makes it presentable for MTI.
Speaker Change: We can do it initially by greeting the anxiety when we begin to feel that assigns.
Speaker Change: We can do it when we reframed the situation is a conversation and we do it when we become present oriented.
Speaker Change: Those are three of many tools that exists to help you manage your anxiety.
Speaker Change: If you have questions about other ways I'm happy to chat with you and at the end I'm going to point you to some resources that you can refer to to help you find additional sources for you.
Speaker Change: And to understand and then eventually goes about analyzing the data. So on that note. We can come check out type of floor data analytics that can be done into this business case.
Speaker Change: So let's get started on the core part of what we're doing today, which is how does it feel more comfortable speaking in spontaneous situations. Some very simple ground rules for you.
Speaker Change: Couple of types of data analytics and I'll be walking you through the same for the first type of data analytics you can go about doing it the descriptive analytics.
Speaker Change: With respect to the descriptive analytics again, the quick all won a straight answer toward descriptive analytics is is basically you know picking up data pharma source summarizing your day.
Speaker Change: First I'm going to identify four steps that I believe are critical to becoming effective it's speaking in a spontaneous situations with each of those steps I'm going to ask you to participate in an activity none of them are more painful than saying the tongue twister out loud. They may require you to stand up they might require you to talk to the person next to you, but none of them.
Speaker Change: I can show you the data can be understood by everyone that is picking out really good insights from their data from all the past events doing some predictive.
Speaker Change: Alex on that as well and then keeping it Lady So your data is descriptive at the end of the day and a question, which goes by to understand the descriptive analytics as a what happened in my business. So the answer to this question what happened in my business is given Dubai, the descriptive analytics space. So most of the time.
Speaker Change: Painful.
Speaker Change: And then finally I'm going to conclude with a phrase or saying that comes from the wonderful world of improvisation.
Speaker Change: Through the continuing studies program here at Stanford for the past five years I have co taught a class with Adam Tobin. He is a lecturer in the.
Speaker Change: Creative Arts Department, he teaches film and new media and he's an expert at improv and we've partnered together to help people learn how to speak more spontaneously we call. It Improvisationally speaking and Adam has taught me wonderful phrases and ideas from improv that I want to impart to you they're really stick that's why I'm sharing them with you to help you remember these.
Speaker Change: The data, which is generated by descriptive analytics is very comprehensive it is extremely accurate visualizations away effective as well. So he was a very simple scenario that you are asking constant so considering the scenario. All you know about and learning website decides to focus on our pending of course, according to the analytics of the search volume of the course content.
Speaker Change: Techniques and again at the end of all this you'll get a handout that has this listing.
Speaker Change: Pretty much the users gone sourcing and all of the revenue generated by the course in the past few months as well. So there are certain technologies again, we live in a world full of cranes riser, let's say, our data sciences and the boom right now so pretty much any learning company or any company for that matter, they're going to find these plants, they're going to ignore that paid data.
Speaker Change: So let's get started.
Speaker Change: The very first thing that gets in People's way when it comes to spontaneous speaking.
Is themselves.
Speaker Change: We get in our own way.
We want to be perfect. We want to give the right answer we want our toasts to be incredibly memorable.
<unk> is the thing in the top tier and we need to do something about it if they have students in data science and most of these companies are aimed at making the the student's life better right. So even as a firm here at I'm in daily bought that's exactly what we do is when we did this amazing insights from all of our learners for any vertical of course, and we use all of those.
Speaker Change: These things are burdened by our effort by are trying the best thing we can do the first step in our process is to get ourselves out of the way.
Speaker Change: Easier said than done.
Speaker Change: Most of US in this room are in this room, because we our type a personalities. We work hard we think fast we make sure that we get things right, but that can actually serve as a dis service as we try to speak in the moment.
Speaker Change: Insights all of the feedback that we get and what spending in the market. What's the latest in the market and we used all of these to perform analytics and then pretty much all of this helps us to put out a better courses when guys. So at the end of it you can already see how are the descriptive analysis is already helping out of business right and then the mix type of data and analytics.
Speaker Change: I'd like to demonstrate a little of this for you and I need your help to do that so we're going to do our first activity.
Speaker Change: We're going to do an activity that's called shout the wrong name.
Speaker Change: We can take out of the diagnostic analytics space. So we already checked out of what is happening to my business with respect of descriptive analytics with respect to diagnostic analytics, you will take out of Hawaii. This particular thing is happening to my business case to simplify it again, it's basically gives you the ability to dig down to the root cause of Hawaii something has been.
Speaker Change: In a moment, if you are able and willing I'm going to ask you to stand and I'm going to ask you for about 30 seconds to look all around you in this environment and you are going to point at different things and I know its route to point, but for this exercise. Please point I want you to point to things and you were going to call. The things you are pointing to out loud anything but.
Speaker Change: Having just like how what should gates, so drilling down the data do identify certain set of problems, which are present in your data on the trains that the data shows is already likely part of diagnostic analytics guys and then it basically helps in answering the question for US about AEP issue is a good you know so it just takes one look towards the data out.
Speaker Change: What they really are.
Speaker Change: So I might point to this and say refrigerator.
Speaker Change: I might point to this and say cat.
Speaker Change: I am pointing to anything in your environment around you can be the person sitting next to you standing next to you, we'll just shout and shouting is important the wrong name.
Speaker Change: What's the analytic result of the data to understand what the cause behind a problem. If one should exist as days. So again to give you a very simple scenario of how diagnostic analytics would work, let's say counter a company where the fares went down for a month, so let's say they weren't doing as good as the previous months so to diagnose this particular problem.
Speaker Change: So in a moment I'm going to ask you to stand and do that.
Speaker Change: Please raise your hand, if you already have the first five or six things youre going to call out.
Speaker Change: Yeah.
Speaker Change: That's what I'm talking about.
Speaker Change: We stockpile you all are excellent game players I told you the game shelf the wrong name and you have already begun figuring out how youre going to master the game there.
Speaker Change: You will listen to consider these attrition by the number of employees with good thing that a job and they weren't bringing in a lot of sense. So this could pretty much show. This number of people quitting the company could directly impact the sales that they brought into the company. So the sales have been down this month, because they did not have the company right.
Speaker Change: That's your brain trying to help you get it right.
Speaker Change: I'd like to suggest the only way you can get this activity wrong.
Speaker Change: Is by doing what you've just done.
Speaker Change: There is no way to get this wrong.
It does not have a sales rate so finding out why this is happening and are hoping.
Speaker Change: Okay, even if I call this a chair.
Speaker Change: Thank for that root cause of it is basically inspiration and with your data that you're gone fine being humbling and speaking insights from relate them finding other Hawaii part of it is extremely importantly, you'd be finding on what's happening.
Speaker Change: No penalty will be bestowed upon you.
Speaker Change: Okay cause.
Speaker Change: I don't know what you were pointing at you could've been pointing at the floor under the chair and you called the Florida Chair and you were fine the <unk>.
Speaker Change: <unk> to the data in Hawaii. Its behaving this way again. So this again is a lady likely part of diagnostic and Olympics and coming to the third are type of data analytics big data analytics space and as the name suggests the quick question, who will be asking is what will happen in the future based on the past cleanliness, so finding out hunting out all historical.
Speaker Change: Point is we are planning and working to get it right and there is no way to get it right just doing it gets it right. Okay. So let's try this now we're going to play this game twice again its for 30 seconds. If you are willing enable will you. Please stand up you can do the ceded by the way, but if youre willing enable it stand up okay in a moment I am about to say.
Speaker Change: <unk> that are being used to basically predict all of these better outcomes using oh, let's say machine learning algorithms using deep learning using so many more concepts to predict something in the future based on the data. We have now is again, a very important niche of <unk> data analytics, guys. So predicting the future trend and possibly be.
Speaker Change: Go and I would like for you to pointed anything around here, including me. It's Okay appointed me I hope, it's not a bad thing you say when you pointed me, but point at different things and loudly and proudly call them different than what they are.
Ready.
Speaker Change: Again.
Speaker Change: Porchia pie.
Speaker Change: And the market based on the current trends self explain entity and then it helps and optimizing all of the business plan for the future because novel business has a direction to head to that you're predicting further aspects in the future and you know which path leads to a better business our strategy and this will give you an edge over all of the other businesses as well to give you.
Speaker Change: California.
Speaker Change: Shaker.
Speaker Change: Car.
Speaker Change: Library.
Speaker Change: Tennis racket.
Speaker Change: Purple.
Speaker Change: Orange.
Speaker Change: Future ahead.
Speaker Change: Hello.
Speaker Change:
Speaker Change: Really nice example think of the Netflix recommendation systems guys. So this will basically use statistical modeling to analyze all the content that it's being watched by the audience across the world. So let's say there are a couple of themes shows which drove any famous in India, but then there might be a couple a few shows which is all trending in the United States.
Speaker Change: And.
Speaker Change: And.
Speaker Change: That's you can stay standing because in a mere moments, we're going to do it again, so if youre comfortable standing where I'm about to do it again first. Thank you that was wonderful I heard great words being called out. It was it was fun and some of you in the back we're doing it in sync so it looks like Youre doing some seventies disco dance it was awesome okay.
Speaker Change: And let's say, Australia, United Kingdom, so much more on making sure that delight our users in the right geographical area get to know the light recommendations are extremely important for Netflix as a business right. So this again a predictive analytics will just do them. Good database again any provide us with the prediction of.
Speaker Change: This this was great now let me ask you just a few questions did you notice anything about the words that you were saying.
Speaker Change: Did we find patterns, perhaps maybe some of you were going through fruits and vegetables. A few of you were going through things that started with the letter a.
Speaker Change: All of the upcoming content into the show us that must be watched by all the different gasp of audience as well so let's say someone's I think in the United States is very interested in watching an Indian not blending TV feed eases that it's annoying on Netflix finding now did this person exist and to recommend and Indian television show for this person sitting in the United States to watch it.
Speaker Change: That's your brain, saying, Okay. You told me not to stockpile, so I'm going to try to be a little more devious and I'm going to give you a patterns okay.
Speaker Change: Same problem.
Speaker Change: When we teach that class I told you about that Improvisationally speaking class, we'd like to say your brain is there to help you.
Speaker Change: This is another a very good business opportunity for Netflix and then if they go about kneeling their recommendation system for everyone and just makes them a better business model and then it just makes a better user environment for the users to global doll watching we do a standardized so the next one the next type of downtime.
Speaker Change: These things that's doing have helped you be successful, but like a windshield wiper, we just want to wipe those suggestions away.
Speaker Change: And see what happens okay. So we're going to do this activity again.
Speaker Change: This time try the best you can to thank you brain. If it provides you with patterns, our stockpiles and just say thank you brain.
And disregard them, okay. So, let's see what happens when we're not stockpiling and we're not playing off patterns. We will do this for only 15 seconds and see how this feels baby steps ready begin.
Speaker Change: Olympic Prescriptive analytics, so as soon as we think of a prescription again.
Speaker Change: We think if a doctor or we can give something a medical appointment to something great here and it's again something similar the question, which would be Australia as what should be done so applying advanced analytical algorithms to ensure that youll make ready. Good recommendations you make sure you're bumping out we're eager to strategies that help the business and so much more as a very vital part.
Speaker Change: Kodak.
Speaker Change: Bicycle chain.
Speaker Change: <unk> Board.
Speaker Change: Nanos.
Speaker Change: Careful.
Speaker Change: Future Ed.
Speaker Change: Of prescriptive analytics case, so basically in was breaking down all of this complex information into a very simple set of steps and these steps are likely prescription we have handed out when he was at the doctorate and these are the prescriptions, which are pretty cautious as the let's say all of the precautions basically use.
Speaker Change: Yes.
Speaker Change: Okay.
Speaker Change: Okay.
Speaker Change: Hi, Ann.
Speaker Change: Please have a seat.
Speaker Change: Thank you again.
Did you notice a difference.
Speaker Change: Between the second time in the first time.
Speaker Change: Yes was it a little easier that second time.
Speaker Change: To remove any of the future problems that might occur and it is also helping performing predictive analysis. This will basically help to predict the outcome and eventually that will help us optimizing businesses when Dave. So this again demand the use of artificial intelligence and big data and then brita analysts person.
Speaker Change: No.
Speaker Change: That's okay. We're just starting these skills are not like a light switch it's not like you learn these skills skills and then all of a sudden you can execute on them.
Speaker Change: This is a wonderful game. This is a wonderful game to train your brain to get out of its own way.
Speaker Change: Always been that's what the data engineered as well and the data scientist as well because he's going to need all the help he can get with respect to artificial intelligence from the data scientist and help with respect to big data from the data engineer as well right. So all these three guys walking in correlation makeup of business and I've already told you that so again to give you a common <unk>.
Speaker Change: You can play this game anywhere anytime I like to play this game when I'm sitting in traffic.
Speaker Change: Makes me feel better than I shout things out they're not the naughty things that I want to be shouting out, but I shout out things and it helps.
Speaker Change: Your training yourself to get out of your own way, you're working against the muscle memory that you've developed over the course of your life with a brain that acts very fast to help you solve problems, but in essence in spontaneous speaking situations you put too much pressure on yourself trying to figure out how to get it right.
Speaker Change: <unk> of how this would happen.
Speaker Change: Consider the prescriptive data analytics that Google map goes about doing so.
Speaker Change: So L E Bay area communities from our office to our homes are let's say, we're going on a vacation and then you need to get out of the CB as quick as possible.
So a game like this teaches us to get out of our own way. It teaches us to see the things that we do that prevent us from acting spontaneously.
Google map has a wonderful API right of the map at the best possible root considering live traffic conditions weather conditions road closures and so much more it comes into view of distance traffic country, England again as I already told you even gone through those if it's raining you feel walking.
In essence, we are reacting rather than responding to react means to act again.
Speaker Change: You've thought it and now you're acting on it that takes too long and it's too thoughtful we want to respond in a way that's genuine and authentic.
Speaker Change: What's the best rule that you can take the work what's the best way to take FID on a car and now they've come up with the Baidu as vessels here in a moment or a bike you could take a different route compared to the person who do come out of the car and so much more so knowing this kind of a prediction into the future and giving you the sort of a prescription to ensure you don't run into any problems.
Speaker Change: So the Maxim I would like for you to take from this and again. These maxims come from improvisation is one of my favorite dare to be dull.
Speaker Change: And in a room like this telling you dare to be dull as offensive and I apologize but.
Speaker Change: Along the way.
Speaker Change: This will help rather than street striving for greatness.
Speaker Change: As a very important part of Brazil through data analytics guys. So on that note, let's quickly check out what the lifecycle of data analytics is like the deep analytics lifestyle. It basically defines the analytics process.
Speaker Change: Dare to be dull.
Speaker Change: And if you dare to be dull and allow yourself that you will reach that greatness. It's when U S set greatness as your target that it gets in the way of you ever getting there.
Speaker Change: And all of the best practices, which goes on from the discovery of the data or the project until the completion of this project.
Speaker Change: Because you over evaluate you over analyze you freeze up so.
Speaker Change: So the first step in our process today is to get out of our own way dare to be dull easier said than done, but once you practice and a game just as simple as the one we practiced as a great way to do it.
A couple of steps and warrant and this lifecycle process and the first one is business understanding I'd be aimed of it again understanding the purpose at all of the requirements that come from our business and understanding it from the business viewpoint is very important and white will flow to be functioning of a business like this also gunther.
Speaker Change: But that's not enough getting out of our own way is important but the second step of our process has us change how we see the situation we find ourselves in.
Have a very good introductory planet, Kansas over decision plan at Kansas are off it kind of a formal to do list, let's say for the business to go into achieving the target for the first important thing about the lifecycle is under.
Speaker Change: We need to see the speaking opportunity that we are a part of as an opportunity rather than a challenge and a threat.
Speaker Change: When I coach executives on Q&A skills, when they go in front of the media or whatever investors.
Speaker Change: Timing of what's going on around you and the second one is the data understanding with respect to data understanding again, mainly in malls. The process, where we are collecting the data and we are processing the data in a way which leads to analytics and then after the analysis of the data has done many do pick up some insight that we can go about using from the data so expecting.
Speaker Change: They see it as an adversarial experience may versus them.
Speaker Change: And one of the first things I work on is changed the way you approach it.
Speaker Change: A Q&A session. For example is an opportunity for you it's an opportunity to clarify its an opportunity to understand what people are thinking so if we look at it as an opportunity it feels very different.
Speaker Change: All of these meaningful insights from the data again is a very vital step in the lifecycle of data analytics through data understanding is or the.
Speaker Change: We see it differently and therefore, we have more freedom to respond when I feel that you are challenging me I am going to do the bare minimum to.
Speaker Change: Third part of it of data preparation so data preparation is the let's say converting the data from an unstructured form to a structured phone and this involves constructing let's say I did that that does well and then this data certainly provided and fed into a model and then this model will be used by a machine learning algorithm to train to understand to see what's going on.
Speaker Change: To respond and protect myself if.
Speaker Change: If I see this as an opportunity where I have a chance to explain and expand I'm going to interact differently with you.
Speaker Change: So spontaneous speaking situations are ones that affords you opportunities.
Speaker Change: Predictions and then it will be given to the data analysts do we visualized using tableau or any other business intelligence tools and so much more so data penetration again is a very important phase where the data is actually being transformed and even understates. The cleansing of the data is pretty much performed as well as and then comes from modeling.
Speaker Change: So when you were at a corporate dinner and your boss turns to you and says Oh, you know him better than the rest would you mind introducing him you say great. Thank you for the opportunity rather than.
Speaker Change: Right I better get this right.
Speaker Change: So see things as an opportunity.
Speaker Change: This step is very important because it didn't also selection of various modeling techniques blame all of these modeling techniques are making sure that bad on either the right and all of the meetings you had a right to ensure that your beta being converted into the inflammation draw Rodney there've been converted into the information is.
I have a game to play to help us with this.
This is a fun one the holidays are approaching.
Speaker Change: We all in this room are going to give and receive gifts.
Here's how this game will work it works best if you have a partner so I'm, hoping you can work with somebody sitting next to if theres nobody sitting next to you you turnaround introduce yourself great way to connect if not you can play this game by yourself, it's just a little harder and you can't do the second part of the game. So after I explain the game give this gives you a chance to get to know somebody.
<unk> or the optimal or let's say it's at.
Speaker Change: The optimal tolerance that can be used for all business usage and then as soon as you're modeling part of it is done that's one of the techniques are applied on a lobotomy doesn't Mark then comes evaluation guys. So evaluation is a very important phase of it again the model. It is being built it'll be big Lady vigorously tested Lady at a good at least based on what you've built.
Here's how it works if you have a partner.
You and your partner are going to exchange imaginary gifts.
Speaker Change: Pretend to have a gift can be a big gift can be a small gift and you will give you a gift to your partner.
Speaker Change: The initial stages based on what you have built in the initial stages and then so many tests will be performed on those data as well so evaluating something that youll generate in performing gladius best on it is extremely important or most of the name. This is all looked about NBC as everyone knows the value of evaluating good AWS. So this again and oils.
Speaker Change: Your partner will take the gift and open it up and we'll tell you. What you gave them because you have no. You just gave them again. So you are going to open up the box and you're going to look inside and you were going to say the first thing that comes to your mind in the moment not the thing you have I'll just thought of.
Speaker Change: Or the thing after that remember what we talked about before that's still plays that's still in play Hey, you're stockpiling look in their my favorite that I said somebody gave me. This gift during playing this game I looked inside and I saw a frog leg I don't know why I saw a frog leg, but that's what I said, that's the first part of the App.
Speaker Change: Reviewing all of the steps that out again needed to carry out.
Speaker Change: Ill caveat to construct this particular model N to perform tests on it is very important and with evaluation. That's exactly what we do this so the mixed all lifecycle concept I Wanna pay raises deployment deployment of the last step in the data Lake and the data analytic life cycle, because deploying guzman you're sending.
Speaker Change: Activity now.
Speaker Change: Now the opportunity is to fold in this game the opportunity is for you the gift receiver to name a gift that's kind of fun that as an opportunity it's not a threat.
Speaker Change: Oh, Dear model into the world of or let's say by the World I mean, let's say for your team for your pillars, or let's say even for your non customers as well, so or making a data room from just using it all the way to spreading the data and perform it.
Speaker Change: But the real opportunity is for the gift giver because the gift Giver then has to say.
Speaker Change: So you look and you say, thank you for giving me a frog's leg and the personal will look at you and say I knew you wanted a frog's leg because so whatever you find the person who is received it is going to say absolutely I'm. So glad you're happy I got it for you because so you have to respond to whatever they say.
Speaker Change: Spreading the data to your planes to your peers or anyone else. If you can put some more tests on visa spent so after deployment you can have after the plumbing best if something is wrong and you can go back to modeling performed mode evaluation performed well deployment as well. So the thing you need to know who are the deployment in pretty much all goes about to be the final phase of the day.
Speaker Change: Right.
Speaker Change: What a great opportunity now some of you are sitting there going Oh, that's hard I don't want to do I might make a fool of myself others of your if you're following this advice are saying what a great opportunity right. So the game again has played like this you and your partner will exchanging each will exchange a gift one will start and the other will follow the first person will give a gift to the second person second person opens the box. However.
Speaker Change: Donna Olympics lifestyle.
Speaker Change: And on that note or we can take out weight quickly or what are the rules of analytics in various industries around us guys.
Speaker Change: Again.
Speaker Change: Data analytics has an amazing insight and impact when it comes to the telecom industry, because again, if you've been absorbing all the prices of all let's say the calls the messages or the Internet back. These days have been coming down and down and there was a time when they were being exorbitant be high as well so again the core telecom.
Speaker Change: The boxes and if the box is big and you find a penny and it perfect doesn't matter. The box is heavy and you find a feathering. It fine. It does there's no way to get it wrong, Okay whatever's in the boxes and the box you can return it and get what you wanted later okay.
Speaker Change: The person then you will name it you'll say thank you for the whatever you saw in the box.
Speaker Change: We realize that if you keep the prices are very high to make more profit than you answer to your customers will not come they will not buy internet bikes. So to keep this in mind, probably they decided lets say that having a bottom by clayton or so let's dropdown advisers to feed like books and does I guess and this is L b telecom industry, and bringing better business and for other uses.
The person who gave it to you I'll say I'm, so glad you're excited I got it for you because.
Speaker Change: And you will give a reason that you got them whatever they decided you gave them.
Speaker Change: Makes sense alright, so very quickly just in five seconds find a partner if youre willing to do this with a partner everybody have a partner.
Speaker Change: This has helped us.
Speaker Change: Just I'll make it a bit economical and efficient on RSA, though with respect to money as well and then the retail banking and lastly, albeit down on it because of a huge impact on the retail banking industry as well to know what the customer wants because again.
Speaker Change: Okay.
Speaker Change: Alright in your partnerships.
Speaker Change: And your partnerships pick in a person in a b person you may stand or sit it's totally up to you.
Speaker Change: Again, with the telecom industry as well with them a huge amount of customers to play with right. So they need to understand the view of each customer has a requirement of each customer and then to find out of all of the customers that are actually in the common kindle for what's being supplied by the bank all of the customers that against something Thats being offended by the bank. So.
Speaker Change: Picking a and pick a b.
Okay.
Speaker Change: P goes first.
Speaker Change: Uh Huh alright.
Speaker Change: We give a a gift <unk> gave a gift a thank them.
And then B will name and give the reason they gave it to them.
Speaker Change: But that again is a very important thing that you need to take care of as well and then with respect to the E. Commerce industry is that the performance again somewhere in analytics in the ecommerce industry made me add recommendations are there a really big fans, which are run by some of the big names in the industry, such as Amazon flip God mentor and so much more.
Speaker Change: Okay.
Speaker Change: Okay.
Speaker Change: So these guys will have to perform extremely heavy analytics on the data that they're fee based on the products that they sell and based on the places based on the city and with the product set of the people are unhappy with the prize again performing analytics has just changed the e-commerce industry. If you mean, guys and the and lost most embark.
Speaker Change: Yes.
If you have not switched switchblades, if you have not switched switch plays.
Speaker Change: And then lastly, you added audit analytics has stopped for the healthcare industry doesn't have the most impact with respect to analytics in the healthcare industry with respect to so many things are me me doesn't respect to with respect to finding out what medications required by what countries what amount of medication is working for what population and so much more that I can draw.
Speaker Change: Yes.
Lee Talkable Duffy roads of analytics in these industries, where days together and we can still be going on and have a very good discussion of how important analytics has become leased is again with respect to insurance as well just a quick info guys. That's generally just data analytics by answering this question which of the following.
Speaker Change: So let's wrap it up in 30 seconds place to wrap it up.
Yes.
Speaker Change: Yes.
Speaker Change: All right if we can all have our seats.
Speaker Change: Method creates a new object that looks at the same data eight deal.
Speaker Change: If we can al.
Speaker Change: Take our seats please.
Coffee.
Speaker Change: Based.
Speaker Change: All of the above I'm going to answer in the comment section below subscribed into Loopnet right onto now that's going to do with decision what geographical location requires insurance, what the or the audience odd and so much more so again as I've said, we can go on talking about this but then to give it to the scope of the study.
Speaker Change: I know I'm, telling a room of many.
Speaker Change: MBA alarms to stop talking and that's hard.
Speaker Change: All right, ladies and gentlemen.
Speaker Change: Did you get what you wanted.
Speaker Change: Pretty neat hi, you always get what you want now for some of you. This was really hard because you were really taking the challenge and not seeing what was in the box until you looked in there okay with anybody surprised by what you found in the box.
Speaker Change: All we can discretely brief to each of the base so with respect to the healthcare industry I'll do the formal way of going about analytics are the first one is to again underlies all of these disease <unk> analyzed all of the disease outbreak that pretty much goes out we had a disease outbreak a couple of your stack, which is Ebola and so much more we had a H one N one and so.
Speaker Change: What did you find Sir what was in the box.
Speaker Change: But.
Speaker Change: Right.
Speaker Change: Wow nice nice if you've got a Ferrari you need a transmission I like it who else found something that was surprising what did you find.
Speaker Change: Tomorrow to keep it lack of all of these outbreaks. This basically again in blue saw the surveillance with respect to health and then this gives out good responses to the emergency sectors has been gas and then development of better targeted preventive techniques.
Speaker Change: A live Unicorn, that's a great gift right.
Speaker Change: How was it as the gift giver were you surprised at what your partner found in the box isn't it interesting that when we give an imaginary gift knowing that the person who's going to name. It we already have in mind, what theyre going to find.
Speaker Change: Obviously, and then the development of vaccines, making sure your vaccines again blue chip customers and all of these led all you need to Egypt customers are let's say and this gives the patient arval very important Nathan So island defined consumers again and this is the greater Cisco fall business. So I in defining certain customers of patients one of the great.
Speaker Change: And when they say live Unicorn, we go well that's interesting right.
Speaker Change: So.
Speaker Change: The point of this game is.
Speaker Change: Two one remind ourselves we have to get out of our own way like we talked about before but to see this as an opportunity and to have fun I love watching people play. This game the number of smiles that I saw amongst you and I have to admit when I first started some of you looked at little dour, a little doubting, okay, but in that last game you. We're all smiling and it looked like you were.
Speaker Change: This risk.
Speaker Change: Again.
Speaker Change: Because there might be developing somewhat doors health outcomes, and then developing welfare programs to keep track of their health to track.
There has on a daily basis, even a weekly basis monthly basis to perform and Olympics and all of the aspects in the bottom he doesn't you're tracking with respect to the patients again that is a very important day and lastly to ensure that they can order it use it he admissions because they might know what the cause of fault and adverse effect is and if there are 10 patients with very similar symptoms.
Speaker Change: Having fun so when you reframe the spontaneous speaking opportunity as as an opportunity as something that you can co create and share all of a sudden you are less nervous less defensive and you can accomplish something pretty darn. Good in this case a fun outcome.
Speaker Change: Then you can put a whole analysis and you will not be filled.
Speaker Change: He went out and find out that all of these 10 patients might have this one common symptom.
Speaker Change: Symptom associated with them and this might be the cause of that so mapping that for every patient is again very important so coming due we are telecom in Australia in telecom industry pretty much goes about using predictive analysis to gain all of the insights that they need to make better decisions to make faster decisions and to make more effective decisions again, they're talking about the Internet Bank example, again this was.
Speaker Change: This reminds us of perhaps the most famous of all improvisation sayings, yes and.
A lot of us live our communication live saying no but.
Speaker Change: Yes, and opens up a tremendous amount of opportunities and this doesn't mean you have to say, yes, and two a question somebody asked this just means the approach you take to the situation.
Speaker Change: Very keen that and then by learning more and more about the customers' daily and the percentages and the needs. These telecom companies can be more successful in this extremely highly competent new industrial well, it's good for them with respect to business and it is good for us as customers by bringing down the prices again. So it is used for analytical customer relationship management.
Speaker Change: So you're going to ask me questions, that's an opportunity, yes, and I will follow through.
Speaker Change: Versus no and being defensive.
Speaker Change: So we've accomplished the first two steps of our process first we get out of our own way and second we reframed the situation as an opportunity.
Speaker Change: The useful fraud election, it is useful bad debt reduction to surprise optimization call center optimization and so much more so now that you are looking at data analytics and this way you realize that data analytics. Eventually has a big play or has a big theme when it comes to any of these business models and eight so again coming to banking as already mentioned analytics was making.
Speaker Change: The next phase is also hard, but very rewarding and that is to slow down and listen.
Speaker Change: You need to understand the demands of the requirement you find yourself in.
Speaker Change: In order to respond appropriately, but often we jump ahead, we listen just enough to think we got it and then we go ahead starting to think about what we're going to respond and then we respond we really need to listen because fundamentally as a communicator. Your job is to be in service of your audience and if you don't understand what you're.
Speaker Change: Banks become very smart day-by-day, guys. So managing all of the plethora of challenges that the bank faces and then again why pretty much all going about doing some basic reporting all the way to lift crypto and Alex. This is all a must for every single bank grade even performing let's say advanced a prescriptive analytics is so much more.
Speaker Change: <unk> is asking her needs you can't fulfill that obligation, so we need to slowdown and listen.
And all these are starting at this age or banks are starting to realize that this will again help you generate very good insights and this will result in extremely good business impact that'll help the banks has been on that or we need to take all told eat analytics is helping in the banking industry is a later refused to acquire and retain customers at a new subject, Florida, which is again extremely important.
Speaker Change: I have a fun game to play.
Speaker Change: And this game you are going to S. P. L. L. E V E. R Y T H I N G Y O U S E y.
Speaker Change: T O Y O U R. P. A R T N E R.
Speaker Change: Banking it is used to improve risk control find new sources of growth for the bank and to optimize all of the product and generate the portfolio models as well so as soon as we take on the banking sector again with the ecommerce industry right. So this is again due the monitored with exploding for the last couple of years I can see even the cantillate ebay came off flip God came up.
Speaker Change: I will translate.
Speaker Change: Youre going to get with the same partner you've just worked with and you are going to have a very brief conversation about something fun that you plan to do today I know this is the most fun youre going to have all day, but the next fun thing Youre going to do today, you were going to tell you partner, what you were going to do that will be fun today, but you were going to do so by S. P. E. L. L. I N G.
Speaker Change: Amazon is again digging a little evident they meant a night got guys that are so many E. Commerce board has today and to make sure. We both are in very good analytics on leaf E Commerce industries as many vital so how was it analytics. He was again introduced to improve user experiences interviews to enhance customer engagement customized offers and promotions maintain effect.
Speaker Change: <unk>.
Speaker Change: Yes.
Speaker Change: Okay.
So youre going to spell it.
Speaker Change: It's okay. If you are not a good speller.
Speaker Change: Okay.
Speaker Change: You'll see the benefit of doing that so with the partner you just worked with person a is going to go first. This time you are simply going to tell you a partner actually youre going to spell to your partner what it is.
Speaker Change: Supply chain management optimized, but I think Martin is minimize the risk of fraud provide them very good advertisements that pretty much held them pick up products. Good recommendation systems that it'll pick up in other product. After the first product gave us so much more if you've just bought an iPhone again, you will pretty much be a recommended with a couple of cases that the people have bought as soon as the board. Another iPhone. So you might be there.
Speaker Change: A fun something a fun that you're going to do today. Okay. Do you. What you were really going to do for fun and not do things like S. E. T. H E C. A T right just because you don't want us felt right. So.
You can use big words, alright, 30 seconds, each spell to your partner something fun that youre going to do today.
Speaker Change: And you might like to give a new mine pick it up at the end of it you have the case to predictive one the business is creating more money out of it right. So again the analytics the role of analytics and E. Commerce industry is it's extremely widen let's say the people in the E. Commerce industry have known this for a while so coming to the analytics and the insurance industry again.
Speaker Change: Would you like to play.
Speaker Change: Go ahead.
Speaker Change: Okay.
Speaker Change: Okay.
Speaker Change: Oh, my goodness eight against spell it again, yes.
Speaker Change: <unk> basically used to enhance customer engagement acquired new customers regain being listing customers make sure that your customers don't leave prevented the fraud at the end of or the venue was the Florida prioritize all the claims that needed to be you'd have medical insurance, you'll have fog and health insurances you'd have life insurance you have so much more that you need to take and all of these directors.
Yeah, Hey, Amy E X C E. L. L E N T I H O P E T H E T T. A G Y W. I N.
Speaker Change: Okay.
Let's see.
Speaker Change: Thank you that was very good thank you.
Any impact to US right, so making sure you take the feedback from the user walk on it and then create some analytics out of it is again very vital guys. So on that note. We can quickly come to our Rocky study, which I was talking about and this gave that either very famous one its basically the house up by the gate I mean I'm sure all of you be doing.
Speaker Change: If you.
Speaker Change: Have not switched switch.
Speaker Change: Predicting how spaces. So we have a certain set of data, which we will use to predict the prices of the houses. So basically how can you predict appraisal for house Theres. So many things that you would need to know that youll be looking at the locality and which dialysis present will be looking at the omni deal will be looking at the number of bedrooms. The living space, then I'm going to <unk>.
Speaker Change: Takes 30 more seconds with a new partner spelling.
Speaker Change: Florida's in your house or the number of cars that can fit into a garage the size of the garage the quality of the construction of the house. If it has a swimming pool in order for toys R. US bar, none I mean, so many things if we have to list down all of this particular use case, then it will be extremely tough because each one of us has our own judgment of how.
Speaker Change: G R a T.
Speaker Change: T exclamation point P. H, a N K Y O you P. L E. A S E T. A K E Y O U R. S C a T.
Sure.
Speaker Change: We can validate the house right because how is it something that is very personal to US again the materials of what was used to go to build the house the style of the house, which was built in the number for the houses and elevated how convenient is the house where are do they will be but there's so much more so basically we'd be performing exploratory analysis on those days of exploration.
Speaker Change: So what did we learn.
Speaker Change: What did we learn besides that were not so good at spelling.
Speaker Change: Do you have to parse between the words.
Speaker Change: How did this change your interaction with the person you were interacting with what did you have to do.
Speaker Change: Donald This is again is used to find a hidden trend in your data by performing analysis on it and then at the end of the trends would be shown as numbers, but since we already know visualization, we are going to be pretty much are using these numbers to visualize all of the data for us and we'll be doing it step by step. So it would be finding correlation between the data is the correlation is basic.
Speaker Change: Focus and listen.
Speaker Change: And you can't be thinking ahead, you have to be in the moment when you listen and truly understand what the person is trying to say then you can respond in a better way a more targeted response, we often don't listen.
You need to check how one variable is linked and how the changing of one really will be directly.
Speaker Change: So we start by getting out of our own way.
Speaker Change: We then reframe the situation as an opportunity those are things, we do inside our head, but in the moment of interacting we have to listen first before we can respond to the spontaneous request.
Speaker Change: It changes the other radio Blizzard to how these two radio moves are all pretty much hung up together, how changing one way they would change the other can be known using correlation as well, yes. So a couple of steps that would be much too generally followed in the case of the farming exploratory analysis, firstly, you'll be realizing all of our data finding the missing values that we've been looking for coordinator.
Speaker Change: Perhaps my most favorite Maxim.
Comes from this activity.
Speaker Change: Don't just do something stand there.
Speaker Change: <unk> days and then after this is done will be cleaning the data to take if any issues are fake or we'd be taking out of the data that we have we have is pretty much all being used fully or not and then we go about building a model, which is used to visualize a result, it will give us the diagnostic it'll give us the residue diagnostic auto seekers charged for golf stabs are tables and so much more.
Speaker Change: Listen.
Listen and then respond.
Speaker Change: Now how do we respond that brings us to the fourth part of our process and.
Speaker Change: And that is we have to tell a story we respond in a way that has a structure. All stories have structure, we have to respond in a structured way.
Speaker Change: I do not want to basically over value with the use case so to keep this use case really simple. We just performed our exploratory data analysis at this stage to find out correlations between the data and as soon as the global are progressing with respect to our data set and to work with you will understand how beautiful data analytics escape. So let me quickly.
Speaker Change: The key to successful spontaneous speaking and by the way planned speaking is having a structure.
I would like to introduce you to two of the most prevalent and popular and useful structures you can use to communicate a message in a spontaneous situations.
Speaker Change: But before we get there we have to talk about the value of structure. It increases what is called processing fluency, the effectiveness of which or through which we process information.
Speaker Change: Jumping to Google Collab, which is basically a Jupiter nonbook hosted on the Google Cloud and here. We can go about performing our raw data and analytics on the use case that I just walked Youtube. So we can meet a couple of fires to run our use cases are well I can eat one file which is our training data set file let me just quickly add the fight.
Speaker Change: We actually process structured information roughly 40% more effectively and efficiently than information that's not structured.
Speaker Change: I love looking out in this audience because you will remember as I remember phone numbers. When you had to remember them. If you wanted to call somebody Okay yup.
Speaker Change: Two our Google Collab, and then we can global preceding performing got analytics Guy. So those are just because they can do applaud the fine give me a second.
Speaker Change: Young folks today don't need to remember phone numbers. They just need to look at a picture push a button and then the voice starts talking on the other end 10 digit phone numbers, it's actually hard to remember 10 digit phone numbers. How did you do it you chunked it into a structure three three and four.
Speaker Change: We just actually need one file form out here, but then it doesn't harm to upload it and keep it in your London, but then you just take the message data, particularly pretty much all of your files that are recycled lessening. The run time is pretty much changed so the first step for our used cases to load all of the necessary.
Speaker Change: Structure helps us remember.
Speaker Change: The same is true when speaking spontaneously R&M planned situations. So let me introduce you to two useful structures. The first useful structure, you have probably heard or used in some incarnation. It is the problem solution benefit structure you start by talking about what the issue is the problem.
Speaker Change: Refis in the libraries that we require to get to the first again, we'd be using <unk> to handle all of our data will be using seaborne and Mac brought live to perform plotting operations at all or is it eight outperformed visualizations on all of these data despite them using is pretty much call. It EMEA image method and what does it take it'll be image again be image is nothing but the bashan method for hikers and boom.
Speaker Change: You then talk about a way of solving it and then you talk about the benefits of following through on it very persuasive very effective helps you as the speaker remember it helps your audience to know where you're going with it.
Speaker Change: This type of a graph when would you like vision method, which gives us good asked which look nice it and then it helps us to perform analysis bedded on linear datasets gas. So the second thing will be doing is again loading all of the necessary to fight the one of the important financing we needed the training dataset, which is.
Speaker Change: When I was a tour guide on this campus many many many years ago.
Speaker Change: What do you think the single most important thing they drilled into our head it took a full quarter by the way to train to be at a tour guide here. They used a line us up at one end of the Quad and have us walk backwards straight and if you've failed you had to start over to this day I can walk backwards in a straight line because of that.
Speaker Change: This I'm sure you have the idea of the houses you have the subclass of that'd be offices, but isn't able to zone in which thousands present or what are the idiopathy frontage that you have what are the area a lot of your house what are the street itself. What are the alley again large share block on tour of what does it you have the houses built and order those remodeled in order to save them.
Speaker Change: As part of that training what do you think the most important thing they taught us was.
Speaker Change: Never lose your tour group.
Speaker Change: I'm not sure. It Nevertheless, that's never lose your trip.
Speaker Change: The roof.
Speaker Change: Again, assuming the conditions out there what are the foundation made of how is the quality of the basement condition of the basement the exposure to the basement again, finishing type of debasement guys. Just look at how expansive. This dataset is this is again, a dataset, which is extremely popular among us analysts there are we pretty much all.
Speaker Change: The same is true as a presenter never lose your audience. The way you keep your audience on track is by providing structure.
Matt: None of you would go on a tour with me if I said Hi, My name is Matt Let's go.
Matt: You want to know where you're going while you're going there how long it's going to take you need to set expectations and structure does that problem solution benefit is a wonderful structure to have in your back pocket.
Speaker Change: Like to walk on this because it comes off everything and you've been performed so much on the single dataset and so much more organized so on that note, let's quickly find out the information of all the variables in our present again as I was talking to if the housing is heating how is the quality control of the heating does a centralized air conditioning, how was the condition of the electricals what are the R square footing of the first store square.
Matt: It's something that you can use quickly when you're in the moment.
Matt: It can be reframed. So it's not always a problem you're talking about maybe it's an opportunity maybe theres a market opportunity you want to go out and capture it's not a problem that we're not doing it but maybe we'd be better off if we did so it becomes opportunity solution, which are the steps to achieve it.
Speaker Change: Putting the second lower what are the low quality finished itself and how many square feet of that do we have our living space area. How many full bathrooms, we have how many have bathrooms do we have a kitchen call it'd be guys. This will go on right. So all of these data is what we need to check out again, if you see here all of these are the values that up but I think which will help us.
Matt: And then the benefit.
Matt: Another structure.
Matt: Which works equally equally well is.
Matt: Is the what so what now what structure.
Matt: You start by talking about what it is.
Matt: When you talk about why it's important and then what the next steps are.
Speaker Change: Map something but then Ali al again does not many values you can take out your order fill rates of Ali. It's any of them is basically not a number. So there are not many you know ali or details, which we can meet analysis out there. So we will not require any again coming down.
Matt: This is a wonderful formula for answering questions for.
Matt: We're introducing people so if I'm in the moment somebody asked me to introduce somebody I changed the what to who I say, who they are why they are important and what we're going to do next maybe listen to them maybe drink our wine whatever right. What so what now what the reality is this when you are in a spontaneous speaking situation you have to do two things simultaneously you have to figure out what to.
What's your fireplace quality as well, we do not have for bone quality control at all miscellaneous features already less again, even fencing is very less so the average is somewhere around 1400, right. So to make sure that our cleaning up all the adults are very important part of it is basically how we go about doing it again all this is more all of these data out.
Matt: Say and how to say it these structures help you.
Matt: By telling you how to say it.
Speaker Change: Let them, 30% or so less than 30% off again. Unfortunately zero and then we can have at least 70% of the data to give us some I get it there's I'll say, there's already take Ali I D. Not empty houses in IV, So thats a moot.
Matt: If you can become comfortable with these structures you can be in a situation where you can respond very ably to spontaneous speaking situations.
We're gonna practice, because that's what we do here.
Matt: Here's the situation as everybody familiar with this child's toy its a slinky.
Speaker Change: Every house mapped doing Ali a bone quality gondolas, all their friends and loved that miscellaneous which is already list. So we have got all of these columns and will not be using <unk> to pull forward analytics says and so again to describe how it goes onto a no or do a distribution of <unk>.
Matt: Okay.
Matt: You are going to sell this slinky two year partner.
Matt: Using either a problem solution benefit.
Matt: Or opportunity solution benefit what does the slinky provide you.
Speaker Change: I Hope you guys noticed concept award normal distribution and all and with respect to all of the details that we can get out of it when we perform the math operation gave so basically we can count the total number of data to present with respect to all the individually. The what does the mean a sale price of the data what is the standard deviation, let's in a minute.
Matt: Or you can use what so what now what what is it why is it important in the next steps might be to buy it so by using that structure see how already it helps you it.
It helps you focus.
Get with your partner and we're only going to have one partner sell to the other partner.
So get with your partner.
Speaker Change: The normal distribution of the I understand there are some that here right. So the mean is somewhere around 18000 date of I'm sorry, There's 180000, if you just keep raising from the Centerpoint down. This is something that lateral 180000 exist. Today. So again what are some of kind of deviation is basically the deviation from the mean, so what is that our house values with BVA.
Matt: One of you will volunteer to sell to the other okay sell a slinky using problem solution benefit or what so what now what please begin.
Speaker Change: This means when again, 25% deviation. So if people some deviation 75 per cent deviations and then what is the maximum sale price of the house as well. So all of these can be found out from this particular graph, Dave and if you can already observe and even if youre not exposed to normal distribution. This started out as a very steep curve, but then it aimed out.
Matt: Yes.
Matt: Yeah.
I'm also going to be doing that the microphone. So.
Matt: When I Debrief. This you can go ahead and pass them out does that make sense.
After this activity and then.
Matt: Correct.
Speaker Change: With respect to a lot of data he orders really goes on in the low 800000, so basically probably from 5000 or even a 400000 all the way to 800000 week. All of these are outliers or these are called outliers because these at a very moderate from our normal distribution and then this actually might not be useful for us with respect.
Yes.
Speaker Change: Our mean or whatever and these will impact a lot when we're performing analytics with respect to the mean or standard deviation or anything for that matter. So we would have to actually remove them and not consider them to basically performed very accurate analysis. So on that note, we can pretty much gone to finding.
Right.
Speaker Change: Type of the dataset from the time of the data that will only concern because in this particular case since we're playing with numbers. It has to be the pneumatic DD&A rate again, if you can check out we are indeed, a number to the floating numbers and so much more so that's pretty much gone to print or what it looks like after we have dropped the values, where we not using we're not using I'd, even not using Ali we don't think so.
30 more seconds. Please.
Speaker Change: Much more right. So these are all the numerical values that will be using to pretty much constant again, you would've been to the numerical value 2003, the bid I mean, your overall condition overall quality all of these can be repaid from a particular scale right. So again a square footing is a vertical numbers litho. The second floor of this particular house has 854 square feet. So.
Matt: Excellent that's all.
Matt: Closed the deal sealed the deal.
I have never seen.
Speaker Change: Much more so on that particular node as soon as he took out all of the numbers up but then we can start off I mean of analyst has been so before that I mean, you don't just flawed all of these to just thick all what it looked like on drops because seeing numbers are one thing feed that's on the other hand or something else, it's a pretty much immediate developing histograms and we can be taking out.
Matt: More people in one place doing this at the same time.
Matt: I love it I teach people the gesture and gesture big it's great I love it.
So if you were the recipient of the sales pitch thumbs up did they do a good job.
Matt: Did they use the structure awesome I'm recruiting you all for my next business is my salespeople.
So let me just disclosed on a little yeah. So with respect to foster square footing. The mean is somewhere around Dol here right is around let's say 500 square foot thousands square foot and again look at our second floor at all the other square footing look in the bedroom average basement, finishing qualities of garage as the number of cars that you could partner.
Matt: Please try to ignore this but as we're speaking the handout I told you about is coming around.
Matt: On the back of that Handout, you were going to see a list of structures. The two we talked about and several others that can help you in spontaneous speaking situations.
Matt: These structures help because they help you understand how youre going to say, what you say structure sets you free and I know that's kind of ironic, but it's true. If you have that structure then you're free to think about what it is you're going to say.
Speaker Change: Rather than looking at value here too. So the majority of the houses you'd have to have spaces to bar and patio cards. The year to be get age was built in just a quick info guys. Interloper provides online data analytic scores in partnership with IBM and Microsoft of course link is given that the description below now let's continue with the session.
It reduces the cognitive load of figuring out what you're saying and how youre going to say it all of this is on that handout. Okay.
Speaker Change: Again, all day long for living gave you of Hanmi have bathroom. So you can see one half bathrooms again at this point of time, you have certain values are vital as well well sure. We can come to the values of zero. If it's very important but then essentially talking about sale price. What does present is more important than what is absent right we need to have something in the script.
Matt: So what does this all mean.
Matt: It means that we have within our ability.
Matt: The tools and the approaches to help us in spontaneous speaking situations. The very first thing we have to do is manage our anxiety because you can't be an effective speaker. If you don't have your anxiety under control.
Further our analytics methods to work. So that gives me you'll have something called the Golden feature list and radio, but who basically got dates all of the features are that will be associating with respect to why all the fish prices as high as it does so this variable called as the Golden features list will have all of the features guys. So basically we are creating a very.
Matt: And we talked about how you can do that by greeting your anxiety reframing is a conversation and being in the present moment.
Matt: Once you do that you need to practice a series of four steps that will help you speak spontaneously first you get out of your own way.
I would love it if all of you on your way from here to the football game.
Speaker Change: But we are finding on the correlation and then we can already checkout the top been correlated values, which are strongly correlated by correlated again, let's say Oh, let me do a quick back basically we have all of this game, though this indeed defending Gordon as you can already checkout. So the first thing here is overall quality again or to transcribe doesn't do literally dumped.
Matt: It things and call them the wrong name it'll be fun, if most of US do it then it won't be weird, if only one and two of US do it'll be weird right second.
Matt: Give gifts.
Matt: By that I mean see your interactions as ones of opportunity not challenges.
Speaker Change: Third take the time to listen.
Speaker Change: Overall quality of the house what are the leading that was given in our dataset is monitoring the most of how the houses as being bright again, the linearity of it the number of garages, you can borrow because having almost 64% impact of fall with respect to why the places like that garage area of having a 62% them back the basement square footing has 61.
Matt: Listen.
Matt: And then finally use structures and you have to practice. These structures I practiced these structures on my kids I have two kids when they ask me questions I, usually answer them and what so what now what.
Matt: They don't know it but.
Matt: But when they go over to their friends' houses and they see their friends as their dads questions. They don't get what so what now what so you know you have to practice the more you practice the more comfortable you will become.
Speaker Change: Send them back and then the year. It was remodeled then changes made as having a 50% impact of lighthouse Bayou places like that base. So looking at a couple of for a linear relationships you can pretty much find out that a lot of values. Our leaders that I just walked you through a couple of things they look at that with respect to our again the gone for a living.
Ultimately you have the opportunity before you to become more compelling more confident more connected as a speaker.
Matt: If you leverage these techniques.
Matt: If you're interested in learning more this is where I do a little plug Hey, I've written a book many of the MBA students, who take the strategic communication classes here that I and others teach read it it's called speaking up without freaking out.
Oh very little number of zeros with respect to the sales plays again I'll take out the basement door surface area again with respect to the debasement of is it yeah I didn't even join as well. So look at all of these tiny dots with just taking up the zeal with respect to the Pittsburgh to leaf have new impactful on our sales price because they have they're not giving us any valid linear do you look at this again video wall.
More importantly, there's a website here that I keurig called no freaking speaking.
Matt: And it has lots of information that I've written and others have written about how to become more effective at speaking. So that's that's the end of my plug what I'd really like to do is enter into a spontaneous speaking situation with you.
Speaker Change: It is at least up to a lot about $600000 sites. So all these are not adding any meaning to our data. So FX is equal to zero again. This might indicate that house does not have that feature adult. So if this is veto. This host of like I mean, a lot of policies do not have Bluetooth so pool area illegal.
Matt: And I would love to entertain any questions that you have there are two people who are running around with microphone. So some of US who remember the Phil Donahue show, we're going to do a little bit of that if you have a question the microphone will come and I'm happy to answer it.
Speaker Change: That particular case, we need to remove all of the veto. So that we can go on to finding more coordinated value that we can actually use to go about working with them because again here all of the coordinated values would be found as soon as we land is coming basically they're off starting it again it'd be descending order to find out what helps more and you can see that is almost 80% of.
Speaker Change: I think if he did on yes, yes.
We can hear you great could you talk about hostile situations hostile situations, yes. So when you find yourself in a challenging situation first it should not be a surprise to you it should not be a surprise before you ever speak you should think about what is the environment going to be like so it shouldn't surprise you that there might be.
Speaker Change: For the total quality of the house, which my daughters, when Youre buying the because of the buys of the wholesale and the people are actually preferring this living area. Our second floor surface area as it has a 67% chances are for affecting the bright and so much more and take out would be golden our features our list looks like.
Speaker Change: Some challenges in the room.
Speaker Change: When there are hostile situations that arise you have to acknowledge it. So if somebody says that's a ridiculous idea why did you come up with that just simply say so the idea I came up with was right acknowledge the emotion I recommend not naming the emotion.
Speaker Change: With respect to all of these to only go to their values. We just found against your remodeled euro built are also much more so total surface area again number of full bathrooms divorce or surface area of the garage area total basement door square footing. The number of cards you can pardon the square footage of the second floor, the living area and the oil quality and in this particular order.
Speaker Change: Okay. So you sound really angry versus I'm, not angry and frustrated now we're arguing over their mental state right emotional state. So so I say something like I hear you have a lot of passion on this issue or I hear there is great concern from you. So you acknowledged the emotion because otherwise it sits in the room and then reframe a respond the way that makes sense. So if somebody raises their hand and says your prada.
From the leased the highest is exactly what we're trying to find out with respect to exploratory data analysis. So just looking at the data that you could never figure out why be list price of a house with so much and once we break down into simply don't like this we can find out that there is an 80% impact from the oil quality.
Speaker Change: As ridiculously priced why do you charge so much.
Speaker Change: I might say I hear great concern and what you're really asking about is the value of our product and I would give my value proposition and then I would come back and say and because of the value. We provide we believe it's priced fairly so you answered the question about price, but you've reframe it in a way that you feel more comfortable answering it so.
Speaker Change: The house or whats the users are taking out to discuss sort of the house or not of the quality is very low even like pick up the house and the quality is higher than shown he will pick up the house. So 80% of the reason why there by the satellite that is a very important I think to for why the house appraiser casing studies and bottlenecks again. This has been a very important really amazed dataset the wall.
Speaker Change: The way to do this is to practice all the skills. We just talked about the only skilled that I'm, adding to this is the awareness in advance that you might be in that situation first I have to truly listen to what I'm hearing right. It's very easy for me when I hear a challenging question to get all defensive.
Speaker Change: With I didn't even get a lot of analytics that can be done using god. This particular dataset as well next up I wanted to discuss the skills of a data analyst with you guys. So you know on data analysts is a person who pretty much works with and create a lot of beautiful ratio escape. So to do this you need to understand the basics of mathematics.
Speaker Change: And not here with the person's asking I see it as an opportunity to reframe and explain okay. So again you have to practice.
Speaker Change: But that's how I think you address it are there other questions I see a question back here, yes. Please let's first of all thank you very much great great presentation.
Speaker Change: Because.
Speaker Change: And if you have to have knowledge of statistics mathematics of the foundation to go and offer you understand the foundation built a step by step growth a carrier bought for yourself, where learning becomes the most important thing because again, having the knowledge of the vertex will help you clear with a lot of numbers at the same time and keeping them at par.
Speaker Change: For a lot of the the speaking I do I have remote audiences. So huntington distributed all over the country with telecom and tips for those kinds of audiences. So when you are speaking in a situation where not everybody is co located okay. In fact right at this very moment there are people watching this presentation remotely.
Speaker Change: Aside for a second you need understanding of languages, such as pipe and a programming language as well.
What you need to do is be mindful of it second try to include engagement techniques, where the audience actually has to do something.
Speaker Change: At this point if there might a goal here is to not overwhelm you. If you did not know statistics. If you do not know Python at auto any of these technologies mentioned on his clean wealthier not make sure to stick with me till the end of the video and I will guide you on a fast track, but to become a professional data analysts Gabe and then the next point we have this data.
So physical participation is what we did here through the games you can ask your audience to imagine something imagine what it would be like if when we tried to achieve a goal rather than say here's the goal. We're trying to achieve say imagine what it would be like yet see what that does to it pulls you in I can take polling questions. Most of the technology that you're referring to has some kind of polling feature.
Speaker Change: Wrangling data wrangling again is to have an idea to play around with the data through the data, which is not needed to keep the important ones to know that you can work with because if you are working with inefficient data youll wish realizations would be really bad.
Speaker Change: You can open up some kind of wiki or a Google Doc or some collaborative tool where people can be doing things and you can be monitoring that while you're presenting so I might take some breaks I talk for 10 15 minutes and say, okay. Let's apply this and let's go into this Google Doc I've created and I see what people are doing so it's about variety and it's about <unk>.
Speaker Change: Again, if I bring my own afford example, adding a little less salt or adding too much salt is bad as well and to give you a more clarity of data wrangling lets say youre preparing noodles, but then if you just put the noodles in find it working it's working because you've just making use of the baidu need, but what if we throw the entire package of notice along with.
Gage meant those are the ways that you really connect to people who are remote from your.
Other questions.
You pointed out I've got to look for where the Mic is just this may be a similar to the first question, but I do a lot of expert witness testimony, what's your recommendation for handling cross examination specifically.
Speaker Change: The packet right it doesn't make sense, if you try to boil the bucket as well what is the right data wrangling makes somewhat fence in the world of data cleansing and pretty much data processing gas. So you need to understand your data before you do this and then coming to a little bit of a big data concepts you will require a little bit of knowledge about spot couple of competence and towards a spot.
Specific radiological to cross examine right.
Speaker Change: So.
Speaker Change: In any speaking situation that you go into that had some planned element to it I recommend identifying certain themes that you think are important or believe need to come out and then with each one of those themes have some examples in concrete evidence that you can use to support it.
One of it is big and other one off under the one is higher as well gas well.
Speaker Change: To break it down a little bit simpler for you guys, let us talk about each skills.
Speaker Change: Go in with memorized.
Speaker Change: Last thing I want to talk about is the analytical skills. Because you know data analysts will work with large amounts of data at every point of time. So what does this large amount of data that can include a lot of facts. It can be a lot of numbers that can be a lot of figures and so much more days. This effort is structured data. If it is unstructured data they can work with images.
Speaker Change: Terms are ways of saying it you just have ideas and themes and then you put them together as necessary. So when I'm in a situation where people are interrogating me I have certain themes that I want to get across and make sure that I can do that in a way that fits the needs in the moment. If it's hostile again use the single best tool you have.
Speaker Change: They can work with audio video and much much more of the same time, so basically what they'll do as you know we need to go through this particular data they need to understand the data and analyze it to pretty much find some sort of evolution. So that is a very important skills to half and then coming to the communication skills well again as I told you.
Speaker Change: To buy yourself time and to help you answer a question efficiently is paraphrasing. The paraphrase is like the Swiss Army knife of communication, if you will.
Speaker Change: Remember the show Macgyver, it's your macgyver tool right. So when a question comes in the way you paraphrase. It allows you the opportunity to reframe it to think about your answer and to pause and make sure you got it right. So when you run to those situations. If you have the opportunity to perfect day, So what you're really asking about is X y and Z that.
Speaker Change: At the start of this presentation. It says I data analysts will have to present his or her findings to store nontechnical person. So having the communication skills, where you can convey all of this is volume well is extremely important because at the end of the day you know you'll be translating your data, which could be meaningless to someone because they cannot understand.
Speaker Change: <unk> gives you the opportunity to employ one of these techniques now I've never been an expert witness because I'm not an expert on anything but.
Speaker Change: Those tools I believe could be helpful.
Speaker Change: But at this point of time into understandable documents with a very good looking dashboards or again very nice looking at reports at the same time guys. Because again, you know I'm, having a good communication skills will ensure that you can convert a complex idea into something which is easily understood by a lot of people and this brings us to this.
Speaker Change: The microphone is back there. Thank you.
Speaker Change: Thank you. So much this is Dan so helpful and enjoyable. Good morning. Thank you would you. Please show the last screen. So we can get down the name of the book that you've written and the information absolutely.
I think they actually there you might even have an opportunity to them.
Speaker Change: On the sheet to everything I said is on the back of that cheap, but I'm happy to have this behind me, while I, while I talk.
Speaker Change: Skills, our Mexican skill set which is much very important and this is one among the important skills to have which is critical thinking skills.
Other question, Yes. Please yes, I work with groups that are from that represent many different cultural background. Yes. So are there any caveats or is this a universal strategy. So in terms of from your perspective is the speaker I believe.
Speaker Change: Because at the end of the day again, you'll be looking at thousands of numbers right. When you need to wear to look you need to know what the look what in numbers can do for you where the trends how can you get to those trends what are your process of benchmarking. How can you get to the goal that you set and so much more games and why he is all of this done well all of this was done to make sure that you can have a con.
Speaker Change: The supplies, but when you whenever you communicate part of the listening aspect is also thinking about is who is my audience and what are their expectations. So what are the cultural expectations of the audience that I'm presenting too so there might be certain norms and rules that are expected. So when I travel and do talks I have to take into account where.
Speaker Change: And to look up to rate so.
Speaker Change: To simplify it again to give you. An example, let's say you go shopping for all the food that you have to pretty much in all our book at your house, So you're going to need vegetable is youre going to need seasonings, you're going to need. So many things to quit that you will need oil for ambitious. So you will plan right. So most of us pretty much you know god once a month or twice a month to bring out all the groceries.
Speaker Change: I'm doing the presentation. So I'm I help present in the ignite program and if you have not heard about the ignite program in here at the GSP, It's fantastic and I just did a presentation standing in one of these awesome classrooms that have all these cameras and I just taught 35 people in Santiago Chile.
Speaker Change: And you know it started so if that's the given you need to understand how much of groceries you need every single month, how much you're consuming right. So you have to say the good thing you know what the amount of vegetables that I'm going to need for this month and then you got a plan of Daniel I'll pick it up weekly daily order, where you are or should you list. So how do you think that duration of when.
Speaker Change: And I needed to understand the cultural expectations of that area and what they expect and what they're willing to do when I ask them to participate so it's and part of that listening step where you reflect on what are the expectations of the audience. I think we have time for two more questions and then I'm going to hang around afterwards, if anybody has individual questions.
You should go up they go at it doubles that is exactly why you need to formulate conclusions to understand how you can go from nowhere to bad guys and then coming to the most important communication skills that you know again walk together would be critical thinking skills, which will basically enhance the output of you guys and then the most important thing.
Speaker Change: But some of these folks really want me to keep on schedule. Just I wanted to ask a question one of the things that you've done effectively and you're talking and I've seen other effect. The speakers do is interject humor in their talk how what are the risks and rewards of trying to do that well first thank you and I. Appreciate all of you laughing. Those are that's the sum total of all my jokes you've heard them I am not funny beyond those two.
Speaker Change: I want you guys to contemplate about it and think about as that data analysis isn't I gave because you'd be working with numbers, you'll be walking with grafts visualizations and everything can look its best its most beautiful if you know how to create that right. So quantum bit on that for a second Dave again, if you have a.
Speaker Change: Okay.
Speaker Change: So humor is wonderfully connecting.
Speaker Change: It's wonderfully connecting its a great tool for connection it is very very risky.
Speaker Change: Cultural reasons get in the way that sometimes what you think is funny isn't funny to other people. What research tells US is that if you're going to try to be funny self deprecating humor is your best bet. Okay. Because it is the least risky there is nothing worse than putting out a joke and having no response it actually sets you.
Speaker Change: Any questions about data unless again gave I want you guys to head to the comments section so pardon us as an open source Python library, which is useful minor bleeding one dimensional Lange <unk> <unk>.
And the name bond auto's derived from final data, but theres a common dome for multi dimensionally that's it.
Speaker Change: Encountered in status sticks and econometrics.
Back farther than if you would have gotten where you would have gotten if the joke would've hit so.
Speaker Change: Now, let's look at those types of Ddos structures and bond us. So we've got one dimensional.
Speaker Change: Basic fundamentals you need to think about with humor. One is it funny, how do I know I ask other people first second what happens if it doesn't work to have a backup plan right.
Speaker Change: I think I mentioned earlier. So if you are working on a one dimensional DSA is no. Another series object and the two of them. It still do you object is known as a bond Dusty Duffy and if youre working on higher than two dimensional data bundles would create buchbinder leader for Ya.
And then third if you're worried about the answers to those first two don't do it right.
Speaker Change: One last question. Please the microphone is right here and then like I said I will hang around afterwards, yes. Please.
Speaker Change: So thats properly understand what exactly is that Cds object.
We see these object and condos as a one dimensional label, which is capable of holding mix data types like in video string floating point number one no let's understand there wont be offering so what data for him as a two dimensional liebelt ddos structure with columns, which contain data of different types. So he obviously that we have.
I'm sort of on the opposite side, if there's since I am a journalist and I frequently have to ask spontaneous questions of people who have been through media training, yes. So.
Speaker Change: Uh huh.
Speaker Change: So any tips for chinks in the armor way to ask.
Ask the question without being antagonistic, but get it.
Speaker Change: I would do the install leader for them, where the first and the third columns are upstream type in the second column is new medical in nature. So other people understood. What exactly is bond does and they've also understood. There are different types of searches and find US let's go to the Jupiter and start working with bond us. So I'll start by importing the bond does deed of him.
Speaker Change: Facsimile of a straight answer.
Speaker Change: Well so let me give you two answers one is I have young boys and the power of the why is great. Just ask why a couple of times and you can get through that first two layers of training.
Speaker Change: Why do you say that well how do you feel about that the second bit is too.
Speaker Change: So I'll dive.
Speaker Change: What I have found successful in getting people to I do this to get people to answer in a more authentic way what I'll do is I'll ask them to give advice. So what advice would you give somebody who's challenged with this or what advice would you give to somebody in this situation and by asking for the advice. It changes the relationship. They have to me is the question Asker and.
Speaker Change: Embowed fund SBB, so the speed at which you see this is just the areas. So I'm importing bond does with the Helios B. So we have successfully employed this bond us ddos.
Speaker Change: I'll go ahead and create a series object from a list.
Speaker Change: So, let's see I will name the list as data and I really given these values. So the values inside these lists 123 and four in order to create a series object somewhat list. While you have to do is use this BD dart series function.
Speaker Change: I often get much more rich detailed information so the power of the why and then put them in a position of providing guidance and that can really work.
Speaker Change: With that I'm going to thank you very much I welcome you to ask questions later and enjoy the rest of your reunion weekend. Thank you.
Speaker Change: Speed at Cvs and inside this I will pass in this list.
Speaker Change: I will store to listen.
Speaker Change: One no.
Now let me bring this out.
Yeah.
Speaker Change: Yes.
Speaker Change: Right. So we have successfully created a cvs object from this list. So we've got these four number than these other index values. So what you see or hear that the indexing start from zero. So 012 and three this is mixed.
Speaker Change: Okay.
Speaker Change: I think we're actually live this time droop, Andrew the founder of English any one dot com and welcome to another live video here on Youtube and let's see if we get this working should be coming through clearly alright, and making sure we're okay.
Speaker Change: Mixed values and these are the actual values now, let's see how can we change the index of this series object. So I'll just copy this over here for us.
Speaker Change: It all here again, so we have got we indexed at reviewed over here.
Speaker Change: Beside this whereas the words I will given different set of values now, let's say I Wonder index values to B E.
Speaker Change: So I'll give people a moment to come in we should be live and working right now, but the reason I wanted to make this video because I get really one question actually I get a lot of questions, but I get one particular question over and over and over again from students and this is how can I actually speak fluently about many different.
Speaker Change: We see.
Speaker Change: So I was just passing these values for the index by over here and that's the way it back to S. One now, but let me ask one over here.
Speaker Change: So we have successfully created a Cds object right the index value that a b C and D. Now if you want we can actually extract beef individual elements with these index values. So, let's say I wanted to extract the value which is present at the <unk>.
Speaker Change: Kinds of things will huddle.
Speaker Change: He put it.
Yeah.
Speaker Change: So a lot of people again, they can understand a lot of English, but they have trouble expressing themselves and especially if the topic is new or unfamiliar or something.
Speaker Change: Index values.
Speaker Change: S. One filed burden that emphasis and inside this burden.
Speaker Change: Then they have trouble being able to speak so I wanted to talk about this because in a video I did a couple of I think we'll do that at last week I did on Instagram I spoke with a few learners.
Speaker Change: Great. So I have successfully extracted this value.
Speaker Change: Let's say if I wanted to extract the element, which is present that index.
Speaker Change: So as hyphen over here right now lets say if I were to extract or first two elements. So if I were to extract the first two elements I'll just put in a colon over here.
And one of them in particular was talking about how she is reading lots of books and taking time to improve or English, but she doesn't feel like she is actually becoming more fluid and so fluency again just to make this very clear for people. This is how well you can communicate it's not how much you know and we know this.
Alright, so I would expect that the first two elements. Similarly, if I wanted to extract the last two elements I'll put in colon and order here on the left side of the colon I'll put in minus two so this is how we can extract the last two elements from the Cvs object.
Speaker Change: Because often little children 456 year old Kids Native English speaking kids can communicate better than many adult English learners and so that's the the goal is not to just try to learn more and more and spend more time learning what you really should be doing is actually focusing on vocabulary. Because this is how.
<unk> created a series of object out of a list no. Let's go ahead and create a Cds object or a prediction.
Speaker Change: Just a quick info guys best of your knowledge of data analytics by answering this question, which of the following is a false statement about data analytics E. It collects data be it looks for patterns see it does not organize data D. It analyzes data come in doing so and they're gonna infection, but ill.
Speaker Change: How people get fluid its how you got fluent in your native language Italian develop mastery in anything.
Speaker Change: So I had a couple of notes actually there are quite a few things I wanted to talk about.
Speaker Change: [laughter].
Speaker Change: Subscribe to Intel, but the right answer now that's going to do with the session. So let me see here at Mitek studio here I mean this is the one I'll put in golar pieces over here.
Speaker Change: So we got let's see here.
Speaker Change: Hopefully mute issue desert.
Speaker Change: Hopefully the hopefully the the volume as loud enough to let's see welcomed from Germany nice to see everybody I want to be quick go through a couple of different examples about this just to make the point about how you can actually speak fluently about almost anything.
Speaker Change: No it might wiktionary, so I'll just start off by ascending with key value pairs. So eight one.
Speaker Change: B.
Speaker Change: Due.
But also just to let you know this is not like an instantaneous process.
Speaker Change: For that for C fee earned off without for D furnace for South.
Speaker Change: But it really actually does happen quite quickly if you start doing the right thing. So that's what we'll talk about in this video.
Speaker Change: <unk> successfully created Mike Wiktionary over here, but let me also bring this out.
Speaker Change: So I wanted to talk about this again I mentioned the learner at the beginning but also just because I really want to show how natives or learning and remind you about how you learned your native language. So I had my.
Speaker Change: Key value pairs.
Speaker Change: <unk> are going to create a series of object out of this I will die BD, Dod Cds and inside this I will pass in the dictionary, we chose the one.
Speaker Change: And I will store this is stu.
Speaker Change: Actually before I do that let me give you a quick a quick example of something.
Speaker Change: Let me point out us too.
Speaker Change: So this is my series object over here right. So all of the keys has been assigned as the index and all of the values are the actual values in this series object right. So <unk> becomes the index value over here B is the index C is the index <unk> index and the value corresponding to this.
Speaker Change: Just to kind of contrast, these two ways of learning. So the typical will just call. This the <unk>.
Speaker Change: The ESL approach.
Speaker Change: So this is English as a second language and then we have the ear bell approach over here, which is learning English as a first language and what most people do is they spent a lot of time trying to learn as much vocabulary as possible. So imagine each one of these is a new phrase. So if you spend today trying to learn 10, new phrases or 10, new.
Speaker Change: Key becomes a value in this series object as well no, let's say if I want to re sequence the index values over here, let's see how can we do it. So I will copy this and I will be used at a wall here now again I will use the index Pat and inside this let's say.
Speaker Change: Words or whatever.
Speaker Change: E F. L approach really you're trying to focus on one thing and learn it very deeply.
Speaker Change: Hey, this is basically the simplest contrast between these things so children. They will often be learning something like a child will watch the same movie 100 times. So there. They are just naturally getting lots and lots of review while most English learners are trying to just and again.
Speaker Change: <unk> I wanted to sequence to meet BCB. So that is what I will give over here B C.
Speaker Change: B.
Speaker Change: Okay.
Speaker Change: Now let me add on this and let me just point out as two over here right. So we have reversed the sequence of these endeavors. So initially it was a b C and b, how big was it in nowadays BCB.
Speaker Change: This is it's not really the fault of learners part of it in your brain just trying to get you to learn new things and part of it is teachers that are really not spending enough time focusing on things.
So this is how we can create a series of object order for data for him and also teams the sequence Obeef Emesis correct. So we have worked with Cds now, let's go ahead and see how to create a ddos dream or a full list.
And so they will learn something new and they will learn another new thing and then they will forget other things.
Speaker Change: That they thought they knew.
Speaker Change: Before I talk about exactly what you should be doing and give you. Some examples of this I thought it would be interesting to just use a brick example to get you all thinking about something very simple like a break so just imagine I have a break right here I want you to write down and the comments right now just think of.
Speaker Change: <unk>.
So I'll just try to come in over here.
Creating.
Data for <unk> from a list right. So let me again go ahead and create my list and data.
Speaker Change: Equally there's 123 and four.
Speaker Change: Alright, So I have created my list now I'd have to go ahead and clear the data.
Speaker Change: How many different ways you can use a break alright, so very simple object you could do the same thing with a marker or an eraser or something like that but how many different things could you do with a break.
Speaker Change: To create the data for him and distributors index, so as diamond Bebe Dot data.
Speaker Change: So what are your do you have to keep in mind that D is capital and this capital great.
Right.
Speaker Change: And inside this I'll just pass on this list.
Speaker Change: This will help you get thinking more like a native speaker about vocabulary because what we want to do is get your focusing on something rather than trying to we look at one way of brick could be used and then we go to some other object.
Speaker Change: And I will steward of this thing.
Speaker Change: And B if no other group in this out.
Speaker Change: Great. So we have successfully created a tiered offering are of this list.
Speaker Change: Let's also go ahead and create a data for the amount of a big state.
Speaker Change: Right.
Speaker Change: So Aoc just popping in looking at keep up the good work here we go.
Speaker Change: So typing.
Speaker Change: Food or here so the name of the dictionary this fruit.
Speaker Change: So yes, everyone take your take your time and and give me just any kind of use you can put it right in the channel how could you use a break alright doesn't think try to try to be a little bit of creative you can think about this as a ah creativity.
Speaker Change: And a word here I will write the key to be equal to fruits and the values for this.
Speaker Change: So we have got all of these foods with guard Apple they've got mango.
Speaker Change: Let's say you could build something okay.
Speaker Change: After that we have got banana and finally, they have good grower.
So even if we have only one brick I mean, obviously, if we have many bricks, we could probably build a wall, but yeah. We could build something we can make something that's true it could be the like the first piece.
Speaker Change: So this was the first evaluable offer that I was given the second key value pairs, which would BV go into other fruits, so I'll dive in cone.
Of a wall, but we could.
And well here I am just selling the counter so lets say that are then apples when be mangoes 40 bananas into legal hours.
Speaker Change: Throw it for sure.
Speaker Change: Yes.
Speaker Change: You could use it as a weapon if you wanted to throw at us something yet it could be a bridge, maybe like even a bridge a small bridge for a mouse or something like that so if we put I don't know something under here then we have our brick like that it could be a little bridge over some some water something like that.
Speaker Change: Alright, so we have created a dictionary.
Speaker Change: Let me bring this out now let me also go ahead dedicated data for any more of this so you already know right after diamond BD, Dod Ddos cream and inside this <unk>, Boston fruit and Iris toward this and let's say food underscored the earth.
Speaker Change: So we could probably think of I don't know 30 40 different uses as a supporter shirt. So you could put something on top of it you could sit on it that's right you could use it as a chair very good.
Speaker Change: Now let me bring this out crude underscore deal.
Speaker Change: So we could also so again, we could put things on top of it or we could put the brake on other things like using it as a paperweight paperweight. So things may be if it's a windy day, we put the brake on paper to stop it from blowing away I don't know, maybe we throw in some water to make a loud splash just to hear the sound of that.
Speaker Change: Great.
Speaker Change: What happened to hit US these duties have dawned into this column names. So fruit has become the column Nemo adhere Golan has become the korlym name over here and these values come into videos right. So foods, we have a word here and Apple mango banana and guava, although values again count becomes.
Speaker Change: Alright, I don't want to spend too much time in the video going over all the different ways you could use a break but the point is if you think a little bit about it and you spend just a little bit of time thinking you can probably think of some pretty creative uses for brick. Okay. And then we also have maybe there we could have different.
Speaker Change: Column Naeem and these values over here come over here right. So this is how we can create a data frame our our predictions.
Speaker Change: Alright, so now understood. The basics of CD, then that data for him. Let's go ahead and see how important data for Humira and do some sort of monetization on top of it. So I have discussed them with your own data for him with me. Let me go ahead and imported sort of important data offering.
Speaker Change: The bricks he could have like a lego brick where you're you got your little pieces in there you're connecting into something or it could be maybe made out of plastic or something a different kind of brick or it could be a different color, maybe just use it as a decoration or something alright, so why am I, bringing up. This example.
Speaker Change: BD dark Reed CSV.
Speaker Change: No inside this I was given the name of the file so the name of the filers.
Speaker Change: <unk>.
Speaker Change: I will store it isn't a new object and named it object to be customer churn.
Speaker Change: Point of this is really just to show you. The difference between what learners are doing so what teachers are usually doing in classes and what natives are doing alright, so I was walking.
Speaker Change: No, but let me go ahead and bring their first fight columns of it.
Speaker Change: Walking my older daughter ARIA to school today.
Speaker Change: So it will be customer churn Dod head.
Speaker Change: Alright. So this is our customer churn data offering and it comprises of all of these columns. So you've got customer IV gender senior citizen partner and so on.
Speaker Change: And.
Speaker Change: While we were walking she said dad, what does harsh me harsh.
Speaker Change: The word harsh.
Alright.
Speaker Change: So this is a special function and bond us, which gives you. The first five boroughs right. So 12345 or some early let's say if you were to have gone to the first 10 rows, we just given the value than over here and you can glance at the first 10 gross or this customer churn data for him so anlage function to head.
Speaker Change: Do you think pay attention at native English Mills can help you with pronunciation, yes. If you obviously, if youre watching what people do but listening is more important.
Speaker Change: And it's better to listen to like 10 different people speaking than to try to watch one person's mouth.
Speaker Change: If that makes sense.
Speaker Change: So Arya my older daughter ask what does harsh mean.
Speaker Change: Bel function, which will give you the last few rows.
Speaker Change: And I said, Oh, where did you hear that and she's Oh I was watching a show boss baby, maybe you've heard of this show Bask Bank.
Speaker Change: <unk> paid over here.
Speaker Change: No I think on run so this over here gives you the last five years from this customer which are indeed up.
Speaker Change: So there are actually a lot of complicated words and expressions, it's really a show for adults, but because it's a cartoon kids like to watch it.
Speaker Change: Similarly, if you were to have gone through the last 10 gross you will just bring the value.
Speaker Change: Alright. So these are the last 10 draws present in the customer churn data offering right now, let's see how can we extract a specific rows and columns stroke bond us Ddos.
But she's learning lots of interesting vocabulary and so one of the words that she learned was harsh.
Speaker Change: So she's asked whats harshly and so I said, Okay, where did you hear that youre watching boss baby, what's happening in the scene or in the show when you hear something or someone you use the word harsh.
Speaker Change: So for this we've got block.
Speaker Change: <unk> functions, so, let's actually start working with the <unk> function.
Speaker Change: So, let's say I want to extract only that rose from <unk> five to <unk> 15, and <unk> columns from column number two to call him to move forward, let's see how can we do it right. So I'll start off by giving the name of the Ddos and we discussed them with you and after that I will use the function I look and I'll.
Speaker Change: So I think.
Speaker Change: One character and neither babies or whatever I didn't I didn't see the show which is listening to her talk about it so one character.
It's kind of like yelling at yelling at this other cared maybe we'll give them a leg of angry fix.
Speaker Change: <unk> come up with here right. So whatever it is presented on the left side of the power that Dino talk that rose in order to present on the lighter of the color that below sort of the columns. So let's say if I were to extract that rose from five to 15.
Speaker Change: And the other characters says like don't be.
Speaker Change: B so harsh.
Speaker Change: So this is one example of this so don't be so harsh so what's happening here is okay. If we imagine the scene. We can think about one character as being angry at another character and Theyre, saying some some harsh words.
Speaker Change: So I'll put in five I'll put in a colon and opened 15, alright, so I'll be extracting all of the rules starting from doing them. If I threw a number 15 excuse.
Speaker Change: Similarly, if I want all of the column starting from Carlo number two column number fight this is Howard NGO.
Speaker Change: Some harsh words to the other character.
Speaker Change: And you can start to understand what harsh means or kind of one. One example of what harsh means but after she she kind of understood. This it's like okay. It's maybe kind of something maybe a little bit mean or something difficult.
Speaker Change: So, let's actually have gone to this so this due to fight a word here. So since we already know that in Python indexing starts from zero to 012. So this would be a column number two would just senior citizen. So two three and four senior citizen partner independents right. So we have extra.
Speaker Change: Or something hard hard to hard to deal with something hard to accept something that's harsh like that.
Speaker Change: But I didn't stop here as were walking to school I start, giving her more examples of harsh.
Speaker Change: Great column number to call and number three and column number food and we have extracted the ROE starting from <unk> due to a number 14 right. So this is how we can extract specific gross and columns somebody no. Let's go ahead and see how can we performed some sort of data might ablation. So let's say from this entire data frame I want only.
And so you might have we'll just put some more examples up here obviously this.
Speaker Change: So we might have some harsh.
Some harsh weather now if you think about it okay. We understand this meaning of harsh than if we had harsh weather do you think that's like good pleasant weather or maybe not good pleasant weather.
Speaker Change: Those records were.
Speaker Change: The agenda of the customer is female.
So what.
What we'll do is I'll just start off by typing the name of video of him, but discussed them with Joan.
Speaker Change: And a child can imagine Oh, wow that looks like there will be some harsh harsh weather today theres a snow storm in lots of wind. So that's what we mean by harsh weather harsh weather alright, So you might have a harsh store.
Speaker Change: And then inside up here and this is I will given the name of the column, which is agenda.
Speaker Change: After that I'll using double equal dual but he did and then given the condition, which is the agenda of the customer needs to be equal to female right no bulk con run and let's see where do we get so we got a bunch of true and false levels note. There's a bunch of drew and falsely was basically means Doug wood here Gordon.
Speaker Change: Or are we could have a harsh season.
Speaker Change: Again, we're still talking about similar kinds of things here.
Speaker Change: But again the point is that we don't stop with just one example of something we really want to make sure that you can understand that vocabulary very well so when I'm teaching my own kids, we spend time with the vocabulary yet so bad weather again harsh weather the same kind of thing and you understand it from the situation from the context ran.
Speaker Change: Number zero it is actually to notice the agenda of the customer is female. So far also adhere indicators agenda of the customer is not female here again, it as far as you're looking to dispose. So again at a record number for the rest of gender or the customer is female noteworthy Lewis I will guard. This.
Other than trying to get a definition or a translation of the word. So this is why I asked my daughter like when did you hear that what situation what's happening to those characters when they say that because I want her to be thinking about that when she's learning new things again, it's going to be much easier for I understand new vocabulary. This way so we got harsh weather.
Speaker Change: And I will be stood back inside this right. So what is happening or what he orders from this customer churn the downstream I will extract only those regards where this condition is true right and I will store to this and let's.
Let's see female customer.
Speaker Change: Other harsh storm harsh conditions.
Speaker Change: Alright, I've gone run now let me point out the head of this FEMA.
Speaker Change: All of these things it means something that's difficult or it's going to be bad and again, we already have the example of harsh words someone might use some harsh language, maybe they're cursing if someone they're angry at someone does this makes sense is it is it starting to be a little bit more clear what harsh means.
Speaker Change: Female underscore customer Dod head.
Speaker Change: Alright, so we have successfully extracted a subset from the original data frame, where the agenda of the customer is only female similarly.
Speaker Change: You've probably heard this word before but have you heard all of these different uses of it.
Speaker Change: Similarly, let's see if we want to do some sort of complicated operation than this so.
Speaker Change: Maybe maybe not.
Speaker Change: They go to extract on lead those records were tenure of the customer is greater than 50, and the Internet service of the customer is equal to fiber optic alright, so let's start off with the first condition. So I'll give you the name of a deal for him, we just discussed and which one and inside of the pit emphasis I will given the name of the kilometers venue.
Speaker Change: But this is usually what native they're doing so they don't learn like I'm actually very efficient about how I teach my kids because I understand how they should be learning, but usually what children do is they will here. Maybe one example of something and then it could be weeks or months later, they hear another one and they make that connection so I.
Speaker Change: So the tenure of the customer needs to be greater than 50.
Speaker Change: Want to help my kids make that connection as fast as possible like we get out here's something harsh here's another thing. That's harsh. These are all different harsh examples so they understand very well what it means okay.
Speaker Change: Alright, so I look at this and but at the same site basis for the <unk>.
Speaker Change: And operator, and given the second condition, so the second conditioners.
Speaker Change: Bennett service of the customer needs to be equal DSO.
So this is just one example, but I wanted to give some more especially kind of higher level things, but remember just because the bulk of the vocabulary. It looks simple you might have a word like harsh and it seems like a short simple word.
Speaker Change: So I'll dive in Internet service and this needs to be equal do D. S.
Speaker Change: Alright, again I'll put this condition inside.
Speaker Change: So we're here.
Speaker Change: Alright, So we've got these two conditions and finally.
Speaker Change: Okay, that's not an advanced word, but again in a real conversation would you be able to use harsh in these different ways with that vocabulary come to your mind automatically if you learn this way. It does so if you could think wow like harsh.
Speaker Change: I'll put those two conditions inside of it. So what is happening is from this and Dod customer churn beat offering we'll be extracting on lead those regards to add these two conditions are satisfied.
Speaker Change: And I will store this and lets see see underscore tenure underscore internet now.
Speaker Change: And now because I have this word in because I know it so well have gone deep into the vocabulary, we're going to focus on something rather than trying to learn a bunch of different things. So remember their traditional ESL approach, we have a lesson I try to teach you 10, or 20 words or something but we're not going to go very deeply worse.
Speaker Change: Let me point out ahead of this so it will be.
Speaker Change: C underscore Danielle underscore and connect.
Speaker Change: Alright, So let me have gone to the tenure or hear it. If you see the tenure of the customer is greater than 50 foot. All of these values. So 62, 50, 870 270, and so on now similarly, if I have a glance at the Internet's always column, then youll see that all of these values are DSL right. So this is how we can perform data.
Speaker Change: Spend much time on them and then next week, there's no review of any of that vocabulary. So you're learning a few things.
Speaker Change: And then you don't spend time reviewing them, but with the EFL approach, we really want to take something and go deep into that vocabulary. So we learn the word harsh.
Speaker Change: <unk> operations are in double defined nasty alright.
Speaker Change: And then we want to hear lots of different examples of how the word might be used and it's all of these examples that really make a fluent in that vocabulary alright now here's the amazing part about it now that you do this with one word you can use this word when you're talking about other things like don't Judge me so harshly.
Speaker Change: Alright, so we have done in the practical so exploratory data analysis or ETF, which one it is a.
Speaker Change: Process performing initial investigations on later go after this talk buttons abnormalities anomaly no assumption with the help of summary statistics and graphical presentation basically when we have some data on basically one on data centers statistical analysis and machine learning modeling because you have to make sure that we understand what the data.
Speaker Change: So people are being critical they are criticizing you hey, like this is a bad presentation or something like Wow wall don't don't be so harsh. Please give me some kind of helpful feedback. Okay. So again, it's another example of like harsh feedback harsh words harsh language and this is just one.
Speaker Change: I represent shape up the data what are the different kinds of things that are available in our data and data thats available in that data.
We can visualize that data and understand the relationships between individuals column setting dividend features allow data that can be good and all of that and more.
Speaker Change: An example.
Speaker Change: Okay. So what I'm teaching my Kid. This is what I'm trying to do I don't want to give them a definition of the word I want to help them understand really what it means by covering lots of different things does this makes sense. Let me know when the comment that this is like yes. So rough another idea and these all kind of similar because often words don't have maybe one definition.
Speaker Change: Visualization and all that's done.
Speaker Change: Called exploratory data analysis, that's what <unk> all about Bell media allows us to get a better understanding of our data and make important observations on it.
Speaker Change: In order to understand it.
Speaker Change: I understand with an example.
Speaker Change: Or the definition might be broad so it could cover many different things, but in this way my daughter, Oreo now understands okay harsh means something difficult well that test was really harsh weather that was a difficult a difficult thing okay.
Speaker Change: Thank you have a data set which will have no understanding you don't understand what the data at <unk>, Inc.
Speaker Change: The columns represent.
Speaker Change: How do the columns relate to each other and which columns are important for the particular.
Speaker Change: You are trying to solve it looks like but it is very difficult to understand how you are going to solve the problem of Honeywell.
Speaker Change: So the point here is.
Speaker Change: If you want to be able to speak fluently about almost anything.
Speaker Change: How are you going to.
Speaker Change: One statistical analysis, and so on and so forth Linda section. What happens is we would like to perform explosive data analysis in order to get a better understanding of our data also helps us understand whether or not the data that we need.
Speaker Change: The you actually should do the opposite of what most people think so the English as a second language approach says if we try to learn all of these different vocabulary words, let's say, we have like a thousand thousand word vocabulary.
And if we if we try to learn a whole bunch of words or try to learn more worse than that then we can talk about anything but what happens is they don't actually know very well the vocabulary and so they actually can't have conversations about anything they can have a very limited conversation about some things but the.
Speaker Change: Well thought of.
Speaker Change: Boost so it has some data thats highly biased on one day.
Speaker Change: For example, we are trying to predict whether or not a particular.
Speaker Change: Particular in.
Ton ship is going to convert in two adult that's where we have a data set that we wanted to figure out whether or not we can create.
Speaker Change: The EFL approach the English as a first language approach what we're doing is really trying to help you understand something very well and then you can move on to the next thing. So once you feel confident about this then you move on to the next word or phrase or whatever.
Speaker Change: The model in which we can <unk> some data about an interim and figure out whether or not we should give them a job. Although it's a very specific use case, if all we have.
Speaker Change: <unk> did not get converted into full time employees are data second completely by it and this is something that we could make it.
Speaker Change: But because you know this so well now you can use it when talking about everything you can use it when talking about relationships, we're going to the doctor or whatever you know know that vocabulary and you can use it when you are talking about almost anything okay. So the point is not to know every word the point is to know like the vocabulary that you have.
Bank analyze that data doesn't exclude UK data analysis, though.
Speaker Change: Then they would have to get more data so that we can balance out.
Ability and then teach our model about the Haynesville it has better understanding of what it does to me.
Speaker Change: Very well and the better you know it. This is how you do it so youre getting lots more examples you're really trying to understand something very well hopefully this is making sense. Let me know when the in the comments if everybody is getting this before I move on to some additional vocabulary for this.
Speaker Change: So now we come to Y E D Y what do you want to perform exploding data analysis.
Speaker Change: Watergate, but why exactly what they want to do it but external kidney dialysis.
Speaker Change: Most crucial steps in data center.
Speaker Change: It allows us to achieve certain input and statistical measures.
Speaker Change: Alright, yes, it could have some harsh parents that's true.
Speaker Change: Let's see alfonzo, there so I just want to know if it is possible.
Speaker Change: Essential for their data center.
Speaker Change: Understanding of the clinical data analysis process allows us to make some important observation and early decision that could help us.
Speaker Change: Oh, who is fluent in English.
Speaker Change: To be able to express one's thought on all topics.
But do you see how this works so now that I know the word harsh I understand how I can apply it in different topics.
Deals.
Speaker Change: That could help us.
Speaker Change: Got it.
Speaker Change: So as we discussed in the previous example.
Okay. So if I just learned the word harsh in that definition, maybe I'm only thinking about harsh weather.
Non performing.
Modeling.
Speaker Change: Multiple algorithms.
Speaker Change: Okay.
Speaker Change: Alright, but actually harsh doesn't it doesn't only mean web that's not harsh doesn't mean that it's meaning like as you look at all these different examples you can understand that what that means.
Speaker Change: So each model is not going to get.
Speaker Change: Mainly because the data set we have and if you don't have those.
Speaker Change: Those headwinds.
At present.
Speaker Change: The reality in the current quarter than your model is not going to be good.
Speaker Change: Fifth.
Speaker Change: None of it underperformed dataset, so aesthetically data analytics helps us perform fewer steps as possible in order to get the best at it.
Speaker Change: Yeah, Doug I hope better yeah, my neck out on that.
Speaker Change: But I look at it on that you'll get there, yes use your English, though use your English.
Speaker Change: Despite experiencing an abundance of.
Speaker Change: Of course.
Process.
Speaker Change: That is one of them.
Speaker Change: Certain steps.
Speaker Change: [laughter] Carrizo Don I.
Speaker Change: Fine and refined our important feature and may enable silicon. So it could be that could have a data set that in eight different column, but.
Speaker Change: I was just watching that this morning.
Speaker Change: Yes.
Speaker Change: That's from a Japanese TV show called on the Cooper.
If I'm talking about the same if we're talking about at the same thing.
Or what you're trying to predict only three or four columns are going to be enough.
Speaker Change: But yes, it is kind of a harsh name over there alright.
Speaker Change: Most of the or most of the variation in the data.
Speaker Change: [laughter] alright, hopefully this makes sense, though.
Speaker Change: In the call them, the preclinical predict well you on anything or they gave us.
Speaker Change: All of those scenarios.
Speaker Change: So the idea again is to focus on something to understand it very well and that's how you can use it to talk about lots of different topics. So you can use this vocabulary and other things Alright, Let me give you two more examples of something so this is just a single word but I thought I would also give you.
Speaker Change: The coordinated out of the pipe.
Speaker Change: So that if the data data analytics is important.
Speaker Change: Us too.
Speaker Change: I was going to be useful for us in case, we don't use the correct Peter.
But the model that's highly loaded contains data that 11 acquired use us to collect data that's at 11 am.
Speaker Change: Two other ones.
Speaker Change: I know learners love phrasal verbs. So we will talk about that very quickly.
Speaker Change: It could lead to incorrect or highly improbable.
Speaker Change: Phrasal verbs or things that young kids learn when they're trying to understand more complicated things than just a single verb Lake stand we might have stand up or he might have sit down or sit up.
Speaker Change: To be very careful and ink water column of columns on this we're trying to model out data automate the permits now why would you want to perform exploratory data analysis with bi.
Speaker Change: There are many other languages out there.
Speaker Change: So again, we're not just we're not just looking at six because we have said we might have sit down.
Speaker Change: Loading.
You can even performance.
Speaker Change: In Javascript, and Java, and C plus plus so why fight the explorer.
Speaker Change: And we also have set up.
Speaker Change: So sitting down just means a sit down in a chair, but sit up means actually to have correct posture, when you're sitting alright to sit up.
Speaker Change: Exploratory data analytics using Python.
First of all by.
Speaker Change: Probably one of the you got languages to get started.
But let's just give another example of bring up.
Speaker Change: This is led by Denis so popular in data science community because many people on data science operation All machine learning operations. All in particular data set of people who come from the academic world. They don't have much understanding of what programs or what variables hour plus with us.
Speaker Change: Now usually when kids are learning these things for the first time, they will hear somebody using a physical visual example of something so if I say, Oh I might bring up like if I bring up.
Speaker Change: <unk> Court.
Speaker Change: Mark I'm going to raise something up so if I'm maybe walking from.
Wanted to get done.
Speaker Change: <unk> comes into play.
Speaker Change: The first floor in my house, we're going to draw a house here, you've got two floors in it and there are some stairs. So if I walked from here up to the second floor and I'm carrying something with me I'm, bringing up a like a thing I might bring up a marker from the first floor alright to bring something up alright.
Speaker Change: Yes.
Speaker Change: So easy I think.
Speaker Change: Another advantage of Bison play is that it has a number of liability.
First of all.
Speaker Change: Clinical data analysis.
Speaker Change: In games, we are performing the stuff ourselves, it's very very important product to make sure that the loading ECA using acoustic annual spend multiple people because it's likely that someone has created a confidence in it.
Speaker Change: So we're getting there we're getting there.
Speaker Change: Very good.
Speaker Change: But again, it's the same idea we'd begin with something physical that you can see and this is how kids are able to understand the vocabulary without using translations alright, so were going to physically bring something so pull it from one place to another to bring it up.
Speaker Change: Davita needle.
Speaker Change: For example, if it's allowed us to perform dwt location, but it's.
Speaker Change: Not a lot of people have used.
Cannot be sure that the diabetes.
Speaker Change: Yes.
Speaker Change: Mr transitions.
Bring it up alright.
Speaker Change: All of this is going to be a bit problematic and python, although their logos that have been used by millions of developers and academic people.
Speaker Change: So we might have another thing so we've got another example.
Speaker Change: If we imagine a conversation to people are talking about something here's one person and another person talking and the first one says Oh like I had an idea about something.
But they do.
Speaker Change: Understand the basic and then importantly on the path that they need to perform.
Speaker Change: And that's really important.
Speaker Change: And they're just introducing that in the conversation so that person might bring up.
Speaker Change: That's it.
Speaker Change: Bye bye.
Speaker Change: Such a good candidate, but it is limited to that in a little bit mainly because it's easy to understand easy to Lee with TSMC as well because it's got so much lumpiness.
Speaker Change: Bring up a topic, so again youre kind of imagining if it's like inside your head and you bring it up to the to the topic bring it up to the conversation. It's a similar idea of carrying something from one lower level to a higher level. So if you bring up a conversation Oh my friend just brought that up.
Speaker Change: Around Dakota, kidney dialysis and data center.
Speaker Change: Uh huh.
Premiums for developers or people, who are getting started with.
Speaker Change: Person data analysis or data side.
Speaker Change: In the conversation. So we are talking about something in my friend brought up I don't know if some idea about that what we kind of mean as they're bringing something maybe from their mind or from the inside of their head and now they're making it obvious.
Or even professional <unk>.
<unk> have liabilities to perform all kinds of thoughts relating to its productivity.
Speaker Change: Okay.
Speaker Change: Nations analysis.
Speaker Change: A lot of data available.
Speaker Change: Available not just one luxury but multiple.
Speaker Change: Okay.
Speaker Change: Turning on the features that you are going to go through can import either one of the lessons that out of it.
Speaker Change: Bring up your date, yet and so another example, another very common what I'd like to bring up a child to bring up a child.
Speaker Change: The Lebanese Bottega Veneta.
Speaker Change: The medical manipulation.
For many other different kinds of stuff.
Speaker Change: So if you bring up a child same kind of idea on August it's neither although went away maybe it'll come back.
Speaker Change: Neural networks dependent stepped it up in your neck of.
Visualizing.
Speaker Change: So.
Speaker Change: Bye bye.
Speaker Change: So if we bring up a child, we can think about that child physically getting bigger over time. So they are bringing we are bringing them up we're trying to raise them up trying to help them grow to bring up a child alright. So all of these examples the ESL approach would be okay, we're going to break up.
Speaker Change: So some of the most popular lebanon's to perform a number of tenders in Atlanta now.
Speaker Change: Linda.
Speaker Change: And you'll have no understanding of what the slogan of the bank.
Speaker Change: Well, what we do in a later point in the presentation also again, if you wish to learn more about these labs open motorboat hsn's any clinical data analysis.
Speaker Change: And then maybe they would learn one example of something.
Speaker Change: But the EFL approach is really to help you understand the vocabulary very well alright does that makes sense. So the thing thats really stopping people from communicating it's not their vocabulary.
Speaker Change: Despite and in general stick with us till the end of the presentation and will help you.
Speaker Change: Thank you.
Speaker Change: Some of the resources that can help filter.
Speaker Change: Let's take a look at some of the advantages go to figure it out on the horizon.
Speaker Change: Again, you might have like a child it knows 100 words.
Speaker Change: So it provides us with several benefits.
Speaker Change: It allows.
Speaker Change: And in adult that knows 1000 words, but the better communicator will be the one who knows that vocabulary reasonably well.
Speaker Change: Allows us to spot missing data.
Speaker Change: Okay data analysis, we can easily find none.
So me speaking Japanese in the same way like my my Japanese vocabulary is I don't know how many words I know in Japanese I know I actually I can't even count how many words that would be but I have no trouble communicating in different situations about different topics.
Speaker Change: Making values.
Speaker Change: Yes, we have a data set in with certain columns have non value a certain quality a certain proportion of the data.
Speaker Change: He can remotely.
Speaker Change: Tilda, Linda although it moving the needle feeling part.
Part of it sort of ticking down a little.
Speaker Change: Look I think that that definitely a part of it.
Speaker Change: Okay.
Speaker Change: Data analysis and it allows us to do that it allows us to find the underlying structured data so on the surface, but we can see the number of columns columns.
Speaker Change: So confidence is a key factor, yes confidence is key but confidence comes from your understanding of vocabulary.
Speaker Change: So you don't you don't just magically become confident for no reason you'd become confident because you think oh, okay. Now I get it now I really understand what's happening I really understand the vocabulary.
Speaker Change: On the data that we have in them.
Speaker Change: Underneath the row and columns and establish a structure of our data.
Speaker Change: What we can do it.
Speaker Change: Dan.
Speaker Change: I'll go back and take a look at a few comments and then I'll give a final example here.
And then what the underlying structure.
Speaker Change: Underlying structure means what other defense data.
Speaker Change: Uh huh.
Sure.
Speaker Change: Particular column.
Speaker Change: Let's see alright, Chris says I like your way of teaching from India and glad to hear it.
Either in the current format to be processed.
Speaker Change: <unk> of data that is contained in a particular column do column, the generic or coordinate with each other in the data and if so is it helpful or how or the task.
Speaker Change: Why native people use this like going doing it just to be faster so theyre speaking.
Speaker Change: Faster English Shihan says where are you from them from the United States.
Paul.
Speaker Change: No.
Speaker Change: That makes sense means I heard and lots of movement, yet so that makes sense.
Speaker Change: Then the variable importance as clarity data analysis helps us to figure out which variables in August at the most important.
Speaker Change: So in that case.
Speaker Change: Help us understand which many of us have the highest influence on the things that we're trying to predict on the column.
Speaker Change: To make sense means to be understood. So if I say O M. I am I being understood do you understand what I'm, saying I can say does that make sense. So in my in my talking something are bringing up a ball.
Speaker Change: That's it.
Speaker Change: The drop the rest of the columns and only the ones that have influence on our column above a particular threshold and finally data visualization now we can sit and talk about a lot, but one of the issues that occurred as the data analysis is that when we show the statistical measurement salespeople.
Speaker Change: So it usually like if youre like playing basketball or something it's again, bringing something like forward down the court to bring something up alright, but again. The point is that you are spending more time with fewer words. So its like Youre learning less okay. I know people are they really want to learn more and like yours out or have a mass.
Speaker Change: Plus especially people who are not statistically electric.
Speaker Change: It's going to be really difficult for them to understand mainly because they have to firstly scan through the data and understand which data has higher value, which stood at a lower value and then understand the implications of each of them.
Speaker Change: The vocabulary, but you don't get there for speaking unless you actually spend time with the vocabulary, but it's new for your mind as you're learning. These different examples just like we talked about with harsh alright, so I'm going to teach a new phrase maybe some people know this already maybe some people do not.
Speaker Change: Much easier.
Speaker Change: I didn't understand the characteristics of the data.
Speaker Change: If scenario aesthetically data analysis Neely.
Speaker Change: It allows us to perform visualization and explain our data in a better capacity even for people who may not meet that.
Speaker Change: But this is the same way I would explain it to my own kids and again. This is important really want to help people understand it the same way natives do alright, Saar. It says bring up something that happened in the past, yes exactly.
Speaker Change:
Speaker Change: As well as the statistical.
Speaker Change: Analysis, that's a false now let's take a look at the level that we had discussed earlier and what.
Speaker Change: So something in the past you might bring that up as well.
Speaker Change: And as you feel more confident about the vocabulary then you will feel much more comfortable in a conversation using it alright.
Speaker Change: And it's not a kidney dialysis.
Speaker Change: I would just like to mention that these celebrity.
Speaker Change: Kayla.
Speaker Change: So we're going to draw a tree here.
These are the most one or at least some of the most common level.
Speaker Change: For this last expression and then I'll take more questions. If we have them.
Speaker Change: Not necessarily the case.
Speaker Change: These are the only lagging.
Speaker Change: So here's a miner.
Speaker Change: Also Martin Luther King that data alone.
Speaker Change: This is supposed to be a tree I can draw a tree okay.
Speaker Change: That are going to help you in a particular problem could be that there are better alternatives alternatives available and to understand about the alternative unit to firstly look at all the options that are available. So I had a comment before starting any external data sources taken over the past that you wanted to take a look at the lessons available with leveraging the most popular.
Speaker Change: And so each one of these things we can call. This a branch.
Speaker Change: Or a limb.
Speaker Change: This is a silent and be a landmark so a branch of a tree or a limb of a treat now if you look at this by climb up some of these branches are going to be stronger than others. So maybe I will if I stand alike on a very strong branch up here I know I'm safe I will be supported but if I'm out here.
Speaker Change: And for good reason for this should help build especially you've got a big enough, but that will become more and more advanced and more and more into the data analytics and data science community you I understand that they're better tools available for that one particular path that you're going to perform better.
If I try to stand up.
Out here.
Speaker Change: Do you think that's is that safe or not safe.
Speaker Change: And again this is the same way I would teach it to my own kids just to make sure it's understandable.
Speaker Change: Alright, so the first one it's lumpy number or many people call it lumpy as well.
If I'm standing up here on a very strong big limb then it will probably be pretty safe I will not fall from there, but if I try to stand out here.
Speaker Change: In numeric manipulation.
In Python began performing medical manipulation and everything is all by itself, but it's the interlock off.
Speaker Change: Do you think that would be safe or not.
Now forgive me if this seems like an easy question, but I'm trying to get you to think like a native as I do this so let me know in the comments.
Speaker Change: The information that unlocks the 11th for Alibaba since by attending an update arlinda broken its programming language. It's fine. If you don't understand what that is but in case you do understand stores a lot of information that is not relevant so any type of form complex mathematical locations both of those properties about the.
Alright bring down is that possible. Yes. So that's another example, you can bring something down as well. So you bring something up he brings something down and you would also learn different expressions like like another person is maybe make them feel bad so depressed and I'm talking with my friendly I can bring him down to I can bring them down.
Speaker Change: Object orientated Ness of the particular variables come into play and then negatively.
Speaker Change: I don't negatively impact.
Speaker Change: And my level.
Speaker Change: We will negatively impact the performance in situations like this.
Speaker Change: Yeah, you can improve your English desert view, if you understand what I'm, saying you can you can improve.
Speaker Change: Firstly, just given all the 11 data secondly, a lot of functions that allow us to propose new medical.
Speaker Change: Sure.
Speaker Change: Alright, so I can't create a sentence, while speaking what should I do so yes. This is the point the point is about getting lots of input to help you really understand.
Manipulation ingalls functions for calculating the signing of the cost savings.
Australia transform it also available in many different kinds of numerical manipulation.
Speaker Change: Difficult to implement buy ahead are already implemented.
Speaker Change: But you don't have to worry about implementing that type of variable.
Speaker Change: When the needs to be done the standards. If you have done a data centric pricing for any.
Speaker Change: Hello, good must have chemicals.
Speaker Change: Great.
Speaker Change: Relative to the summarization and many other leading to data it allows us to load.
Speaker Change: Any.
Speaker Change: And that buyback program using something called using.
Speaker Change: If I can call data.
Speaker Change: So shell object artificial gain there, but their data and panda.
Speaker Change: <unk>.
Speaker Change: Affords us a lot of benefit it allows us to perform a lot of complicated stuff online data joining together.
Speaker Change: And hanging our data manipulating data copying data and a lot of different kinds of stuff by hand are going to be very difficult.
Speaker Change: A layer of abstraction or this complicated so you can perform them yourself.
Very efficient.
Speaker Change: Silicon backlog.
Speaker Change: A visualization tool or a division of medicine globally allows us to create charts and graphs and.
Speaker Change: All of us to perform a lot of visualization.
Many independent of each other.
Speaker Change: Global seaborne.
Speaker Change: Meta click underneath the hood. So in case, you wish to become.
Speaker Change: A data scientist and Monica perform aesthetic and your guidance is by visualizing that data.
Speaker Change: It'll either that you might be we could also use other lovely.
Speaker Change: But the backlog is.
Speaker Change: Probably the best one to start with because it's very easy exporters with the some of the fundamental concept that you need to understand when you are dealing with.
Speaker Change: In.
Speaker Change: Item seven.
Speaker Change: Visualization across a charge taken has dealt with that alright. So we have reached the demos Hudson now all the demo I have already returned Nicole for that item right. The chord and you'll have to then follow along and look at the <unk> and expand the court.
Speaker Change: Hi, Tony.
Speaker Change: Need to import the Lumpiness that you need in the current scenario I didn't need to use any new medical manipulations because the data is a little manipulated for me that I can use this.
Speaker Change: I view, it any ballots seaborne ore interactive Visualizations and also Matt.
Speaker Change: Two different equalization Liberty.
Speaker Change: Seaborne.
Creating some realizations.
Speaker Change: Dave.
Speaker Change: And met level of its breadth of each individual well.
This third quarter here.
Speaker Change: The Jupiter notebooks lumpiness fulfillment because it may be possible that you guys aren't able to see that now in backlog in the.
Speaker Change: Possible to use us for multiple decades.
Speaker Change: Ethernet revenues.
Speaker Change: Jupiter notebooks, but just trying to tell method, let them whatever visualizations that can produce make sure that they are optimized you'll be rendered instead of Jupiter.
Speaker Change: Not many people say, it's not build.
Speaker Change: It was another 30 previously, but now it's not but I'd like to keep it then.
Speaker Change: I am importing of Kingdom dataset and give you some context things a little bit that they have downloaded health guide that you can download it yourself.
Speaker Change: We're recommending a the do it afterwards.
Speaker Change: And you can play with.
Speaker Change: This dataset.
Speaker Change: Many other datasets out available on Camden for yoga experts.
Speaker Change: Experts in data analysis.
You can do it all.
Speaker Change: And when you are doing it Ken.
Speaker Change: That is correct.
Speaker Change: That you have consented that people segment is a great platform for that.
Speaker Change: And now we take a look at the first column.
Speaker Change: Column caused by both of the data now as you can see the shape of the cycle in one column now 81 columns is probably a lot better than that even.
Speaker Change: Anthony it's an efficient use of all of it because it's not that it's.
It is not possible for us to show a difference.
Speaker Change: Now, we'll take a look at the inflammation of the data.
Speaker Change: It tells us about the data set that we have and it gives us information about each column.
Speaker Change: So we have 146 as you know.
Speaker Change: Columns normal data points.
Speaker Change: <unk> 64 in the <unk> column, and Youre going to take a look at everything and you can see.
Speaker Change: In.
Columbia will have 91.
Speaker Change: And then given date of it.
Speaker Change: That means that the majority of the data if not we'll deal with that in a moment at the casino data quite disparate sometimes it's $4 60, sometimes hotels 14 52 data points. So we have we are dealing with a data set in which we can have another vendor's quite a lot.
Speaker Change: Q.
Speaker Change: He has only seven value that others are all of them and as you can see the data set the spread the spread occurred somewhat objects without missing a strain or some other data other than the Americas somewhat ambiguous and somewhat flawed.
Basically.
Speaker Change: Neither numbers with decimal points, so currency could be considered low.
We've got a number are considered.
Andy: Andy just for a moment.
Andy: Number one because it does have because its not going to have a decimal point with that so.
Andy: Don't want to get bogged down into the specifics of that but I'm trying to tell us how will go.
Andy: Here, what we're doing there.
Andy: Taking all the column and rich.
Andy: Data is greater than or equal to Turkey.
Andy: Any color on the content, let them, 30% data because it has 40 860 column, but only hockey or lift or the values are present in that and we're going to drop it so any colander condense less than 30% of the data, we're not going to consider it.
Andy: Could have negative implications, but we'll do that now.
Andy: We also the I'll call them commodity does it mainly because <unk> is just something that is generated by the database.
Andy: Or maybe not by the data center by the people, who typically can fortify interloper.
Andy: <unk> provides online data analytic scores in partnership with IBM and Microsoft The course link is given that the description below now let's continue with the session. Just a quick info guys. That's generally just data analytics by answering this question, but just the falling method creates a new object that looks at the <unk>.
Speaker Change: I'm data eight deal.
Speaker Change: Colby C based b.
All of the above I'm going to answer in the comment section below subscribed into Loopnet. The right answer now that's going to do with the session.
Peter.
Speaker Change: Not really important.
Speaker Change: Important for storing the data for analysis of the data is just a number that was part.
Speaker Change: It has no significant button now we will take the list of columns that the drop as you can see Ali when Youll see trends.
Speaker Change: And miscellaneous other column that adopt.
The one that can have dock manually even though it contained a lot of data now.
Speaker Change: <unk>.
But we take a look at our San Jose.
Speaker Change: Column that were very interested in.
Speaker Change: Columbia FSM from water.
Speaker Change: Uh huh.
Speaker Change: Let me describe the AWT and count the number of columns.
Speaker Change: Everybody will have data available the main is it.
Speaker Change: One lakh 80921 point some value.
Speaker Change: Mission minimum value 25%.
Okay.
170, <unk> percentile of the top quartile and the maximum value in the in the column.
Speaker Change: By the way this is something that we didn't have the right functions to look all the data and figure out the main match specific buttoned down and all of that so it gives us the name of the column.
Speaker Change: David.
Speaker Change: And now what we do.
Speaker Change: Don't visualize it.
Speaker Change: As you can see the majority of our data.
Speaker Change: Mhm.
Speaker Change: Yeah.
Oh here.
Speaker Change: This is what is known as a histogram.
Speaker Change: Or this condition.
Speaker Change: Seaborne allows us to do it.
Speaker Change: Don't worry about the court stick with us at the end of the presentation I'm going to show you what you can learn from data.
Speaker Change: Right, now, which I think I understand the process of exploring.
Speaker Change: So as you can see even though we have a maximum value.
Speaker Change: Seven seven last week about Poland.
Speaker Change: A majority of our venues are lagging somewhat I don't.
Speaker Change: But.
Speaker Change: Now, let me take a look at the different data thats available.
Speaker Change: We have antigen.
Speaker Change: We have all of which stands for object.
Speaker Change: But the basic elements.
Something that's not a number.
Speaker Change: And we have built.
And just basically how many which are used to represent that.
Speaker Change: The data could be a hyphen.
Bye there changed at all with it.
Speaker Change: I'm here with <unk>.
Speaker Change: Okay give me all the numerical data that we have.
Speaker Change: This was done mainly because we wanted to be sure that they don't need any numerical data.
Speaker Change: Dealing with data that is not nomadic but doesn't have to convert numerical format components of their clinical broadly estimate the task ahead of us.
Speaker Change: No medical data lots of software and ankles before basically not selecting the.
Speaker Change: Object database.
Speaker Change: We're taking a look at the head and will continue to test 37 column, which is a massive drop from what we had earlier.
Speaker Change: We had 81 column.
Speaker Change: We dropped some.
Speaker Change: Or because.
Because they had a lot of knowledge values. This one is something that we have done all year to understand what the data is about now.
Now we take a look at the distribution of our data with them to serve them.
Speaker Change: What we do is we basically create understood all the columns.
Speaker Change: Again, we didnt have to do it ourselves we thought I'll put anyone columns.
Speaker Change: Now when we can take a look at that data as you can see it contains a lot of data.
Speaker Change: Well again, a lot of that is centered around a particular data point others.
Speaker Change: This funnel both only consume so as I said does need also that's fine and you can take a look at our data and.
Speaker Change: We're not going to analyze it right now in the process.
Speaker Change: Understand how thats under this facility.
We take a look at correlations coordination basically the number that allows us to understand how.
Speaker Change: Allows us to understand.
Speaker Change: How much of our data.
Speaker Change: How much how much incentive that one column has on a particular problem.
Speaker Change: When you move that lost volume because the lost at all it basically.
Speaker Change: Some of the comments in the last column.
Speaker Change: So we don't have to worry about that from here, we basically just take all the columns correlation greater than.
He called Little Golden feature list any column that has a higher value.
Speaker Change: In coordination with <unk>.
Speaker Change: One correlation, but I think we're making the absolute value, which means we don't care, whether it's negative or positive correlation goodness from negative one positive one negative one means that data in Colombia.
Speaker Change: The other column negatively correlated with increased positive sentiment.
Speaker Change: Great.
Speaker Change: Column.
Speaker Change: Data and other kilometers further than accordingly.
Speaker Change: So we take that and we started in ethylene.
Speaker Change: Now if we take a look at the coalitions. So these are the corner lessens the highest of all understand we have an overall quality and.
Speaker Change: Life area.
8% to 70% mono wasn't endo.
Speaker Change: We take a look at all the new medical columns.
Speaker Change: We generate.
Speaker Change: First five one or all.
Speaker Change: Values, but the <unk> been trying to create visualizations to understand how a particular column and that's the first five columns.
Speaker Change: For instance, <unk> MSR class.
Speaker Change: <unk> locked.
Speaker Change: And so on.
Speaker Change: And as a consumer.
Speaker Change: For ethical and we're doing it right.
Speaker Change: Now that we have done that is a bit difficult that Lincoln had been difficult on defense bank of China and explain it to you.
Speaker Change: But basically here.
Speaker Change: What we're trying to motivate them to create individual data.
Speaker Change: Our individual feature.
Speaker Change: As I've named it.
Speaker Change: For some time, but I think it happened.
Speaker Change: Based on particular person.
Speaker Change: The Colombian and defense space, and then using that data.
Speaker Change: The columns value market for the deal.
Speaker Change: We added to that then.
Speaker Change: All called emissions metrics and loan.
Speaker Change: While the terms of the existing part. So these are all the 11th phony coordinated value et cetera.
Speaker Change: Right.
Speaker Change: But essentially we have 81 columns, another Nielsen down to only 11 columns.
Speaker Change: Hi influence on.
Speaker Change: Right.
Speaker Change: Column.
Speaker Change: Now the real I think what we have done is we have created at.
Speaker Change: Something called Hikma.
Speaker Change: Assuming that you guys can see.
Speaker Change: We will build a little too much.
Speaker Change: Also you can manipulate the size of the blood.
Speaker Change: But when you're writing code I'm not going to go with right now, but as you can see that.
Speaker Change: Columns have correlation of zero point fall or somehow.
Speaker Change: And this is negative as you can see the color coded.
Speaker Change: So any color.
Speaker Change: Light colors, such as blue or purple.
Speaker Change: The negative.
Speaker Change: Any color.
Speaker Change: The golar.
Speaker Change: Yellow or green.
Speaker Change: As we continue to have 0.8.
Speaker Change: 80% of coordination with Gi and liver data.
Speaker Change: The <unk>.
Speaker Change: And so this allows us to understand what are the particular columns that are highly correlated with each other.
Speaker Change: Allows us to understand whether or not a column has influenced our largest one columbus multiple columns and how does that affect our purposes. We have a fancy thing called package and bite them. So those packages that are available for free in the market. It has a lot of Britain functions and building things that will help you guys too.
Many many or easily without waiting for them to begin an algorithm and so in that way of these packages that really helpful. So number what it does it is used Florida mathematical and logical operations on that is okay and it provides richer in Florida multi dimensional in Arizona.
Speaker Change: In retail at Asbury and doesn't have any area. It is built on a list on mute. So basically when we go to number eight when they go for indexing slicing and all those kind of stuff. It will only be about list everything will be based on the list. So that's how it goes okay. So number it can support one lead to lead both of the banger Fridays, Okay and I think.
Speaker Change: You might be using this anaconda version of brightens up there that could be coming along with partners around I remember your Russian opinion start okay.
Speaker Change: Somebody will be pre installed and thereby conversions, okay, but I mean, I'm not going to debate now revenue user package rate, what we do we improve the package.
Speaker Change: We improved our package with something like <unk> or those indexes. It can be any package. Okay. Eylea can be anything so what are those impacts in Texas import them and then move back to engineered then they'll ask you weren't dystrophin earlier about revenue would need to pay the entire thing throughout the program and then we will have less mammals and be okay.
Speaker Change: So that's what it was okay. So now when I say a number yet it can convert anything <unk> by 10 that are structured to address okay. So that's how it works. Okay. So if I assure you like this.
Speaker Change: Ryan just picking Diana.
Speaker Change: Yeah. So it is converted both these things so the first one is a bypass lift second one Lisa Barton capital right. So when we parse that our human capital our list to acquire them at a function it converted them Aldo.
Speaker Change: Nobody at it okay. So that is what this.
Speaker Change: Mr Zuckerberg and whatever we provided obviously the.
Speaker Change: Our pumps are stocking and ending with scrubbed record side. So that means it is already converted to a list and now we can perform various functions in we can perform various functions in them by a year or so but once we have number yet it created where it can be any different.
Speaker Change: Hey, listen it's not anymore. A couple so that's why it isn't it doesn't and number it doesn't come by default with Python, if we want to use it we need to install it. So let's say you are not using and I Wonder and you are using this kind of fighting a user interface.
Speaker Change: And what do you need to do you will need to install it as income on chrome Okay paper installed sorry, Dan again that again, we've been strong numbers, where it will find it and it will install lump I guess I don't have this big question threat. So that trade is not working but it should extraordinary okay. So that's how it works. So we can use.
Speaker Change: And this kind of functions or so yeah now we can have to be at his impact.
Okay.
Speaker Change: So I'll reiterate that we don't get like this so we just need to pass will be areas that would be adamant to less rate. We can pass to list. So it will it be conquered anything at all.
It will be at it.
Okay. So it will be changing it so that is all related to him a number right now very very clear that is in Britain. So that's how we do it. So now next will be Uh huh.
Speaker Change: It is.
Relative advantages that <unk> been using a basic part of time, let's talk a bit like that right. Then they start by fantastic right. So very number you were thinking that yes. So do we attending from multiple to non notable star then one rate mixes MBIA object. Okay. So and then by as I have shown you.
Speaker Change: To be at his right. It can go up to and then you can have any number so in the real World. If you asked me read on views more than two <unk>, but yes. It can go up to that okay. So like that it works for next year and the next two years or like Hawaii, where you use a number of areas. So in numbers.
Speaker Change: There's MBR is we'll have all the items of same day. Okay. So it can be one of them is and then the one off premise string list. It has to it all of them has to re enter is okay. So items can be accessed using zero based index as you know our vitamin areas I can list. So this will start from zero indexing rate. So here I'm sorry, it remains there.
Speaker Change: Same property it has zero index or if we go forward number yet and then if we want to see that first draw it will be like this.
Speaker Change: So it really give you the first true and this will give you the second row right. Similarly, four columns. If you wanted to see first sorry.
Speaker Change: If you want to see the first column, you'll need to do this right.
Speaker Change: So you need to do this so you will see the entire values. If you need to see the on leader first column then you need to do later this like back to work right. So that's how it will be working now for to be areas that will be coming okay. I'm working on to be honest. So that's how it works. Okay. So we will be coming to that in a few minutes. So could you.
Speaker Change: I will show you so for others, what was the names and the rate could be additive will be showing you that.
Sure. So far it is if you see you on there. It will give you is you know that will be one one would it be too like that and if you do want to go and then it will be printing only single element because that right hand side. The mix is always left okay, right and trade index. It is not up to it goes up to that it is not.
Speaker Change: Great uncertainty about trade has two elements now if we do this then it will trend all this.
Speaker Change: That also will remain the same and it will be in the same way. It is brokerage fee base you are pretty much baked the list that we haven't by.
Now a number of areas are beneficial because it picks up the same size of blocking the memory for all objects stores. Okay. This part we will see a company that in the later half of the section and then we can understand if theres been any questions rather than starting at a medical classical list.
Speaker Change: Our next is if you go and check the individuals yesterday, so it will be begin colon and minus one.
Speaker Change: I can tell you we will always be executed like that Okay. Next is initially do some great. So how do we initialize number yet because we are already seeing great. How do we grow that still this is how you do it. Okay. So initialize means are what we want to do is.
We don't want to play SME listing.
Speaker Change: Less than that but what we want to do we want to have some by default values. Okay. Before to add is we want to pick it up.
Speaker Change: Let's see how we do it that's the first one is we will I mean, if you guys are familiar with the problems like masking and all that is for image processing and data processing, we will do that lot of banks, we will do that okay. So a lot of time, we will be.
Speaker Change: Total thing we will be using this zero based or Onewest indexes, okay. So far creating a zero based studies right, what we need to come on what we need to do we need to have some <unk> some sort of like some shape of zero areas. Okay. So that's how you do it again and that's how you do it in our numbers. So what do you do it right now.
Speaker Change: <unk> zero turn you specify either menu was basically frida dimension of that pre buy for means it will have like three doors and Fort Collins. So first one is off so you can think of it like this it can be and be dark zeros, Andrew Cuomo column.
Speaker Change: Our total unit II project.
Speaker Change: That's that's kind of a properly you need to pass it totaled up right. So that's how it works in Bangkok.
Speaker Change: So that's how you printed you Edison number okay next areas. If you want something within sort of a right. So how are you doing right like this and Peter are there.
Speaker Change: Within the bracket we pass.
So it really there were a number of new areas of what we are doing we are I'm, sorry, and mirrors, what we're doing.
Speaker Change: Eddie.
Speaker Change: Yes, so and this is what we are doing we are experiencing so.
Speaker Change: Actually okay. So what we are doing we are passing through video, but the first one is the starting number second one is the ending number and last one is that the line does that instead of a lottery wanted to print. So from 10 to 25, we are having fire everyone. One lists to refi separate debate. So first one second one difference.
Speaker Change: And if I like that so that's how we have so we have three numbers written for pinpoint and always remember elaborate and will be excluded children keep everyone were taken up till 'twenty. We will go to 'twenty, we will print it out okay. So that's all of those kind of areas are getting printed out in the industry.
Speaker Change: And in.
Speaker Change: Next thing is <unk> done right now, let's say we want.
Speaker Change: We want to spread some wine Toyota strictly okay. So how do we do it we went through.
Speaker Change: I mean, what I mean is if you think of Adobe planned rate like some great plan. If you think of and we want to spread some points along that line. Okay. So what we will do that like we want some point between LNG and range be okay. So here, we are giving them kind of resonate. So we need to calculate the interval, we need to calculate the final dividends and all of them.
Speaker Change: We need to provide it here so that's how it works, but we don't want it we want it other ways. Okay. We wanted to spread out I'm Gonna Nightline like this so far between five and 10. We work. We won six numbers. It is almost CMS number you were little bit different so how it has differing I will show you, okay mixture clean space Okay.
Speaker Change: So <unk> so a range what it does it takes in to see numbers first dark number and number and so we are what we do in our injury have tools are clean numbers. It is apparent that we.
Speaker Change: What we take we take and the most of our stock number and number of Amber interval and we print out a values based on their intervals. So from stock number and then number we've rented a values based on the inter was okay, but emlen space, what we take we take three parameters again and what we have in there that we have lake we have stock number.
Speaker Change: And number and then we'd taken number of points in there, okay and number of points in there. So if we give you our stock number and a number have been if we split it into pinpoint then we will get pinpointed where to invest back in our modern number. So that is a dependent brilliance brings advantage and all of these functions, we will be using heavily when we use a golf what did I say.
Speaker Change: Okay, because these things, we will need and they're going to see how do we do that okay. Now next is how do we do with the same number. Okay. Next is how to create an area with the same number. So this is the same as same park same users like that like four zero state formats photo carving out some mosques buyback.
Speaker Change: Okay. So if you use this keyword.
Speaker Change: And if you pass some values.
Speaker Change: All right.
Speaker Change: If you buy some values then it will give you back a number of the damage I meant for the Diamond turned it will take care and it will give you that kind of an area or all I've been column and then it will be picking up so and we don't produce at Twitter pickup coaster topple startup a lawful Roe numbers.
Speaker Change: Korlym numbers and the political number to fill that out it was okay.
Speaker Change: So felt like that this pain.
Speaker Change: <unk> that's full function works it can be everything okay. It will give you our RF pull off that kind of the most of any any dimension, Jamie diamond Jim can be given okay, and maybe everybody numbers kind of a given I mean, sorry, I mean, depending on what can be provided hasn't been barrington. Okay. So that told us full function will work, okay next to student demand.
Speaker Change: Again, Florida, I mean back from asking only so.
Speaker Change: Oh, we do.
Speaker Change: That said, we want that shire for matrix to be randomized.
Speaker Change: So it really gave you some some haven't been numbers of random numbers.
Speaker Change: So that will be and that those will be structured enough metrics, there, but I mean can you pass and that rate will be organized so random as this thing. Okay. Now we go to how do we access this number it is okay.
Speaker Change: Sure, we are creating to where it is.
Speaker Change: How do we access it okay, how do we see what's wrong with it and whatnot.
Speaker Change: Actually I am creating a two D area, Okay, alright afraid to do that.
Speaker Change: Yeah.
Speaker Change: So that's how the shift function. So again, that's how we can access a shape of ordinary share function will return but below.
Speaker Change: Zero number in column number yeah, so ADR sure.
Speaker Change: Tons of coal.
Speaker Change: Well go on them.
Speaker Change: Like that okay. So it will be returning like this okay. Now the amazing thing is what is their use of this shift function. Okay. So this is a couple but we can't change the values that others.
Speaker Change: So.
This really changed it we can access plus ship function of it and we can change that values. Okay.
Speaker Change: Seven.
Speaker Change: Now we can access like that come.
Speaker Change: Come on.
Speaker Change: Accessed Tess.
Speaker Change: Yeah. So we can access it like this okay come on and I have not given what.
Speaker Change: What is that.
Speaker Change: Okay. So we can access individual elements of those shipped Apple that is being returned like this okay. Like we use like re access no multiples. Okay. So we can what we can do with this <unk> sure. We can give you.
Speaker Change: We can get the Apple I mean.
Speaker Change: Couple of a number and call them number that output, we can change the axis J&J best shape of the shape of the matrix with this step function and we can access individualized I'd say you want to see how many of those out there in the desert.
Speaker Change: You can use that you would want to look through all of it is at all levels of losing a fault of your own portable format and also backs when you use a drop ship and four print there. That's how you use okay footprint there and what do you do your you were just I'm, sorry pardon shape zero that means you are accessing individual elements of what happened right because you will be giving you the number.
Speaker Change: Frozen one will be giving you the number of columns like that okay. Okay. One thing I saw you guys are going to do it.
Norbert Lambda functions across covered yes.
Speaker Change: Correct, but just to want to go back to the material Index Zero Index total return the number for all correct right.
Speaker Change: Come on another zero then it will give you the number of column is it okay.
Speaker Change: Sure I'll ask you for a minute.
Speaker Change: Carlos and gross debt is at zero when he was at <unk>.
Speaker Change: One when we look at it.
Speaker Change: We call them so that is it.
Speaker Change: Conifer's Dudeck sortable, how it was number of element and couple we will we will have a one to one day out of Asia, but one of the areas and we are having to indexes date, okay. So basically don't want like that.
Speaker Change: One day, one day or one day or whatever your product. Okay. So that's how it works. Okay. Now we can have some more examples of in beta I mean, this shift function, but I am not going into the detail about the unit to remember one thing you'll need to match the number of elements in that upper right. We have six elements right.
Speaker Change: <unk> two <unk> hundred three it won't work because the size of that is that the last six to eight so we need to have at multiple of six <unk>. That's how we can break that so we can reshape the Max what is about this share function. Okay mixed is how to get total number of columns. Okay.
Speaker Change: So again I am having been functions and they want to see the size of it. So it really gives you bring before as you can see there are going to four elements in that it okay. So and that's where that told us precise function works and this is another thing backstopping orange. So he heard about it takes it takes some number at all.
The moral values.
Speaker Change: Number of points there.
Speaker Change: When is mace.
Speaker Change: So our numbers.
Speaker Change: Between zero.
Speaker Change: <unk>.
Speaker Change: Okay. So that tells you about numbers index off of Orange box. Okay. So far size function. It will give you. The total amount of Ilene mentioned Antarctic. Okay. So that is what size function of Oxford. Okay. Next is dimension of it is more or less same like this.
More or less the same thing like that like.
Speaker Change: Like a ship function, so I will put it right in that but the only difference it can be modified okay. So literally we read this and I'm right. So how it will work and NIM.
Speaker Change: Telling you, but it is not.
Speaker Change: With different from share shape returns you the exact shape of that area like the number of rows and number of columns right, but and then we'll return to you the number of dimensions of better. Okay. So if you see that in them than what we have we have towards the return rate that means he took over the last two dimensions three in two dimensions.
Speaker Change: So that sort and then returns okay and then Daniel the number of dimensions of Barrick, Okay. So will that be in November.
Speaker Change: But.
Speaker Change: Okay. So that is a word to remember so and then return to the number of dimensions here that is not sure but it is a dimension. Okay now vita it tapers their database that we have already Okay. Next is D day okay.
Speaker Change: But just to give you the type of each element schindler.
Speaker Change: And then Brad.
Speaker Change: It will give you a type of all the elements of a number of areas cannot be heterogeneous. It has to be homogeneous always so number yet is whether return you order them.
Speaker Change: Homogeneous areas. So when you do a deep dive function on that area. It will return the order number off not the maintenance of that it I mean, sorry, but it is painful.
Speaker Change: For each of the elements in that okay. So here, we have 24 before into numbers very very we have 32 is already done.
Speaker Change: So that's how it works okay. So that is what is the vote. This detail fungi mixed is how do we use it formats calculations. Okay. So.
Speaker Change: Any downturn. This part this data not only for you show you something more sure.
Speaker Change: Okay next is.
Speaker Change: They have created now a river play float numbers. So you will see the.
Speaker Change: The tape is treated as short 64, okay. So you have a you got a matter of I mean did they best short 64. So that's all of this speed based on sidewalks. Okay. So a number as the name suggests it used it is used for linear algebra. Okay. So a number of years used for number of mathematics or linear algebra. So.
Speaker Change: You won't be really having stream.
Speaker Change: So you won't be having that there is no provision for him, but he is only useful linearize the brick operations and feedback when it didn't figures.
Speaker Change: I think more than that okay mixed by.
Speaker Change: <unk> mapped okay.
We can do some Asian division multi application and all those kind of are in their stuff with this and by getting all I'll do it do it okay, let's get started with the singularity.
Speaker Change: So this is our number one submission function works just a quick info guys better knowledge of data analytics by answering this question which of the following.
Speaker Change: All statements about data analytics.
Speaker Change: It collects data be it looks for patterns see it does not organize data D. It analyzes data coming to answer in the guidance section below subscribe to Intel, but the right answer now that's going to do with decision and it can add up two different things.
Speaker Change: Okay.
Speaker Change: Okay. So some will it on you or some of all the elements are that can be Daniel Sam Moore, who aries, okay. Some can do anything.
Speaker Change: Yeah.
Speaker Change: So sorry, so some kind of Daniel either some off all elements.
Speaker Change: And if I kind of at a or it can be done you order some more elements of Puma traces so matrix multiplication metrics ambition. All this logic will be implemented in there.
Speaker Change: So now what it will do it will take up to list. It will add up all the element and it really tiny without pork. Okay. So five tend to treat that means 10 515 until two five so 28 adverse subtract it will take two areas and it will subtract barrels and it sort of the U S. So.
So that's how this abstract subtract and non By-talk. Some function is used for summing up and bookbinding difference between two actresses EBITDAR. Some has a few players in it okay. Great again, the world few things. So we will discuss back so let's say, we have seen how it exim who lists and.
Speaker Change: <unk>, Arizona anecdotal show unit.
Now if you specify them excess parameter then it world order some based on the rules are based on the columns. Okay.
Speaker Change: And before you.
Speaker Change: Yeah, So <unk> gone through three rate, if we do a sum of some with excess or documents that will do your column ways. Some so that means five to 710 to 30, Okay and if you will for our roadways someday could be acoustic was one so it will it aneel.
Speaker Change: <unk> and <unk> right. So that so this will this will work that out of the roadway something that that's how it will work. So that's what is so that so this functions or.
Speaker Change: Yes, that's on this number.
Speaker Change: Areas.
Speaker Change: Some function walks okay. So that's how you can get there.
Speaker Change: Okay.
Yes.
Speaker Change: Okay. So this is all covered well said.
Speaker Change: Good morning.
Brian: I'm Brian.
Speaker Change: <unk>.
Speaker Change: So because of that gets.
Speaker Change: I'm, sorry, no not like batted one ticket.
Speaker Change: Yes.
Speaker Change: Two 7% returning product okay. So to confusion about that first of all.
Speaker Change: We liked it at like Axis, one way it is not picking up.
Speaker Change: So let me just for some examples in the ppt, bringing some casual mistakes.
Speaker Change: That's what I heard Dave So 00010 or is it just shouldn't be easier over the last one was it order delays one Andrea so expanded 010 phase one yeah correct 0016.
Speaker Change: Population for example.
Speaker Change: What it does.
Speaker Change: <unk> got some great, Texas, the one hearings and probably let me just give me a few minutes.
Speaker Change: Jack.
Speaker Change: Yes, now walking frame zero, yes, now explain so.
Speaker Change: Okay.
Speaker Change: Hey, Misfit B.
Speaker Change: Most hospitals that are list how it works, yes nowadays.
Speaker Change: Yeah.
So again.
Speaker Change: Okay, I will be getting back to you on this so for now let's move ahead with the other functions.
Speaker Change: So barring some kind of a problem I will take that in the break and I will explain that after the break so I'm not sure where it is happening and it shouldnt be happening and playback. Okay. Let me go ahead saw some I will discuss it later.
We already have discussed for this division also it will work in the same way, okay, what cause it to be sure.
Speaker Change: Sure.
Speaker Change: Okay.
Speaker Change: Yes.
Speaker Change: Past two parameters bodyweight function it really varied there always a column ways as you defined with this exist right. So.
Speaker Change: Okay. So the varied won't work excess ways. It will just take and do parameters Anatol if I do the numbers again it always it really were to the numbers. So if I ran through will be debated like or what or what happens in the matrix metrics Electric's operations right and not predict the same thing will be a player. There. So in mattress operations of what we do we just not there would be great.
Speaker Change: The element that's actually went up a mixture as I can tell you one way or the secondary I wonder if that makes generate so that is how it works. So that is how this market we will do it.
Speaker Change: And we'll walk here, okay similar layer, we can see the multiplication being work done the same thing how we.
Like our data in a matrix multiplication and our cluster of plants. So it will be same things of iron reporting right resort temporary mcnugget will work. Okay. So these are our basic view functions that will be needed for our our data science.
Speaker Change: Science courses and this ESP it will give you each of the power.
Speaker Change: <unk>. It is first square root and predict same as four signed value of each of the elements and <unk> cost per pass value locked for log values. Okay. So these are not really used in the test range near term things.
Speaker Change: I will assure you quickly or lytic Sam Bush.
Just to have your basic knowledge about this number we are I am showing that for you. So these are not really needed for borgwarner datacenters like Oh.
Speaker Change: You can just check this values okay.
Speaker Change: Okay mixtures.
Speaker Change: Element plays a comparison so.
Speaker Change: Yeah.
Speaker Change: So that's one element why waste comparison does so it will compare to each and every element of the ideal with another one and it really doesn't even less staff two horses. So not so it will work.
Speaker Change: So that's how it really work it will give you our output of group was encouraging exchange something this report and the second element of output will also be changed.
Speaker Change: Now along with this and protocol there comes another function that we check the indirect okay. If all the elements of that are same or not so it back one is ordered.
Speaker Change: So it will give you a single value market to work for us. So it really take that is that this past week that will convert those on a broader level, but in Ghana, Oliver I'm going to give you on listening youll output of aircraft through our falls based on the company's undergoes yeah. So that's how it is one that's how it works okay mixtures.
Speaker Change: Aggregate function on this on a singular aggregate functions always work on our singularity. So that's what we're going to see next.
Speaker Change: Now if we show we have already seen this some function right. So these are a few aggregate functions after or what but we can apply to one number here okay.
Speaker Change: Alright, Thank you Miss.
Speaker Change: If you rent it or not doesn't matter when you do an pit at summit will automatically we converted one at it. So you can skip it safely okay, so and predict some and we'll give you some of all elements minimum when you give your minimum Max will give you. The actual amendment main will be the mean of the elements. Okay. So many means.
The relevant after areas okay.
Speaker Change: And the standard deviation is how the how the elements before from the mean.
Speaker Change: So that's what the standard deviation.
Speaker Change: Okay.
So that is what is the road standard deviation I will try to connect my bad I'm going to show you the formula one's.
Same thing will happen. Okay. So it really gives you a sum of all elements minimum of all elements Max of all may not fall like that this quarter illusion Korea's crescendo that machine learning Tom will go into it later I will tell you what is it so it's not non let's not worry about it so except this have all of those things clear. So that's how it will work.
Speaker Change: Okay. Okay. So that tells me median and standard deviation you can take into account, okay and what is I mean, what is within my real socket for the next class fore sight because that then we will be discussing the oldest statistical things at one go and they will give you a brief about our lives. Okay. So if you have forgotten this norton where he will be missed.
Speaker Change: And the next class. So that's framed mingrelian model. This thing so I'm not sure I am not having my bag with me. So I will have it in regulatory the formula than all the small stuff and also what you can do you can one sports through all this basic things, okay, but just the basic stats not net of an entire detail you can go to that basic so it will help you understand.
Speaker Change: Standing what him coding and the next question. Okay. So that's how it will work. So okay. So this is this must be clear no. So read that as I'm sure. We will go ahead with through it because I'm new topics.
So that is lake broadcasting their walkers broadcasting if we have taken to arrays of different dimensions EMIR.
Speaker Change: What would have happened.
Speaker Change: That said, we are keeping till this park.
Speaker Change: What does this together.
Speaker Change #100: Thanks, guys, one is not coming through.
Speaker Change #100: For this kind of cases, yes, okay. So let's say we have this kind of an area to work with so the first area. If you will see the first area is off to a three by three right. So 123 and 4563 by three at it right now.
Speaker Change #100: If you see carefully we have past 456, only three or four five only three numbers. That's a two way street and the next one if youll see background is a one way to reiterate three or four five only three numbers I've been there. So it isn't a run rate right. So now when you added you.
Speaker Change #100: If you'll notice it carefully you will see a second raw fat here is also getting added with the same number 345 right. So that's where it is coming for <unk> III 75496, very relevant. So how this is happening. This is the concept of a broadcaster and Mumbai. Okay. So if you see in this.
Speaker Change #100: Graham what is happening this area is getting expanded towards the two match with the exact dimension of this headache for prosthetic. Okay. So this was an area one by three you back when it stretches down it becomes in area four way right. So it matches the dimension of the austerity been on lead this area ambition is possible.
Speaker Change #100: Right, but this is the concept of broadcasting that this is a countertop broadcasting now one thing to notice this broadcasting warmth only work for at ambitions. Okay. It will work for all kind of production between two areas a number okay. So our view just as you have just seen this thing all.
Speaker Change #100: <unk> walks for this subtraction also right.
Speaker Change #100: Production is also subtraction is also supported Army broadcasting has also done for those obstructions scenarios themselves.
Speaker Change #100: This is the contract profile live broadcasting so what it does but number yet it gets insulated to match with the dimension of the cluster saw that the operation battery.
Speaker Change #100: Suppose to do this possibility is feasible. Okay. So this is what the concept of Arab broadcasting is.
Now indexing and slicing and partner base as you have already went through I really just I will go through the habit quickly. So let's just see how you would do it so and mix them by kind of less through the first door NPL position right that can either start from Miami, Charlotte Gastar chrome plus or minus.
Speaker Change #100: Some of them when we go for so Python when positive indexing is done then.
Speaker Change #100: <unk> gross from smaller number two higher number and then print accordingly, right. So let's say if our best monthly Buyten Riyadh going from six to 10, so that the really big from B H eight because 10 will be excluded but when we go for minus one.
Speaker Change #100: And then we go from there were larger number two smaller number because as you know for a negative everything goes off of it and the bigger the number is it is it is smaller right. So my Master L band minus seven at pretty go from minus 12 till my method because of our it and we'd always be executed. So that's how it will represent.
Speaker Change #100: Printing their entire among these strength, okay, but that is what our indexing walks in price analyst. Okay. Now when we do it the worst X calling them back means that is from six deals like that in mixing is that okay. Mixed. This slicing. Okay. So is zero if you will see this matrix.
Speaker Change #100: 23456, and seven at name. If you go for is zero then it will print all the elements of the pastoral and few so that's how fast draw will be zero with index and past column will be zero index. Okay. A few go than we'd been brackets korlym and one that will also return even the same thing Okay. Let me show you that means.
Speaker Change #100: We are just thinking.
Speaker Change #100: Yes.
Speaker Change #100: Okay.
Speaker Change #100: You know we return the first element of our I mean, the first to go off to matrix. Okay. When we return equaling one what does that mean, so Colin one is for Doug.
Speaker Change #100: So what we want we want all the rules okay. So into the air is how old are indexing walk if you know so it will be a komatsu operated ones. So first part will entirely worked for rules second part will entirely work from columns. Okay. So if free rate column, one that means for the.
Speaker Change #100: Raw part give me all the second draw so that is the first true right. So that's what it is returning 123, but that list is within their list because we are taking the taking it as a list on it we are slicing. It. So I told her list, okay and offer them a list of lists and the first one offer easy, though we are just simply accessing that.
Speaker Change #100: First of all subsidiary I mean, sorry to be at it and that's why we are getting 123 years or less okay. So that's how the slicing walks for areas now, let's go back to the PPD and let's see how or what what all the things. We can do so equaling one extract del Ro equals zero documents.
Speaker Change #100: Perhaps maybe if it is the first draw that is great.
Speaker Change #100: Now for the second part if you.
Speaker Change #100: If you remember the mixing affecting pig.
Speaker Change #100: So the first one will account of Adderall and second one would account for the columns right. So if we do colan, one backman to give me all columns fill one so that is the first column. So that will give you only one okay. If we do one korlym Backman give me all from the first the first column till last okay. So that's where we can.
Speaker Change #100: This one Columbia later, new two three okay. Now if you remove one mcmeans you want all the rules and are you one columns from two onwards, okay, but that's why it gives you 235689, okay. So that's our indexing and placing works, Florida number yet it okay. That's how it works.
Speaker Change #100: Okay. So we will have a few example kits to it so just a quick info guys interloper provide online data analytic scores in partnership with IBM and Microsoft.
Speaker Change #100: Firstly is given that the description below now let's continue with the session.
Okay. So.
Speaker Change #100: Okay. So let me explain this to Korlym means we are one thing the third row of the matrix. Okay from the <unk> onwards, So Todd Rose the last rose only one broke will get printed out and we are giving colon III, but that means it will go up so hooked up to the third element of the column right.
Speaker Change #100: But we'll give you the entire column, that's why Couldnt return deals.
Speaker Change #100: Okay.
Speaker Change #100: Next thing is area manipulation okay.
Speaker Change #100: Manipulation is tool stacking at it I mean like okay. So what is the difference between the area manipulation and some another operational director yesterday. So in that case, what we're doing we are performing some operations on two areas and we are giving out putting I've heard out of here also we will all.
Speaker Change #100: Most of the same thing, but a little bit differently, okay, and we want to add or subtract those we will keep the originality of the Arizona, we will put something out okay. So.
Speaker Change #101: Okay. The first tranche of Navy contract.
Speaker Change #101: Okay. So this is already a horizontal concrete emission.
Speaker Change #101: So there are areas, where we concurred horizon.
Speaker Change #101: And one is out of bounds it shouldn't be.
Speaker Change #102: Okay, we don't have any columns and you're right. So that's very columb ways is not possible, but if we had to be at and it will be possible that sort of it will create for you it will be easier for you to understand.
Speaker Change #103: Yeah. So this is like column ways stacking right.
So this 123 in here in the 345 and they are got concur that together form the new ROE. This 456, and 567 stronger neuro ethical for zero it will be like this okay. So this is just call them ways. So that I mean, sorry. This is always that means the.
Speaker Change #103: All of our rules of thumb first matrix and the rules of the second metrics and will be converted to one other metrics.
Speaker Change #103: And this may persist for you it can be easier associate the first mattresses. One two <unk> second one is 456 when redo roadways concatenation rate, we just add on Canada rose at the bottom of the matrix of 123456 people 5567, Okay. Now if we go for column, where he is the addition of Columbia concatenation than what it really.
Speaker Change #103: It will contact the columns on its own okay excellent conquered the columns on it. Okay. So then it will give you our neuro output. Okay. So that's all so what it does is 123345 gets back then because there's this column right now so that will be column ways and also 123 and three or four five will be stacked.
Speaker Change #103: Together 456567 will be stack them together.
Speaker Change #103: So that's how the output at our Port will look like so the columns will be concatenated daughter'll, okay, but that's all of these conquer termination walks in walks yeah. That's out upon consummation walks. Okay. So this is what about this concatenation property mix.
Speaker Change #103: Next is V stack and eight stack for a number of areas. Okay. So what is the stack and what is each stack as the name suggests the vs. Taki's vertical stacking them eight stack is horizontal stacking stacking means the same thing as congratulation winter, but with with just a small difference that we will.
Speaker Change #103: Discuss now so before going into the stack what are you going to do I will show you. Stack example, okay. There is another function called stack N number okay. So I will show that first before going into this a normal thing Mercer HVAC and restock operations, Okay. So what it.
For these two areas on Lee I will consider.
Speaker Change #103: No.
Speaker Change #104: Okay stacking.
Speaker Change #104: E M B.
Speaker Change #104: Great <unk>.
Speaker Change #104: Oh.
Speaker Change #104: So I am speaking, a and B on X is zero okay.
Speaker Change #104: So it is not asking list I should pass it should be double in the year.
Speaker Change #104: <unk>.
Speaker Change #104: Yeah. So this is how they're stacking looks like okay. This is stacking horizontally, okay and if I go to <unk>. One this is stacking vertically mcculloch with green Dot two separate ones might get a quick change I haven't.
Speaker Change #105: Noticed a true change.
Speaker Change #105: It will be 123456 like that no.
Speaker Change #106: Yeah, my sending it out but I was looking for so for horizontal stacking forex physical seeds.
Speaker Change #106: So.
Again that can try with one of the other ones on second.
Speaker Change #106: China is coming from.
Speaker Change #106: So that is what is the road horizontal stacking and vertical second okay. So.
Speaker Change #106: In Oregon, the stacking, we stack to two areas.
Okay, we stack to Aries roadways, and then vertical stacking what rehab, we stack two areas based on we stacked Torres based on columns get to where it's been strong for them. So as you see in the row equals zero. The output is 123 and three four columns or the two one b areas have bottoming.
Speaker Change #106: Bush sequence.
Speaker Change #106: Our roadways Dev concatenated, but when it is going protocol long ways them for one b areas. It is conquered the columns enbridge source. It is concatenated, Colombia, and Brazil Rose rose. Okay. So this shouldn't be pulling things must apologize for the movement in this way of distributing.
Speaker Change #106: So that's how this stacking works so again this.
Speaker Change #106: To be why this problem I am not really sure.
Speaker Change #106: Tampering the exacts indexes what extent it is not working okay. So first stack are we need to have one of the areas. If we need to have more than one be arris to be on cutting knitted. Together then for that we have conquered function. Okay.
This stack function will only taken one the arris into account and it will.
Speaker Change #106: Back those accordingly, okay that is what is our vote stacking.
Speaker Change #106: Now if we go into and this is what is a warm stacking so stacking little mistake on leave one day or into account, but if we need to pull them in generics we need to go for a lake to the areas that we need to go forward Pollyannish mentalities saw mounted tier I am creating a new area like this.
Speaker Change #106: Okay. So if you see this carefully the stack function. It takes in only one be at it and if you're passing it to be at or it doesn't do it.
Speaker Change #106: Anything with that right, except what it does it just created I mean.
Stacked our efforts on Lake offers one of the area a M. B individually was back down to something and that's what got added in the audience.
Speaker Change #106: Now here on the contrary if you see the eight step function that I ever it in at the bottom of the screen at the law that last line. It took in group will be at least two properly stacked okay.
Speaker Change #106: So that is what is the difference between this normal stacking and this each stack N V stack. Okay. If you want to go for a conga function Hong cap actually before us in two ways.
One we actually just only one difference of Banca <unk>.
Speaker Change #107: Okay liquidated so.
Speaker Change #107: Let me do whatever clearly give me a minute.
Speaker Change #107: Jim.
Speaker Change #107: Sure.
Speaker Change #107: Okay.
Speaker Change #107: Mark because Jack.
Speaker Change #107: Okay.
Speaker Change #107: Let's see the differences now.
Speaker Change #107: Now.
Speaker Change #107: Since you know excuse me.
Speaker Change #107: Okay. So for each stack that is horizontally stacking right. So what it did 123234 Concord and SCM grew 456567 comparably in SCM ROE and the return you.
Speaker Change #107: Little new like our desired output there tier one placebo Roberto horizon Blue stack work. Okay. So this fees are always stack, but the rules are concatenated together and toward new column right neuro sorry, So Rosa on carbonated two are new.
Speaker Change #107: <unk> grew and it out put it a different version of our matrix, which is having a different shape. Okay. So if you compare the output of horizon stacking and vertical concrete emission you will see these two are seem to get this to our same thing. So that is a basic difference between this concatenation stacking so stacking.
Speaker Change #107: He has to add up the elements to those same dimension. So when we are at doing a raw waste stacking that is or isn't booth stacking. We are adding the rules element to our raw raw itself, okay, but instead and concatenation the rules that have added up to the entire matrix.
Speaker Change #107: The rules are added up to the entire metrics, but that is the only difference between this horizon stacking and vertical well and amber congratulations again offer the vertical stack, but same thing happened right. The columns got smartest, Dan Jester columns got increased and those elements are stacked into columns. So cool for year, three six or seven.
Speaker Change #107: I've added into the column of Sofar steroids, which was done in the horizontal congratulation right. So that's how this congratulation and stacking Milwaukee and there was a that means for stacking that's backing will be along the axis rich. It is currently working and for concatenation, they will be on getting needed.
Speaker Change #107: On the same dimension.
Speaker Change #107: So that is the difference between these two so at these things and these these four things clear to everybody or them to explain it a bit more.
Speaker Change #107: First of all for stacking doesn't work onto the Aries and four H track these Bakken horizontal concatenation and vertical concrete emission.
Speaker Change #107: Examples that I have written so at these things clears CMS takes months, Okay. Let's Volvo can go with this so horizontal stacking rate what it did it had two areas to work with okay and be okay. So how it looked like how b looks like.
Speaker Change #107: Sure.
Speaker Change #107: Stack forget about it it won't work here.
Speaker Change #107: So this 12345, 6% relate to 123456234567 days to our bedroom was right. So when we asked acting relevantly.
Speaker Change #107: Two rules that is 123 and 234 us back on.
Speaker Change #107: On the rules so want to see to see for our margin in a single row 456, and 567 at a modest gain in answering a little and the resultant output is at an average return. Okay. So that's how this thing once again. This thereafter this stacking was okay now for her.
Speaker Change #107: Isn't the longer duration, but we have we are on getting the rules to the entire matrix. So 234567. These two rules are concatenated to the entire matrix that is raw ways. Okay.
Speaker Change #107: It Didnt for my new role and stood at just back in the rules at the bottom of the older metrics. Okay. A vertical stacking is the same thing, but on the column business and this is now a single column. This astonishing is kind of like that and for vertical concatenation of business same thing as ours into stacked with added those elements in there so that is.
Speaker Change #107: What is around this for operations on this at it. Okay. So is this are you now okay. Next this column Sac and the same thing next year.
Speaker Change #107: Currently.
Speaker Change #108: The snakes.
Speaker Change #108: So the column stack matches with the vertical.
Speaker Change #108: Okay.
Speaker Change #108: Which matches in the horizontal stack so that is the column stacking okay.
Speaker Change #108: That is what is about this column stocking and if.
Speaker Change #108: And that is how it works just a quick info guys. That's generally just data analytics by answering this question, which of the falling method creates a new array object that looks at the same data eight deal be copy C. Based D. All of the above I'm going to answer in the comment section below.
Speaker Change #108: Subscribed into Loopnet in all the right answer now that can deal with decision. So hard is not quite used columns that concept.
Currently so that's our views so that is what is a more function of this column stack. Okay. So we generally use each stack and reach back and not.
Speaker Change #108: Honda that runs okay. So efforts clear really go and do this splitting off areas.
Speaker Change #108: This is a bit tricky one so we're able to do okay. So the syntax ish.
Speaker Change #109: <unk> does syntax Howard tells us what the split function is and B.
Speaker Change #109: N P dot split.
Speaker Change #109: Okay.
Speaker Change #109: Three parameters into account first one will be adding it can be anything next one will be index fish I will explain the next one is access.
Speaker Change #109: The area.
Speaker Change #109: First one can be any numb byetta.
Speaker Change #109: Okay. It can be any name Byetta indices has two pumps either and teachers are odd list. If it is an integer than what it does it takes into account.
Speaker Change #109: Array is split from back very index, which is mentioned as an integer but if it is then like section like an analyst is biased than than that one the area will be nothing like that area will be slated from natural gas slated from back.
Speaker Change #109: So in column based play back so it will be it will be coming due in one way or the less it will be.