From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. As an independent pure play company, Palo Alto Networks has earned its status as the leader in security. You can measure this in a variety of ways, revenue, market cap, execution ethos, and most importantly, conversations with customers generally and see so specifically who consistently affirm this position. The company's on track to double its revenues in fiscal year 23 relative to fiscal year 2020, despite macro headwinds, which are likely to carry through next year. Palo Alto owes its position to a clarity of vision and strong execution on a TAM expansion strategy through acquisitions and integration into its cloud and SaaS offerings. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this Breaking Analysis and ahead of Palo Alto Ignite, the company's user conference, we bring you the next chapter on top of last week's cybersecurity update. We're going to dig into the ETR data on Palo Alto Networks as we promised and provide a glimpse of what we're going to look for at Ignite and posit what Palo Alto needs to do to stay on top of the hill. Now the challenges for cybersecurity professionals dead simple to understand, solving it not so much. This is a taxonomic eye test, if you will, from Optiv. It's one of our favorite artifacts to make the point. The cybersecurity landscape is a mosaic of stovepipes. Security professionals have to work with dozens of tools, many legacy combined with shiny new toys to try and keep up with the relentless pace of innovation catalyzed by the incredibly capable, well-funded and motivated adversaries. Cybersecurity is an anomalous market in that the leaders have low single-digit market shares. You know, think about that. Cisco at one point held 60% market share in the networking business and it's still deep into the 40s. Oracle captures around 30% of database market revenue. EMC and storage at its peak had more than 30% of that market. Even Dell's PC market shares in the mid 20s or even over that from a revenue standpoint. So cybersecurity from a market share standpoint is even more fragmented perhaps than the software industry. Okay, you get the point. So despite its position as the number one player, Palo Alto might have maybe three, maybe 4% of the total market depending on what you use as your denominator, but just a tiny slice. So how is it that we can sit here and declare Palo Alto as the undisputed leader? Well, we probably wouldn't go that far. They probably have quite a bit of competition. But this CISO from a recent ETR round table discussion with our friend, Eric Bradley, summed up Palo Alto's allure we thought pretty well. The question was why Palo Alto networks? Here's the answer. Because of its completeness as a platform, its ability to integrate with its own products or they acquire, integrate, then rebrand them as their own. We've looked at other vendors. We just didn't think they were as mature. And we already had implemented some of the Palo Alto tools like the firewalls and stuff. And we thought, well, why not go holistically with the vendor, a single throat to choke, if you will, if stuff goes wrong. And I think that was probably the primary driver and familiarity with the tools and the resources that they provided. Now here's another stat from ETR as Eric Bradley. He gave us a glimpse of the January survey that's in the field now. The percent of IT buyers, stating that they plan to consolidate redundant vendors, it went from 34% in the October survey and now stands at 44%. So we feel this bodes well for consolidators like Palo Alto networks. And the same is true for Microsoft's kind of good enough approach. It should also be true for CrowdStrike, although last quarter we saw softness reported on in their SMB market. Whereas interestingly, MongoDB actually saw consistent strength from its SMB and its self-serve. So that's something that we're watching very closely. Now Palo Alto networks has held up better than most of its peers in the stock market. So let's take a look at that real quick. This chart gives you a sense of how well. It's a one year comparison of Palo Alto with the bug ETF, that's the cyber basket that we like to compare often. CrowdStrike, Zscaler and Okta. Now remember, Palo Alto, they didn't run up as much as CrowdStrike, Zs and Okta during the pandemic, but you can see it's now down, quote unquote, only 9% for the year, whereas the cyber basket ETF is off 27%, roughly in line with the NASDAQ, we're not showing that. CrowdStrike down 44%, Zscaler down 61% and Okta off a whopping 72% in the past 12 months. Now, as we've indicated, Palo Alto is making a strong case for consolidating point tools. And we think it will have a much harder time getting customers to switch off of big platforms like Cisco, who's another leader in network security. But based on the fragmentation in the market there's plenty of room to grow in our view. We asked Breaking Analysis contributor Chip Simington for his take on the technicals of the stock and he said that despite Palo Alto's leadership position, it doesn't seem to make much difference these days. It's all about interest rates. And even though this name has performed better than its peers, it looks like the stock wants to keep testing its 52-week lows. But he thinks Palo Alto got oversold during the last big sell-off and the fact that the company's free cash flow was so strong probably keeps it at the 150 level or above, maybe bouncing around there for a while that breaks through that to the downside, its next test is at that low of around 140 levels. So thanks for that, Chip. Now, I haven't get that out of the way, as we said on the previous chart, Palo Alto has strong opinions. It's founder and CTO, Nier Zook, is extremely clear on that point of view. So let's take a look at how Palo Alto got to where it is today and how we think you should think about his future. The company was founded around 18 years ago as a network security company focused on what they called next-gen firewalls. Now, what Palo Alto did was different. They didn't try to stuff a bunch of functionality inside of a hardware box, rather they layered network security functions on top of its firewalls and delivered value as a service through software running at the time in its own cloud. So pretty obvious today, but forward-thinking for the time. And now they've moved to a more true cloud native platform and much more activity in the public cloud. In February, 2020, right before the pandemic, we reported on the divergence in market values between Palo Alto and Fortinet. And we cited some challenges that Palo Alto was having, having transitioning to a cloud native model. And at the time we said we were confident that Palo Alto would make it through the knot hole. And you could see from the previous chart that it had, that it has. So the company's architectural approach was to do the heavy lifting in the cloud. And this eliminates the need for customers to deploy sensors on-prem or proxies on-prem or sandboxes on-prem. Sandboxes, for instance, are vulnerable to overwhelming attacks. Think about it. If your sandbox is on-prem, you're not going to be updating that every day, no way. You're probably not going to update it even every week or every month. And if the capacity of your sandboxes, let's say 20,000 files an hour, the hacker's just going to turn up the volume. It'll overwhelm you.  Now, the cloud doesn't completely prevent that. But what it does, it definitely increases the hacker's cost. So they're going to probably hit some easier targets. And that's kind of the objective of security firms. Increase the denominator on the ROI. All right, the next thing that Palo Alto did is started acquiring aggressively. I think we counted 17 or 18 acquisitions to expand the TAN beyond network security into endpoint, CASB, PAS security, IS security, container security, serverless security, incident response, SD-WAN, CICD pipeline security, attack surface management, supply chain security, just recently with the acquisition of CIDR security. And Palo Alto, by all accounts, takes the time to integrate into its cloud and SaaS platform called Prisma. Unlike many acquisitive companies in the past, EMC was a really good example where you ended up with kind of a Franken portfolio. Now, all this leads us to believe that Palo Alto wants to be the consolidator and is in a good position to do so. But beyond that, as a multi-cloud becomes more prevalent and more of a strategy, customers tell us they want a consistent experience across clouds. And this is going to be the same, by the way, with IoT, sort of the next wave here. Customers don't want another stovepipe. So we think Palo Alto is in a good position to build what we call the security super cloud. That layer above the clouds that brings a common experience for devs and operational teams. So of course, the obvious question is this, can Palo Alto networks continue on this path of acquire and integrate and still maintain best-of-breed status? Can it? Will it? Doesn't even have to, as Holger Mueller of Constellation Research and I talk about all the time, integrated suites seem to always beat best-of-breed in the long run. We'll come back to that. Now, this next graphic that we're going to show you underscores this question about portfolio. Here's a picture and I don't expect you to digest it all, but it's a screen grab of Palo Alto's product and solutions portfolios. Network cloud, network security rather, cloud security, SASE, CNAP, endpoint, unit 42 which is the threat intelligence platform and every imaginable security service and solution for customers. Well, maybe not every. I'm sure there's more to come like supply chain with the recent CIDR acquisition and maybe more IoT beyond Zingbox and earlier acquisition. But we're sure there will be more in the future both organic and inorganic. Okay, let's bring in more of the ETR survey data. For those of you who don't know ETR, they are the number one enterprise data platform surveying thousands of end customers every quarter with additional drill down surveys and customer round tables, just an awesome SAS enabled platform. And here's a view that shows net score or spending momentum on the vertical axis and pervasion or presence within the ETR data set on the horizontal axis. You see that red dotted line at 40%. Anything at or over that indicates a highly elevated net score. And as you can see Palo Alto is right on that line just under and I'll give you another glimpse. It looks like Palo Alto despite the macro may even just edge up a bit in the next survey based on the glimpse that Eric gave us. Now that color, those colored bars in the bottom right corner, they show the breakdown of Palo Alto's net score and underscore the methodology that ETR uses. The lime green is new customer adoptions, that's 7%. The forest green at 38% represents the percent of customers that are spending 6% or more on Palo Alto solutions. The gray is that 48%, that's flat spending plus or minus 5%. The pinkish at 5% is spending is down on Palo Alto network products by 6% or worse. And the bright red at only 2% is churn or defections, very low single digit numbers for Palo Alto. That's a real positive. What you do is you subtract the red from the green and you get a net score of 38%, which is very good for a company of Palo Alto size. And we'll note this is based on just under 400 responses in the ETR survey that are Palo Alto current Palo Alto customers out of around 1300 in the total survey. So really good representation of Palo Alto. And you can see the other leading companies like CrowdStrike, Okta, Zscaler, Fortinet, Cisco, they loom large with similar aspirations. Well, maybe not so much Okta. They don't necessarily want to rule the world. They want to rule identity. And of course the ever ubiquitous Microsoft in the upper right. Now drilling deeper into the ETR data, let's look at how Palo Alto has progressed over the last three surveys in terms of presence, presence, market presence in the survey. This view of the data shows pervasion in the data going back to October, 2021. That's the gray bars. The blue is July 22 and the yellow is the latest survey from October, 2022. Remember the January survey is currently in the field. Now the left most set of data there show size of company. The middle set of data shows the industry for a select number of industries in the right most shows geographic region. Notice anything? Yes, Palo Alto up across the board relative to both this past summer and last fall. So that's pretty impressive. Palo Alto network CEO, Nikesh Aurora stressed on the last earnings call that the company is seeing somewhat elongated deal approvals and it's sometimes splitting up size of deals. He stressed that certain industries like energy, government and financial services continue to spend but we would expect even to pull back there as companies get more conservative. But the point is that Nikesh talked about how they're hiring more sales pros to work the pipeline because they understand that they have to work harder to pull deals forward because they got to get more approvals and they got to increase the volume that's coming through the pipeline to account for the possibility that certain companies are going to split deals, split up the deals, large deals and want to split into smaller bite sized chunks. So they're really going hard after the go to market expansion to account for that. All right, so we're going to wrap by sharing what we expect and what we're going to probe for at Palo Alto Ignite next week. Lisa Martin and I will be hosting the cube and here's what we'll be looking for. First, it's a four day event at the MGM with the meat of the program on days two and three. That's day two is the big keynote. That's when we'll be, we'll start our broadcasting. We're going for two days. Now, our understanding is we've never done Palo Alto Ignite before, but our understanding it's a pretty technically oriented crowd that's going to be eager to hear what CTO and founder near Zook has to say as well. C.O. Nikesh Aurora long and in addition to longtime friend of the cube and current president, B.J. Jenkins. He's going to be speaking. Wendy Whitmore runs unit 42 and is going to be several other high profile Palo Alto execs as well. Thomas Kurian from Google is a featured speaker. Lee Claritch who is Palo Alto's chief product officer we think is going to be giving the audience heavy doses of Prisma Cloud and Cortex enhancements. Now Cortex, you might remember came from an acquisition and does threat detection and attack surface management and we're going to hear a lot about we think about security automation. So we'll be listening for how Cortex has been integrated and what kind of uptake that it's getting. We've done some, you know, modeling from the ETR guys have done some modeling of Cortex. You know, it looks like it's got a lot of upside and through the Palo Alto go to market machine, you know, could really pick up momentum. That's something that we'll be probing for. Now one of the other things that we'll be watching is pricing. You talk, we want to talk to customers about their spend optimization, their spending patterns, their vendor consolidation strategies. Look, Palo Alto is a premium offering. It charges for value. It's expensive. So we also want to understand what kind of switching costs are customers willing to absorb and how onerous they are. And what's the business case look like? How are they thinking about that business case? We also want to understand and really probe on how will Palo Alto maintain best of breed as it continues to acquire and integrate to expand its TAM and appeal as that one-stop shop. You know, can it do that as we talked about before and will it do that? There's also an interesting tension going on sort of changing subjects here in security. There's a guy named Edward Haletki who's been on theCUBE before. He hasn't been on theCUBE in a while but he's a security pro who has educated us on the nuances of protecting data privacy, public policy, how it varies by region and how complicated it is relative to security because security is, you know, technically you have to show a chain of custody that proves unequivocally, for example, that data has been deleted or scrubbed or that metadata does, it doesn't include any residual private data that violates the laws, the local laws. And the tension is this, you need good data and lots of it to have good security. The really the more the better. But government policy is often at odds in a major blocker to sharing data and it's getting more so. So we want to understand this tension and how companies like Palo Alto are dealing with it. Our customers testing public policy in courts, we think not quite yet, our government's making exceptions and policies like GDPR that favor security over data privacy. What are the trade-offs there? And finally, one theme of this breaking analysis is what does Palo Alto have to do to stay on top? And we would sum it up with three words. Ecosystem, ecosystem, ecosystem. And we said this at CrowdStrike Falcon in September that the one concern we had was the pace of ecosystem development for CrowdStrike. Is collaboration possible with competitors? Is being adopted aggressively? Is Palo Alto being adopted aggressively by global system integrators? What's the uptake there? What about developers? Look, the hallmark of a cloud company, which Palo Alto is a cloud security company, is a thriving ecosystem that has entries and exit, entries into and exits from its platform. So we'll be looking at what that ecosystem looks like, how vibrant and inclusive it is, where the public clouds fit, and whether Palo Alto networks can really become the security super cloud. Okay, that's a wrap. Stop by next week. If you're in Vegas, say hello to the CUBE team. We have an unbelievable lineup on the program. Now, if you're not there, check out our coverage on theCUBE.net. I want to thank Eric Bradley for sharing a glimpse on short-native notice of the upcoming survey from ETR and his thoughts. As always, thanks to Chip Simington for his sharp comments. I want to thank Alex Morrison, who's on production and manages the podcast, Ken Schiffman as well in our Boston studio. Kristen Martin and Cheryl Knight, they helped get the word out on social. And of course, in our newsletters, Rob Hoef is our editor-in-chief over at SiliconANGLE, who does some awesome editing. Thank you to all. Remember, all of these episodes, they're available as podcasts, wherever you listen, all you got to do is search breaking analysis podcast. I publish each week on wikibon.com at siliconangle.com or you can email me at david.valante at siliconangle.com or DM me at dvalante or comment on our LinkedIn post and please do check out ETR.ai. They've got the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week in Ignite or next time on breaking analysis.


What is going on everybody, this is 1920 obsidian basketball picker team to 12 box random number block 16 teams sold in this one pelicans bowls lakers warrior teats el ticks Etc. Etc. We're gonna roll the dice for the number block 12 times So we got 12 times in this list 12 times on this list remember any unnumbered cards are randomized and one line at the end of the break right there Right there. Let me take this team Put it. All right. Good luck 12 times and Any redemptions go to the zero spot Even if it's a confirmed one-to-one, it'll still go to the zero spot 12 times Matt down to Matthew and now for the list of numbers 12 times zero through nine 12th and final time Nine down to three. All right Matt be nine Leonard to Adam one and six Sean four Adam seven Sean five Matthew shear zero Adam eight and Matthew three So there you go Coming up next is the full case break of obsidian basketball


what's going on everybody welcome back to the tablet tutorial series this is a very last video in the series and today we'll be doing an entire project now if you're watching this video i hope that you watch the other four videos in this series just so you can get the basics down you kind of know what you're doing this won't be a crazy hard project this is a beginner tutorial series so i'm trying to make this super easy so they can follow along nothing super complicated i promise and if you were wanting to go above and beyond just make a lot of different dashboards or try a lot of different things there's a ton of data in here and so i'll show you some of the things that i would do you know as we go through it of the things that i would be looking at and some of the different visualizations that i might do as well but again in this video we're going to be singing to a lot of the basics but i'll switch over my screen in just a second i will show you the final product and then we will actually walk through step by step of how to do the entire dashboard and at the end you should have a completed project that you can add to your portfolio or you know just share on linkedin if you want to do that as well with that being said let's jump over my screen and let's get started all right so let's get me off screen and show you what we're going to be working on today this is the final dashboard that we're actually going to be building and so it's nothing crazy right i'm sure you have seen all of these things before and i'm just going to help you kind of build it out show you what to do the buttons to click and it's really going to be a simple walkthrough by the end of this you should be able to do all these things very easily and i highly encourage looking at the data and looking at these visualizations and seeing what else you can do with it there's a lot of different colors a lot of different visualizations that you can do with this data i'm just showing you this today and so the more you go out there and the more you do this on your own and you mess around with stuff and and choose different things and see how it all works the better you're going to get and so i highly highly encourage doing that so what we are going to be working with today is an airbnb dataset i'm going to show you that in just a second and i'm going to show you the data and we're going to just jump right into it all right so this is the dataset that we are going to be using this is the seattle airbnb open dataset and let's go down really quick there's three different csv's in here and so this is some of the data that we're going to be working with some date on listings and some pricing and then there's the actual listing that shows the actual street address the location the price the bedrooms all of these good stuff and then there's a reviews and it has you know some comments and you know talks about some of the reviews so this is what we're going to be working with but you don't have to go in here and download it i have already combined all of these csv's into one i've put it on the github so i'll have a link below so you can just click on that and you don't have to do all the stuff that i did to get this setup just so you know this is from 2016 so this dataset is a little bit old if you want to you can come right here and i will leave this link as well and you can get the dataset from you know what is this a couple weeks ago uh this is they they are continuing to update this this is always updated and so you can go ahead and download these but some of these are the csv.gz so you may need to like convert it i don't want to go through that process um you know in the video and so i am just going to go with what is literally in kaggle and use that but if you want to have an updated one for your project i just advise you to go in here and grab yourself and that should be perfectly good so go ahead and download the dataset from the github and we should be good to go so this is the excel that i was just talking about this has all of our csv's in one place this is you know an excel workbook so in this reviews actually i'll start with the listings because that's kind of where all stems from we have our listing and the date or the data in here is um you know really extensive there's a lot of data in here so let's get over really quick um the listing refers to the actual home that they're renting out the airbnb so it shows their location um and there's a lot more location information over here i'm getting into it in just a second so there's the neighborhood the city state zip code all stuff that you know may be useful there's a latitude and longitude it shows what type of property it is so that's really good um right over here it has you know how many bathrooms bedrooms and beds um you know sometimes if it's a five bedroom house it has seven beds so that's why there's those two different fields i don't know if you're familiar with airbnb and and you know what they have on there but just something to note they have the price this is the price per day there's a weekly price a monthly price and and if there's a deposit needed and then a cleaning fee as well so a bunch of financial data that's you know super useful we go into it a little bit but there's so much you can do with that um you know if you want to dig into that and that's kind of it the rest of it's pretty uh pretty useless um and there's a lot it's there's so much data in here almost you know more than half by far is nothing you would put in any type of visualization um and this is pretty common uh you're you're not going to get data every column where you're going to be able to use it a lot of times it's just a lot of useless junk and so you have to know what you're looking for and know uh you know what's actually useful so that's the listing then we have reviews now what's really a little bit confusing in here and something that you just need to kind of understand about the data um and something that if you were if you get a data analyst job you need to understand your data because it's very easy to come in here and say okay there's an id id field and here's an id field so that means that those are the same well not in this case um this id field is actually the review reviews id not the reviewer id that refers to like the person this is the reviews id this listing id is the actual id right there so really important to note um and then the lat and so then they just have their comment there what they left as a review and then on the calendar um i don't know why i'm scrolled down uh we have this listing id again so again that listing id is equal to the id in this listing table and we have a date and a price so this refers to a specific location and on this day they got 85 dollars for it somebody rented it out um and so then there's these like t's and f's um let's try to find a blank one really quick here's a blank one so there's these t's and f's uh the t means that it was taken um the f means that it's vacant uh i don't know exactly what it means uh with the t enough means but that we can deduce that much from this and so you can see when and how much this person was making or this home made uh in that time so really really good data in here there's a lot to work with um and so we're just going to be kind of i'll give you a little bit of a use case for it in a second and then we're going to start trying to answer some of those the building out some of the visualizations for that use case uh again you could have 20 different use cases for this data or more um honestly for this data where you could build out different dashboards and different reports literally with just this data but you know we're doing a pretty general broad project and so it's hard to answer all of them so let's jump over to tableau we're going to get started on this and we are going to build out everything all right so let's come right here uh this is a microsoft excel we'll open that up do this one we will open it and give it just a second since it's executing the query it's pulling the data in all right so we have our calendar our listing and our reviews those are different tabs at the bottom we're gonna start with the listing this is the kind of the main one has um you know the there's i didn't show you but there's about 3,600 locations that they had in there let's just have it update automatically i don't know why we need to click on that but so we have this listings we have our calendar and our reviews what we're going to do is going to come in here and we're going to open it as we did in our very last video for the joins so now that we've opened it we can kind of go in here and we can do the joins as as needed and so let's go over here and we're going to let's start with calendar i'll put it right there that was super slow i apologize all right let's wait for it to get the data so it's setting everything up did not think it would take this long i apologize no take your time so let's click on here and right now it has the uh the join based on the price which obviously is not going to work um and if you remember there is no id in this calendar it's just the listing id we can actually look right here there's just the listing id so we're actually going to put listing id is equal to id and right down here we can see that we have a lot of well you can't see it but we show that there is a lot of data and so we know that that is correct we know that that is now pulling in data correctly because it's showing up down here so that's a good thing now in this listings there are about 3600 about 3600 listings and so that all the data that's in listings is going to be in there but on the calendar because we converted from a csv to an excel workbook it isn't able to store as much information so some of the ones in calendar may have gotten cut off so we can just keep it this inner join because we know that if it's in listings is going to be in calendar we know that it if it um there may be some in calendar that aren't in listings so if we really um you know if we really really wanted to we could do a full outer or something like that i i haven't really thought through this as i'm talking through it in my head but we know that everything that's in listing is going to be in calendar and so you know we don't really need to do anything other than an inner join and we can also pull in these reviews and it's going to do the same thing as before we're just kind of pulling in the data and it defaults to id equals id now we know that that is not correct um because the id in here is referring to the review id we need to go to the listings id so we need the id be able to look you know be part of that listings id if we do the id it goes down to 2555 rows if we do how it's supposed and there because that's just you know it's random luck there happen to be some numbers that are in both fields um that tie together if we do the correct one where we hit the listing id it bumps it up to i think two million three hundred and seventy three thousand oh maybe more than that uh 23 million rows right a lot lot lot more and so it's super important to get these joins right to tie them together on the right fields if you just do it based off what Tableau tells you because it has that automated um you know it goes into these fields and says okay these are the same exact column name so they're most likely going to be what you're looking for well it was incorrect in this point so it's really important to check those things and make sure you're pulling in the right data again we're going to keep it that inner join um you know if you wanted to you know try to see if there's any other data that correlate or keeping it simple today but sometimes you need to join in multiple things uh so just uh uh you know a tip so let's get out of here um and we are good to go so this is our listings plus Tableau full project that's what we'll that's what we'll be working with um and we we were able to tie all three of these um you know as you call them tables or sheets or whatever you want to call them we were able to tie them together so let's go over here to our first worksheet uh and let's see all right so this is Tableau public only works with less than 15 million rows of data we have 23 million rows of data that is uh that's a problem um and when I did this before it didn't do that so I you know we're going to work through this together so this is date reviews I believe this is date for um this is date for the calendar which is going to be a lot of rows of data and so I'm sure that's part of it let's see let's do years should we only want 2016 oops we only want 2016 let's do okay let's see what that does let's see if that gets us under what we need um we only want 2016 data anyways so if it's in 2017 we were going to take it out um anyway so we'll see if that gets us underneath I have absolutely no if this ends up taking like 20 minutes I will just cut it and you know you won't have to wait as long as I'm waiting so let's see how long it takes all right so it took about 20 minutes and it absolutely nothing um one thing I do know is that we don't actually use this review tables at all it's just for demonstration purposes so we're going to remove that and let's see if that helps us in any way because if it does we're just going to keep it as is um you know the reviews table is really just for demonstrating how to do the joins but we're not actually using any of the data for any of the visualizations although you could again I'm going to see how long this takes and I'll cut ahead all right so that worked perfectly it apparently took out all the data that we needed all the rows that we needed to get under that level again I was just doing that to show you the that that joins how you need to change the columns to make sure that it joined properly we don't actually use it for any of the visualizations so their end product is going to be totally fine I don't know why this didn't happen to me when I when I created this whole thing already um so I'm just going to move forward because I make mistakes so let's keep moving the first one that we were going to make is that uh is that colorful one I'll probably pop it up on screen so you can see it well if I remember I'm going to pop it up on screen um it's the colorful one it's the price by zip code so we're going to be looking at these zip codes and kind of see um you know how expensive is each zip code um and before we actually start I just remembered I want to talk to you about the use case for this data I want to imagine you to imagine that you're working for something they're like hey where you know I I want to start an Airbnb business I want to know where I should go where should I buy a buy a home put it up on Airbnb and start renting it out where's the best place you know what are some of the factors that I should be looking at uh and so that's kind of what our use case is so we're going to the some of the things that he cares about are things like bedrooms um location which is really important and how much price he's actually going to get how much money can he charge and so he's trying to optimize that to make sure that whatever rental he gets he can make a the most profit from instead of choosing something that you know he thinks would work but you know in the end he's actually not making that much money so those things are important so that's our use case we're trying to help this guy out help him find a really good Airbnb um so let's take a look at these zip codes real quick we have uh quite a few of them and there's one that's null uh we'll exclude that or if it doesn't have zip code we'll just exclude those because they're not going to show up on them these visualizations anyways um and so we want to look at the price so we just want to find the price which should actually be down here and not the sum uh no we want to look at the average price and let's order that this is great um so this is the most expensive one uh zip code 98134 at 206 dollars uh per for the average price but let's give that some color really quick let's uh that where's the zip code it's up here so let's take that zip code we're going to put it right over here we're going to do color and it's going to give it some uh assorted colors now these colors are going to um when we do the map it just a little bit these colors will um match what we're doing in there and so you know I like to try to color coordinate things um we're not doing going too crazy with the colors today so this is our very first visualization congratulations it is uh it is complete so uh we can label this one and we can just do price by zip code and I'll make that bold I don't know I usually like a bold we'll apply we'll do like that and boom first one is done and this is our starting place to say hey person who's looking to buy this Airbnb here the zip codes where they are able to charge the most um for for their Airbnb so let's go over to the second sheet and we're going to be doing the map and so um map is pretty easy but it's pretty easy once you actually get the data that you need although there's a lot of different data that you can use for the actual map right here you need something that shows um the location and there's a lot of things that show location here in fact they already um provide a latitude and longitude and then at the bottom they generated a latitude and longitude from from some different fields and then there's just a bunch of different um state there's um states there's zip codes there are uh I think another one yeah like country there's a lot of location data in here so which one do we want to use we want to stay consistent we don't want to deviate from that and start using different um longitude and latitudinal coordinates because that could throw off our results completely we want to stay consistent with what we're using so we actually want to use this zip code but when we pull it up here it's going to give us basically the same um you know it's going to show these zip codes but we are going to right over here we're going to click on this one and now it's going to separate them out so now we have all of these um you know kind of separate it out what you might get when you first do this um is it might look like this you may have to zoom in um I know that that happened to me the other time excuse me go to here that's what happened to me uh just when I first did it so uh know that that may happen and we want to change the colors the exact same way that we did them before so we're just going over here we're doing color and these colors do um they do should match up with the um with the other ones let me um exclude this let me see if it does nine eight one three four that's the blue and right over here nine eight one three four it's a blue I I believe they are going to be the same yep and so just scrolling back if you look at the zip code on the far right uh they are the same so if you're looking like this section right over here I just wanted to make sure I'm not going crazy uh before I get into this and realize I'm not correct at all so uh now what we want is you know this doesn't really give us any information if I was just a glance at this map I would have no idea what you're trying to show me um any information off this so we want to show some actual information so first thing that we're gonna do is we're gonna actually add the label to this so that you can see it you know when you're going over here and you see okay here's this um zip code um in the dashboard when we create it you can't click on this but if you just want to do it visually without having to click anywhere you'll be able to see okay nine eight one three four that's right here so this location right here is you know able to charge a lot of money it's probably a really nice neighborhood so um and we can back that up by putting the average price so these these two visualizations are really they really go hand in hand now we're gonna add oops not the sum this one needs to be the average so you go to this measure the sum go to average and there you go and these should match so this should be 206.6 um I'm looking at the average price right here and then we go over here nine eight one three four 206.6 so this all matches um and we can uh we can actually change that size a little bit if you want to actually get it in um get it within each of these things you know adjusted as you see fit I think that's fine right there um no need to mess with it anymore all right so let me see I think that is everything for this one I don't know if I want to add anything else uh no I'm gonna keep it out of this so that is our second visualization again these ones are directly uh correlated and you know this there's just different ways to visualize that this one you can see actually on the map where it is and the average price this one you can see from highest to lowest so again you know sometimes when you're doing these visualizations you're going to have these accompanying um uh these accompanying visualizations in your dashboard that's very normal so let's move over to the third one and for this third one um you know something that our guy was looking at is he's like okay well you know I'm thinking about listing it on Airbnb but I also want to live in it so I want to know the best times to actually you know put it on the market for people to be able to use and so I was like okay man no problem uh let's let's take a look at when when are people spending the most money in Airbnb's and we actually have that calendar um if you remember let's look let's see this calendar so you have this available the date the listing all of that stuff um and let's look at the date in here uh and we obviously don't want it like this we want it to be more uh more of a time series and we're going to be doing that based off of the price for the calendar so let's go see if you can find that really quick okay here's the price where is that calendar one let me see okay there's the calendar oh here all right totally forgot what that was supposed to be oof that looks terrible okay um let's see let's let's start working on this because this needs some work obviously uh this is the worst visualization I have ever seen um so we need to work on this a little bit what we need to do is we need to change uh whoops we need to change some the way that these dates are are seen so right here is these are two separate things so if I go right here and I do by quarter it's just going to change the quarters here right that's it isn't really helpful we actually want to keep the year here what we want to do it is by year we want to separate it by year but we want to separate it let's just do I don't know let's try week and see what it looks like okay this is great this is this is what we're looking at again um if we went back and change just like quarter it uh changed a quarter and then change it to week it would show the quarters but it wouldn't show everything right this isn't all the data that we need and so you know you really need to make sure that you're doing this correct I it's on by default it's almost always year but if you're looking at it via quarter so like let's say somebody comes in you say hey what quarters I want to break these out by quarters um in that year over year that's how you would do this but in the year we want to break it out by the week and you see this huge drop off at the end well that is actually because the data doesn't go past that there's just like one day of data or one one week of data in here with actual with January of 2017 data so it just drops off because this is an as the sum so it only adds up to like um 591,000 compared to like the 2 million so we want to get rid of that um and how do we do that uh let's see I think it's filter bup bup bup um as a format no it's not format what am I thinking bear with me uh let's see a filter well I was looking for it I just couldn't find it uh let's bring it back to the 31st let's see if that fixes what we need perfect uh that's all you had to do um and the reason that this is helpful and oftentimes you'd have several years worth of data in here um and then you could have you could even do something like this um like this one where it has multiple lines the reason that this is helpful is because if I'm telling my friend let's I mean just I'm going to say it's a friend uh or business partner whatever you whatever you want to use this use case for I'm going to tell him hey the beginning of January all the way until like you know even February it's like really low it's half so there's not a lot of people traveling because everyone travels when at the end of the year so in November December for the holidays to visit family um and then in the summer for vacations I would tell him just based off this one thing I would say hey over the summer and then in the end of the year and during the holidays that's when I would be renting out your Airbnb okay so just this one very simple visualization can help him understand the best times to do that that may have been intuitive you may have already known that but you can prove it with the data which is always really helpful and let's see is there anything else that we need to do with this uh I'm just going to label it and I'm going to say um revenue for a year just do bold do apply there we go do I label this last one I didn't let's label that last one and we'll do price per zip code price per zip code we'll just keep it that just keep it simple um and let's do that all right I believe we have two more so we have done um we've done three of them um we got the zip codes we've got the you know the time of the year now something else that he was wanting to know is um you know just how things affect it and something that's going to affect the price of the actual Airbnb is going to be the amount of bedrooms so the the larger the house the more bedrooms the more it's going to cost typically so we can take a look at that let's pull in these bedrooms and that will be our columns uh no won't what we need to do um and so I I knew this is going to happen I just forgot it until right until right now what we this right now is actually a um it's a value right so it's a number and that's totally reasonable because if we go right here you count distinct that's because there's only seven values right it goes there's zero bedrooms one two three four five six seven all the way up to seven bedrooms right now it has it as a numerical value we want to um change that to create it as um these measure names not a value so we're going to um we're going to remove this we're gonna go right down here and click this drop down and we're gonna say convert to dimension and so now we're going to add it as a dimension so there that looks um much more normal I really quick I'm gonna I'm gonna keep these in here for a second but we're gonna get rid of these nulls and zeros because if a home has zero bedrooms that's a problem um and so we want to look at the price again let's go down here in the listings it should be the price now this is the price for the location per day if you want to look at monthly or or you know stuff like that they have that data but we're just going to do the price the average price not the sum although this is helpful so just really quick before we change it this is going to show you which ones make them which ones are bringing in the most money it also may show you which ones are the most common those are all different visualizations that we can do but the one that brings in the most money that brought in 63 or that has 63 million dollars worth of um worth of listings so they all add up those one bedrooms are doing phenomenal half of that are two bedrooms at 30 million three bedrooms at 18 million and so on and so forth so there's a ton of one bedroom ones we may even keep we could even keep that in there um you know if we wanted to um and then we do something similar later but you can keep something like this in there well we will do really quick those we're gonna do the same thing that we've been doing is keeping average um and we are going to get rid of this because if it doesn't have the bedrooms you know that's not helpful to us and if it has zero bedrooms that's that's genuinely a problem i will not be renting an airbnb with my family that has zero bedrooms in it so now we have this and would be really helpful to be able to see that in the visualization i mean it's just kind of hard to see it as is i mean it just does not hurt to add that right here do a label um why is it angled like that maybe i just need to move it out more that looks much better um that's the average price that cannot be right that's the sum that's why so let's go over here let's make that average as well much better because uh if the price was three million dollars for a three bedroom i would not be going there so this is really really useful information for our friend right if he wants to you know get into those one that one bedroom area you know you're not gonna be making a lot of money and maybe low cost up front but he's not gonna be making a lot of money it significantly goes up when you reach these five and six bedroom homes which makes sense i mean if it has five or six bedrooms in it it's probably a really large really nice home and you can charge a lot more money and our friend is extremely wealthy he can buy whatever he wants and so he may be looking at these larger ones seeing that there's a much higher return on his investment the higher and the more bedrooms he goes so we're gonna keep it just as it is and let me see if there's anything else that we want to do with this no we're gonna keep it just like this and the last one is by far the easiest and we actually just discussed it a little bit we want to know you know what's his competition look like so for those for the bedrooms specifically so let's go back up to the bedrooms we want that one to be right here in our rows so we show these and then we just want to count of how many listings there are so we can do that via the listings id so here's our listings each id represents one location or one home so we're gonna do that right here that looks absolutely terrible that looks terrible what am i doing wrong here oh let me see one thing we need to do is we want to get rid of these nulls and zeros do that really quick um and then we don't want to do just the id because i i'm realizing now uh what i'm doing i need to convert this to a numeric so we can do a count on it so let's um oops let me see what what is happening this is terrible all right let's put this back let's make let me see if i can just um do an attribute let's do the count and let's do text um no it needs to be a distinct count because that's that's basically like a count of the numbers themselves not each individual id okay it took some figuring out i'm gonna keep that in there because you guys need to see a lot of you guys like seeing when i make mistakes so you know it makes it feel like when you make mistakes it's okay um and i'm all about that so i'm leaving that in there you guys can see me fail a little bit um i just forgot how to do that for a second and this is exactly what we're looking for right we want we now it showed us in that visualization that we were looking at earlier before we um switched it to the average price this is showing us that there are for one bedrooms there's 1800 one bedroom two that 483 three that 206 four that 55 only five that 20 and six that have five so the more you go up the less and less it is or the less and less competition there's going to be now is there a lot of demand for four bedroom five bedroom six bedroom uh that's for our friend to figure out um well maybe we'll help them out with that later um in the with the data you know we could look at the reviews that we had um there's so much data in here and we could absolutely figure that out but for what it's worth we're giving him this initial stuff and he'll have follow-up questions for us later that's how it always works i promise um so now we're good with this one let's label this one did i label the last one i will go back and look um distinct i i'm gonna butcher this one i'm gonna do a distinct count of bedroom listings i don't that may not make sense at all but we're keeping it so we're gonna do bedroom apply okay let me see if i added the label on this one i didn't let me do that real quick do average price per bedroom again i'm oops you didn't see that i'm just going with whatever is coming to my head this probably wouldn't be what i would keep if i had this or like an actual project but it works for now so we have our five visualizations one two three four and five and let's create our dashboard that's going to be this button right here so we're going to click that we are going to uh go right here i'm going to say automatic because we want to use this entire area and so now we're just going to start um you know pulling them over and i'm just going to start from the very first one and go to the very last one keep it really simple so this very first one we'll pull it over it you know it's going to take up the entire space until you start adding all the other ones we'll include this one right here um and well let's leave it as it is you know we'll adjust it once it gets to its final place now we have number three we'll add this one on this side it looks terrible right now but give it a second uh and then we have number four we're going to add that across the top okay it's already starting to look a little better and maybe i you don't have to keep this in here um but you definitely can let's start to adjust things a little bit to do okay let's see if i can zoom in one more no i'm gonna do it just like that actually let me see if i can make it even just a little bit closer perfect uh that's the best you're gonna get um if you didn't see i use this um magnifying and then i could click on the area that i wanted to see so we're gonna keep that just like that we're gonna move this over because that is um definitely not as important um and then we're gonna move this way over as well keep it just like that again this is something where if you want to you can click on this um it didn't i don't know why uh i can't remember how to get this connected but you definitely can um but oh okay i was just clicking on the wrong one that's why that is why but you can click over here and you you know it'll filter based on so if i go to this one oops oh geez what am i doing oh this is a travesty okay let's try to get this back all right i'm not touching it guys you get the gist you can mess around with it yourself i'm not messing this up okay so the next thing we need to add is the very last one let's go right up here and then we're just gonna kind of move it off to the side and and let's see i'm gonna add yeah this caption um if you've never seen something like this before um and i actually want to make this bigger as well geez give me a second it's it's kind of lagging a little bit and make this a little bit man maybe i don't want it as wide but i definitely want it a little taller give it a second yeah let me scooch this back just like that that's fine we can keep it like that in my original one i didn't have this you can get rid of this if you want you know you can um you know just exit out right here if you want to do that but there you have it uh this is the entire thing so we started from the very start um we started with this one and this one uh did some um and this is you know all the zip all of our zip code work then we took a look at the calendar where we looked at the price and did some time series visualization and then we're looking at the bedrooms and and the count of bedrooms and so this should be really helpful for our friend it should be an initial dashboard to get him going and once he sees this he's gonna have a million other questions and he's gonna want another dashboard for different data that's in there he's gonna ask about okay well what if i want to do it weekly or you know i want to rent it out for the month or you know how many reviews are people five star reviews are people giving on you know when bedroom two bedroom three bedroom these are all things that you know he may ask and then we'd have to build out in the real world this is what happens all the time you know they make a request and then they're like oh this is great but i also want this so um you know your friend is is going to be right in line with just about everyone else um that has ever gotten a dashboard for work or for personal use with that being said this is it we have done the entire thing now if you want to share this it is super super easy to share and i'm going to try to remember how to share it so we're going to do it save to table of public as and we're going to do this and we're going to make it um let's do air b and b is it like is it a capital b so like that no that doesn't look great air b and b uh we'll do full project and we'll save and that is being created right now um and i will save this so if you guys want to go look at this you can um and i'll provide a link in the description as well for that and see if yours looks um similar to mine or better than mine give it a second because it's thinking all right so here it is so here's our final our final project um and if you followed step by step then you should get this exact or very very similar to this one again i encourage you to if you want to have the up-to-date data to go to that um link in the description that has um the the most recent data and they update that i believe monthly so you can go there get the most recent data and then you can do stuff and you can create a beautiful project just like this um but with the you know the most recent data again i use the kaggle data just so you guys can remember and i encourage you to look at the different data points that are in the excel there's so much in there and you can use uh honestly like there's probably 30 or 40 other fields that you could be using in there that we never even touched um but for this project we're keeping it pretty simple and so go do that make completely unique dashboards and visualizations and create projects and add it to your portfolios so you can create a fantastic portfolio website and get a job and that's what this is all about it's about upskilling and and getting these skills that you can you know get a job or do better in your job so i hope this has been helpful i really appreciate you guys joining me and and doing this entire project with me i have no idea how long this is probably this could be like an hour for all i know um so thank you so much for sticking with me this entire time if you like this video be sure to like and subscribe below and i will see you in the next video


purple Mr. Nice. I know that went up in your area. Did you, did you grow any of that? Yeah, purple Mr. Nice is a little weird in the sense that it, it's got G13, it supposedly got G13 in it. Yeah. You know, and it's one of those things that when, when Neville lost, when Neville went to jail for 11 months and then since he got him out of, since he gave him a bunch of money and got him out of jail and bought his seed company, some of the things that were common in Neville's seed catalog didn't pour it over. Yeah. To Sensi, like any of the G13 hybrids, any of the hash plant hybrids, the maple leaf all disappeared. Yeah. And then in the late 90s after Neville left, the maple leaf, the G13, the Afghani one, all those things started popping back up. Yeah. And I don't know if they pulled them from seed, but I think that's where it came from. Yeah. Is Mr.


Derek van Schaik is a YouTuber whose channel has just exploded recently because of his body language videos. And in this video, we're going to talk about how Derek van Schaik just exposed the YouTube community for its hypocrisy. What is up everybody? This is Chris from the Rewired Soul where we talk about the problem, but focus on the solution. And if you're new to my channel, I talk about all sorts of stuff, but mainly what I try to do is see what we can pull from different topics in the YouTube community or pop culture and see what lessons we can learn from them to improve our mental and emotional well-being. So if you're into that stuff, make sure you subscribe and ring that notification bell. So yeah, YouTube has just been throwing Derek van Schaik's videos at me. He's a great YouTuber. I do want to start out with a disclaimer. Derek van Schaik is a great YouTuber. I love what he does. I am very much into body language. I read a book on body language from this FBI expert in body language. Absolutely loved it. I do think that if more people understood body language, it would help with their social anxiety. So I absolutely love what Derek van Schaik is doing, all right? So those of you who are unfamiliar with his channel, his channel has blown up recently. Last time I checked, he had like over 250,000 subscribers. I think when I first came across his channel, which wasn't that long ago, he was only at like maybe like 30 or 40,000. So it has just exploded. He started getting a lot of traction with his Justie Smollett videos, but he's made videos on Chris Watts. He also did a bunch of videos like I did on the Shane Dawson series on Jake Paul. Most recently, a video of his got recommended to me and it was another one that he did about Jake Paul, talking about how Jake Paul is addicted to lying, all right?  But it's something that I want to talk about. And here's the thing. Here is the thing. Like people are going to dislike this video and that's cool. People are already disliking the videos. I've lost tons of subscribers. Like it's cool. I'm going to talk about the things that I want to talk about, but I am just always fascinated with human behavior, the way we think, the way we act and how so many of us do not see the similarities between things and it blows my mind. Like something I've always been trying to do with all of you is showing you the similarities you can take from YouTubers and what they're going through into your own life and try to use those as lessons, right? And formerly I was talking all mental health all the time and I've kind of rebranded a little bit where mental health is still very much a part of my channel, but I'm doing more commentary videos and everything like that. Here's some commentary. My commentary is with the explosion of Derek van Schaik's YouTube channel, it really shows the hypocrisy of what's going on, not even just in the YouTube community, but just in the world in general. So those of you who don't know about body language, right? So body language, what is this? Body language is a technique used to see what this person is really thinking, really saying or really doing. Again, I love Derek van Schaik, no shade towards him, all right? And I hope I'm saying his name is right. But anyways, like it is a technique used to help determine if a person is lying or if they're scared or if there's nervous or whatever. And like I said, the book I read, it was an FBI agent and there's many stories in there about how he caught people or knew which questions to ask based on the way the person's body language was. But here's the thing. Here's the thing, everybody. And Dr. Todd Grande even did a video about this. But here's what you all need to understand. Body language reading is a pseudoscience, okay? It is a pseudoscience. For those of you who don't know what a pseudoscience is, it is not an exact science, okay? That's why I refer to it as body language reading as a technique, okay? But it is not an exact science, which means is that it is not evidence-based. So the issue that I'm having is, and by the way, again, this isn't to defend myself or anything like that. People are going to think what they're going to think. People are going to believe what they're going to believe. People are going to dislike me if they're going to dislike me based on videos and everything that's made. But the thing, the topic I wanted to bring up is we hold mental health in this precious, delicate little rose. And if you use that word, oh my God, right? But I'm just blown away by the hypocrisy. Derek Vanchic, in his videos using a pseudoscience, is claiming that he can determine if a person is lying based on videos of that person, all right? His videos are very entertaining and he kind of jokes a laugh. I'm like, again, he's a great YouTuber. That's why his channel's exploded. He has a great personality. He's very charismatic and everything like that. It kind of draws you in.  Then on the other hand, you have a channel like mine where I have used clips. I have used clips of people saying their diagnosis. And then I repeated what they said after showing their clip and then further explained that diagnosis as based on science and research from people that are a hell of a lot smarter than me and that is taboo. So one thing that I talked about with Dr. Todd Grande in a video I made with him not that long ago was he was like, I think the line gets crossed when you're assuming someone's thoughts and their intentions, right? But why is it that we hold body language reading channels like in this amazing light here on YouTube when that is a pseudoscience, right? Like we can look at something like hell, I'll throw it out there. We can look at something like borderline personality disorder based on the symptoms, based on years of research and studies. We can say, oh, a person has this diagnosis. Here are common characteristics of somebody with borderline personality disorder based on research and studies and science. You are not going to find the same amount of research when it comes to body language. So why is it okay to say based on body language, we can say that that person is lying, calling that person a liar, but over here, we can't talk about what somebody vocalized. Again, we're holding mental health in this very precious state. And something that's one of my missions in life, I've drawn back a little bit on it here on this channel because it seems like the world isn't ready for it yet. But one of my missions is to change the way that we're talking about mental health. The stigma is not going to go away as long as people are saying, no, no, no, no, no, no. You can't talk about this. Like, but we're constantly accusing people like, here's the thing. And I made this video because a few comments really bug me from some old fans of the channel. Here's the thing, with all the videos ever made about me, I still have never diagnosed anybody. And I know what you're thinking, I know what you're thinking. You're saying, oh, there is that one. I'm not going to mention the name. I'm not going to mention the channel. You're going to say there is that one. But again, I've never said it unless there was evidence of the person saying it themselves. So the one you're thinking about, that was based on dozens of videos talking about that diagnosis. So I want you guys to sit back and just really ask yourself, see if you can set aside your anger, your bias, your hatred, everything and ask yourself, why is it okay to use the pseudoscience of body language reading? But it isn't okay to talk about a mental illness that somebody says they already have. Because the argument is, and this has come up a little bit on the iNabber video that I've done, is that people don't like it. Well, think about that for a second.  They probably don't, right? So something I was talking about in my iNabber video is, is that there's all these blurred lines for the commentary community. Why are channels making commentary not allowed to use direct quotes of somebody saying they have something or something they're going through? Why is that not okay? But it's okay to use a pseudoscience to claim that somebody else is lying, right? Like, and, and I'm not, again, I'm not going to name names, but I've watched a lot of body language channels just because they intrigue me. But I've seen body language channels where there was a clear bias. There was a clear bias when that video started. And if the person reading the body language has a bias, they're going to be more prone to spot things that they assume makes that person a liar. And that is one of the issues with it is, is believing that this is some kind of lie detector test. And the reality is too, like, I don't know if you've ever seen like the series on Netflix, what's it called, the confession tapes or something like that. But what everybody needs to understand is even lie detectors are not 100%. Lie detectors can be faulty. And this is because if a person believes something to be true, a lie detector isn't going to pick that up. So somebody, for example, believes they committed a crime, even though they didn't and they take a lie detector test, it's going to show that they committed a crime. That's why lie detector tests are not scientifically proven to work. All right. But anyways, those are my thoughts. I wanted to talk about that because I was, I like talking about stuff on my channel. I want to hear from all of you. What do you think is the difference between Derek Vanshake and other body language channels accusing people of lying based on a pseudoscience and why you think we're not allowed to talk about mental health subjects? All right. Anyways, that's all I got for this video. If you liked this video, please give it a thumbs up. If you don't like it, you know what to do. And if you're new to this channel and you don't mind sticking around, make sure you subscribe and ring that notification bell because I make a ton of videos. And a huge, huge thank you to everybody supporting the channel over on Patreon. You all are really, really amazing. I love you so much. And I'll see you all next time.


Yo soy el llama. The motivation for the Escuelo Latinoamericana de Matemática aplicada, el llama, arose from my own personal experience. When I decided to study mathematics in Argentina, my family and friends thought that mathematics was a really limiting career choice. Fifteen years later, I feel this could not have been further from reality. In Latin America, the utility and importance of mathematics is not always apparent to the general public. The aim of the school was to showcase the value of mathematics in an effort to begin changing the mentality. With this in mind, we chose Ecuador as the host country and applied mathematics as the topic of our school. Students who were mostly undergraduate and master students came from Ecuador, Mexico, Guatemala, Peru, and Colombia with varying backgrounds in biology, computer science, engineering, physics, and mathematics. The school, which lasted two weeks, consisted of theoretical lectures and laboratory sessions where students could practice coding in different languages and running simulations with real data. The courses and lab sessions took place at Universidad San Francisco de Quito. In order to convey the universitariality of mathematics, a diverse range of applications were highlighted in the topics of the courses fluid mechanics, cancer dynamics, control of epidemics, and machine learning. Next, let's meet our instructors. Yes, so I'm Jacob Petrosian. I'm a professor of mathematics at University of Maryland, College Park in the United States. My research is in the mathematical analysis of differential equations, usually those arising in fluid mechanics or plasma physics or similar physical situations as this. So the course I'm teaching here is kind of an introduction to mathematical modeling using differential equations. So I introduce mostly mathematical biology examples, both ordinary differential equations and partial differential equations show a little bit of how to analyze them by hand and also how to simulate them numerically. And then in laboratories they do numerical simulations, they see pictures, and we discuss kind of what the dynamics are. So the emphasis is more on the dynamics than on any particular fine theory point. And I think this is a good place to start learning differential equations, even if you do want to do theory later in life, because you really need to understand the dynamics. And the focus of mathematics and applied math is understanding really the predictions and the limits of the models and what kind of dynamic goals, behavior you can see from models. Hi, my name is Doran Levy and I'm a professor of mathematics at the University of Maryland, College Park. My expertise is in mathematical modeling of biological and medical systems. I am here in Keto to teach a short course on mathematical methods of cancer. We cover a lot of things starting from cancer growth, cancer immunotherapy, cancer evolution, and many other things. Essentially providing tools for everyone to start thinking about how to do research in this area and what kind of questions researchers ask. It has been a great experience, and I hope that everyone likes it. Thank you. Hello, my name is Maria Soledad-Aronna. I'm from Argentina, but I'm a professor in Brazil, Rio de Janeiro, in Fundación Jutulia Vargas. So I'm here in Keto, Ecuador, to give a course on optimal control with applications to biology. In particular, I will do some applications to disease control. So, my name is Rafael Coby and I work at the São Paulo State University. I'm a researcher there. I work at the high-energy physics group, which is called São Paulo Research and Analysis Center. We are members of the CMS collaboration at the LMC, and I work during machine learning analysis on mainly particle physics, high-energy particle physics. I'll be giving the course here on Introduction to Machine Learning. So I'm going to talk about the very basic topic of so much in learning. So we're going to discuss the idea behind the AI and what actually is being done right now. And during the lab sessions, I ask the students to play a little bit with machine learning tools on data sets. Most of them, they come from the real world, especially the ones that we use at their data from particle collisions and so their high-energy physics data. And I ask them to do manipulations and maybe try regressions and so on and so forth. In an effort to engage the general public, the lecturers of the courses were also asked to give a series of public lectures at La Escuela Politécnica Nacional, the titles of the lectures were Patterns and Chaos, Vortex Motion and Fluid Mechanics, Mathematical Modeling, the role of the immune response in leukemia, the mathematics of epidemics, machine learning, the industry and the basic science. To close the school, we held a panel discussion for students to ask questions about applying to graduate programs around the world and provide them with information regarding broader career opportunities in mathematics. We hope that the experience was beneficial and that there will be similar activities in the region in the future. Hello, my name is Nicola Zapata. I am from Ecuador and I am a physicist and a mathematician from Universidad San Francisco de Quito, the host university of the school here in Ecuador. I really have a lot of fun in these two weeks and I really enjoy the courses, I learn a lot and the lecturers were really, really good. So for me it was an amazing experience. And also I would really like if you continue to do these type of schools here in Ecuador and in Latin America because in that way we learn of different opportunities for doing our masters and our PhD outside of our countries. Hi, I am Esteban Murillo. I study mathematics at the EPN in Quito. I attended the First Land American School for Applied Mathematics. I really enjoyed the experience. I liked the topics that were taught to us and I met new people who I could collaborate with in the future. In the future I'd like to see more theoretical topics maybe, but I really liked the experience. Hello, my name is Juan Zurita. I am from Quito, Ecuador. I study biotechnology at Uesificu and I am 22 years old. What I really like about the course is the attitude of the teachers because they all had a lot of patience and taught us how to write code. And specifically in Matlab I do not have so much experience and they really helped me out. And I also love the contents, the diversity of the contents. It's really cool to be part of that kind of diverse environment. I would really love to see some mathematical models for the expression, for example in Applications to Synthetic Biology or maybe some mathematical physics. Hi, I am Ivan and I am from Guatemala. We went to the First Latin American School for Applied Mathematics at Quito. We learned new stuff such as light and cancer dynamics and fluid mechanics. We want to thank the ICTP for letting us be part of this amazing experience. And also thanks to Chia, Matias, all the teachers and the rest of the organizers that were involved in this activity for teaching us and letting us be part of this amazing journey.


Unencumbered by patents video solution, so we're pretty happy about that and of course you all know about Android Which is our open source source operating system for mobile devices, which I think given its rapid rise Is really demonstrating the power of openness on mobile Platforms so so so why would we actually do all this it seems counterintuitive? But it's clearly not altruistic It makes a lot of sense to us It's an open internet that creates a steady stream of innovations it attracts users and usage and frankly grows the entire Industry we've got an alternative business model a set of advertising applications that Gonna help us monetize the overall overall growth of the platform as it gets bigger We're not the only ones who are doing this looking beyond the kind of traditional model of software licensing And that's what software as a surface service basically is all about But the end result is a is really a collaborative effort to make the internet a bigger and a much more exciting place by the common embrace of Open standards and interoperability that's something that Commissioner Cruz. I think covered pretty clearly I was going to point out that her quote from a couple years ago that you've been choosing open standards Is indeed a very smart business decision? And I think we couldn't agree with that any Basing our developer products on open standards wherever they exist and wherever they work well It really matters because interoperability is critical critical element of consumer consumer choice The Commission's digital agenda recognizes standards as one of the one of the key focus areas and you heard Commissioner Cruz Go through that so it should be pretty clear by now Right, you know, it ought to be an aphema for any government organization Intermediate into procurement decisions You know, they shouldn't be choosing things that will lock them in to proprietary standards seems pretty obvious And Europe can and should contribute by ensuring the government's use open standards And and strengthen the open standards against abuse so I very much welcome that the Commissioner showing leadership again in this important area and They're going to issue a communication to guide the public authorities on these issues Finally open networks This is something Europe. I think it's got pretty right and most you EU countries actually have quite a bit more competition than we see in the US and that competition means greater adoption faster connections I always Enjoy coming to Europe because unlike, you know, driving from San Francisco to Silicon Valley on the major thoroughfare The most technologically advanced place in the world


Hi, I'm Rye Jones, one of the community architects for Hyperledger. I'm here today with my co-worker, David Boswell, to talk to you about getting involved with Hyperledger. Thanks, Rye. The first thing I want people to know about how to get involved is that we want you. It's an open source project and it wouldn't exist if people like you didn't step up and get involved. Hyperledger is a technical community that is 100% open and it is led by a diverse group of technical contributors. And if you've never participated in an open source community before, that's okay. You can still get involved. And here are a few tips that will help you get comfortable. First, feel free to alert. There's no obligation to get involved in conversations and contributions right away. We encourage you to dial into a meeting or join a mailing list and just listen in to understand what's happening. And please don't wait for an invitation. Everything is open and we mean that. All our meetings, all our mailing lists, all our chat channels, you can just show up. You don't have to have anybody invite you to them. And lastly, be aware that we have a code of conduct. If you do get involved in the community, we wanna make sure that you have a safe and pleasant experience and we do take the code of conduct seriously and do enforce it. So feel free to take a look at that. We'll put a link in the description below. Why get involved in Hyperledger? Getting involved will give you the opportunity to shape the direction of the project to fit your needs and use cases. It will help you decrease the time to market and accelerate the work of the project to do what you want sooner. With enough eyes, all bugs are shallow. Leverage the community to fix your bugs. You will learn a lot from interacting with other community members. You'll gain experience that will look great on a resume. You'll be able to tap in to a worldwide group of people that are interested and want to help. Here are some good general guides to getting started. There are a lot of great resources online to help you get comfortable working with an open source community. Links from this page will be provided in the description of the video. If you have any questions, please feel free to send us an email info at hyperledger.org.