All right, so hey everybody, Karl Epiglisi here. I like to keep it informal. I've been with IBM for a few years now. Prior, I used to work for company where I was director of innovations. I've always dealt with emerging technologies and trends. My focus at IBM is the global data science and big data evangelist for our platforms and our tools. So I'm gonna cover for you a little bit about things that are emerging in the space around data science, particularly how the team approach Apache Spark. I'll go in pretty deep why Apache Spark's important in the ecosystem open source tools versus tools out in the space for data science. And then at the end, I'll give you a quick demo. So how many people here actually understand, we know Apache Spark pretty well or deep with it at all? Hands-on, coding, Python developers at all? A little bit, okay, cool. And any R developers or data scientists in the room? Okay, good, this will be a good talk for you guys then. I was worried about getting to high level and then I'm gonna go too deep. So, well first, I talk to clients all the time. I meet with large, big clients and everybody's faced the same problem. We live in a digital age and you guys know this, but everything we do, we live, play, learn and everything's digitally now. And so the way customers are being handled and where companies are dealing with the digital evolution is different for each company. I mean, I work with companies such as large banks, Bank of America, JP Morgan Chase and these guys are going deep into data science and they're transforming their businesses but still their main money is based on banking but everybody's faced with how do they innovate and how do they keep up in today's market. And so we did recently a Harvard Business Review recently came together and it said that 72% of companies are vulnerable to disruption from digital businesses. So digital businesses are disrupting industries. I mean, you see it with the Ubers and so on but it's happening and it's real and so it's all being done with data science and application development. So that's kind of my thing I'm gonna go into here. However, data-driven organizations are really built around a team. Data science is really a team sport and we at IBM have focused in on trying to enable these four personas as well as the chief data officer. The data scientist is at the core, this is the person that builds your models, your algorithms, does discovery work, figure out new business models. The business analyst is the person that's kind of got the business expertise and they're the ones that are kind of identifying opportunities that could become available based on your data. The data engineer is really that guy that you deal with a lot, the DBA type who's got all the data, don't wanna let go of it. I'm more of a developer type myself but the data engineer is still relevant and their job is really bringing in that data, making sure it's secure, all the things that the developers typically don't wanna deal with. And then the application developer is really the true one that's kind of putting that in the hands of your customers and building the applications. However, in this time right now, there's really a shortage of data scientists because the data scientist seems to be the go-to persona or person for all these problems we're trying to solve, what the reality is is we're really just trying to solve, putting data science into our applications. And so a lot of companies are having data scientists be assigning silos and they're doing discovery, data modeling, development and then they work with the engineers, developers and data analysts. For them to be truly productive, there's a lot of draw there. And so what I wanna talk to a little bit about is how that particular role is becoming really a difficult job in the tools that are in this space and why Apache Spark is kind of changing that a bit and why the application developer will become more relevant in this role. So really like what is a data scientist and a data scientist I always talk about, they have three, by the way, I forgot to mention, I got three hoodies here, IBM Spark hoodies. So whoever asks questions first gets a hoodie and I only got mediums, large and extra large. Just so you know, one each. So if you have a question, interrupt me. But a data scientist really has three main skills and this comes together with, you have the statistical side, statistician, they understand algorithms, they understand statistics. Then there's the domain expertise. This is really the understanding the domain you're in, the business problem and the challenge you're trying to address. And then the programming skills or the computer science skills. There's no real one person that has all three of these. That's why data science is kind of a team sport and we see this in big companies. We have some guys that are pure mathematicians and they're very good at data scientists. They also maybe can understand the domain but they may not be as deep in the code and side of things. But then you have people that are more coders, developers who understand the other two or domain expertise but they don't. So data scientists could fall anywhere in this diagram and it's kind of what makes up a data scientist. Data science tools. So Gardner recently came out with the Magic Quadrant and I know IBM's up there so it's a shameless plug but if you see it's like they're all big vendors, guys that make a profit but there's no open source tools here. So my point here is to truly talk about managed platforms are provided but there's a variety of tools and there's a variety of approaches that are being solved. IBM obviously we have our SPSS product that's quite mature but we're also embracing open source is what really I want to cover in this talk. But if you look on Google Trends, this is not market share but you'll see that if you put, and Gardner doesn't put open source against managed platforms they just don't do that but if you put Google Trends and you'll see the interest the open source community with Python and R is grown considerably. Now SPSS and SAS really has managed platforms that are out there and that have quite a bit of market share but you'll see there's a ton of people using the open source technology and Scala as well is kind of low because it's a niche area but a lot of people have developed in Scala and doing data science work. Any questions so far? All right, yes? You know what the market share of open source would be if you were to compare it? You know that's a great question. There's really no data on that I know of on that. I know there was recently a great blog out there with one of the analysts who compared to Gardner report that just came out in February with all the open source and there's nobody, I think there's gonna be work done to that but they really break it down based on managed tools by vendors and open source is kind of separate so I don't know that for sure. I would think open source has a big market share too or maybe more but IBM tells me we have a good market share. I'm sure Sass tells the other guys they got a good market share. You told Gardner that, right? Well, Gardner is independent so you can't really buy those guys from what I hear. Hey, that was a question. Do you want to spark hoodie with IBM logo on it? Sure. What size? Probably larger. Oh, it's the last large. Let me see here. It's not for sale. It's not for sale? Oh, okay. You can't sell this. If you sell it, you gotta split it with me. There you go. At any point, I got two more guys I don't like to put them out. I got something at home, I wanna get rid of. So here's the open source ecosystem that I talk about this all the time and if you're looking for a job, seriously, I'm making good money, learn big data, learn patch spark, you can go either to R path or to Python path. I recommend Python, I particularly like Python better, but I'm the programmer, a lot of stat guys like R. But there's a, I did a search out and I didn't put a chart up for this, but I did a search out on, I think it's indeed somewhere in such in terms and these data science roles, they're hiring like crazy. Everywhere I go, there's a shortage of them and these are the skill sets that are really coming together well and even at IBM, our tool sets are really built around these technologies. So just kind of a heads up. But if you look at Python, you'll see, everybody here familiar with Jupyter Notebooks at all? Okay, good, then my demo's gonna be really well put. So Jupyter Notebooks, it's an interface that allows you to do Python development, which works well with Spark, PySpark, which we're gonna get into more depth around what a patch spark is. You know, Python's obviously a language and Skykit Learn, which is a machine learning capability as well as the machine learning capability in Spark, really are a good combination of tools that give you the ability to create some predictive models. And then R, you got RStudio and Shiny, Shiny's a great visualization dashboard tool. If you're familiar with it, they all complement each other really well and I'll give you a demo of what that looks like. So Apache Spark, I'm gonna be very heavy on Apache Spark here because this is ApacheCon, but we, for folks that don't know or folks that do know, but Apache Spark is an in-memory application framework for distributed computing and a lot of people, for iterative analysis, massive data, I forgot that, but a lot of people think of Spark the same as Hadoop, but it's really not, if anybody's familiar with Hadoop, what Hadoop is? So Hadoop's really a two-headed coin. It's got a computer engine as well, it's got a distributed file system and so they kind of work together. With Spark, it's really that computer engine, that's all it is, I mean, it's a lot, but it doesn't have the storage layer and there's a reason for that. And we consider it, we call it our analytic operating system. At IBM, we embrace Spark quite a bit and we're investing Spark a lot, but it's a analytic application framework that does computation on the fly and it scales out endlessly, obviously distributed. There's no limits if I've rambled that one on pretty bad. So Spark's really hot, I mean, it's one of the top projects, active open source projects. If you look at compared to Kafka and Storm and Flink and some of the other ones, there's way more contributions and commits, so there's a lot of activity going on with Spark. And there's a reason, I don't know why this is, there's a reason, Apache Spark, if you look at MapReduce, to write a simple word count with MapReduce took tons of lines of code, maybe a hundred lines of code, with Spark it's really three lines of code and it's just, it simplified the development against big data so much more and it's so much faster, that's the reason there's so much interest in it and I'm gonna explain why. So here, just a high-level overview, is anybody familiar with this slide at all? So this is Apache Spark, I'm supposed to stay by this microphone, but I like to wander. This particular architecture kind of talks about how, what Apache Spark is, so there's really, it's got a set of API layers that sit on top of a core compute engine. The core compute engine is quite sophisticated, it's much better than MapReduce, it uses a thing called a cyclical, a critical graph, it's a graph that manages all the transformations, so that when you run, you have two types of actions that could happen in your compute engine, you could have a transformation or an action, so every transformation it keeps tracking a graph and then at the end when you hit an action, it'll do them all together as one computation, so it's much faster than MapReduce, which spills back and forth to disk. I always tell the story of, if you have a file that has a million rows in it, and then the next line of code says, just sum up the numbers for the first hundred rows, why does it have to load that whole thing into memory and then wait for the next action, so it kind of waits and does it all together, so it knows I just need to read the first hundred rows and we'll give you back your results, so it's a better way of doing distributed computing and it's very powerful. The next thing that's great about Spark is it's got a variety of APIs which are all part of the same project, so if you look at Hadoop, it's an ecosystem of projects. You've got Hive, one project. You've got MapReduce, another project. You've got another project for Storm, which is Streaming. With Spark, you have Spark SQL built in. You have Streaming, which is built in. Dstreams is the API. You have Machine Learning capabilities, ML Lib, which is what I'm gonna be digging into in this session, and then you got Graph capabilities. And then it doesn't matter so companies have a variety of data stores, so you don't really need to care about what data store you use. You don't have to just use HTFS. You can use a variety of data stores, whether it's public on the cloud or it's on-prem, it works with all of them together. So you can pull a lot of jobs that I do. I pull data from a relational data warehouse and then I'll supplement with just a file on a distributed file system or I can put it in S3 or something and then put them together and do some analysis on the fly. So it's quite powerful. Any questions on the architecture? Spark?  And so the data will be pulled into memory and if it doesn't have enough memory, it'll spill over into some disk, so it does some disk. In the state of memory, is it only for one thing? Just for the scope of the driver application. So when you initiate, when you create a Spark context, it'll know, it'll be aware of the applications telling it what to do. And that Spark context, when the test makes a query which pulls the data from different sources, it'll create connectors into those sources, pull that data into memory, run the computations or iterate through it and then give back the result to that application. Does that make, that's a really good question. What size? Medium or extra large? Thank you. I'm gonna throw it at you. And so that's a really good question. And so Spark, is it part of the big data ecosystem, but it's really more, for me, I see more as application development paradigm. Any other questions? Could you use it against like a traditional database as well? Yeah. You should get the last one. You don't want it? It's extra large for us. Fat guy. Damn. Well, anybody that's bigger want to answer the question? No. Just kidding. So, now nobody's going to speak. They're like, I'm not bigger. I'll just keep it myself. It's just too hot in Miami. But yeah, you can use any data source. I mean, there's connectors. So, some connectors are optimized. So, if you have a distributed data source, for example, S3 is distributed, and the connector from Spark to S3 can co-locate based on compute and processing. Well, HDFS is like that. I know our object store is like that. So, some connectors are smart enough to know to put some computation with the location of where the data is persist, if you understand what I mean. But so, it just depends on the connectors. But some connectors to like relational data warehouses will go through one node, which means maybe you have a bottleneck depending on the size of your data. But Spark's really good for machine-learn application that doesn't really mean you have to have big data. And I always talk to some of the SPSS people and stuff, and they're like, oh man, I got a million rows to deal with. And I'm like, well, for Spark, a million rows is not a big deal at all, even just through, if it's pulling through, connected through one node. So, it's quite interesting. It's very different than your traditional way of thinking, and it's quite powerful. Good question. The other thing that works well with Apache Spark is the open-source notebook. So, notebooks, years ago, people used to use pen and paper. They'd have a thought, a formula, they'd write it down. They'd write the numbers down and put the results. Now it's all done in a browser-based application, so it's a way of basically writing a line of code to request some information and pulls it back. You see it, you visualize it. Then you can compute, do more computations on that and kind of iterate in a notebook style where you're just thinking thought to computer back and forth, and it's a really interesting way of data scientists working, and I'm also very good for developers as well, and I'll show you a demo of what that looks like. The Spark supports multiple programming languages. So, you can interact with Spark, whether it's, somebody's turning the lights off. I think you're good. Whether it's Scala, you know, Scala, Spark's written in Scala, it's Scala, the advantage of Scala is that when you, for everything new developed with Spark, it'll come in Scala, available Scala first. SQL, I don't know why that's there, you should take that up, but Python is another language which is growing in usage. If you look over year over year, Scala's going down. R is new, but it's starting to grow, and then Java's decreasing. Java's the harder one, there's more lines of code. So, what this is telling us is that if you look at the usage, it's around, the growth is around Python and R, and this is where the data science workload or data science use cases is coming to Spark. And then the Spark library usage, so data frames is taken off like crazy, and that's, again, ideal for that data science workload. And then you got SQL streaming, so they're all on the increase, and then machine learning's also increasing as well. And this is the survey that was done to see what APIs are being used in production. I expect next year you'll see machine learning grow more. So IBM's betting big on that Spark. So we have invested in the Spark Technology Center, which we've put, I think about 100 engineers there to do nothing but contribute into Apache Spark. We also use Spark in our portfolio. So not only are we contributing to Spark, we don't just do that because we like Spark, we're doing it because we are setting a lot of, we're putting a lot of Spark tools in our applications. Watson uses Spark, some of our health applications use Spark, so even our security applications use Spark under the cover. So we're using Spark a lot, so we're contributing quite a bit. Everything we do, we put out into the open source community, and we also help our clients use Spark if they want to just use Spark themselves. And when we created the Spark Technology Center, Ben Horowitz, who's the big VC guy, said it was like Spark just got blessed by the Enterprise Rabbi, it's pretty funny, but and so STC continues to grow. And it's just really show that we're committed. If you look at our contributions to Apache Spark, we're really contributing heavily around machine learning, Spark SQL. I mean, IBM created Spark SQL. We have a lot of mind share out there. So we've been growing Spark SQL, machine learning, and PySpark, which is the Python, and incorporating Python capabilities within to Spark for distributed. So those are the areas we focused in on, and R is becoming another hot area that we're doing a lot of contributions. And Databricks, the founding company, they have most of the committers there. So they have Matej, who's kind of the guy who invented Spark at Amplabs, which we support Amplabs. So they do a lot of contributions. We're not gonna beat those guys, but I actually got lucky once. I have a selfie with Matej. I was on a flight, and I was flying from Boston to Tampa. I live in Tampa, and then all of a sudden, this guy comes down, skinny guy sits right next to me, and I'm like, man, he looks familiar. And I'm thinking, I'm like, I started talking to him, and he goes, oh yeah, I'm in distributed compute, data science and all the stuff. And he goes, I'm like, Databricks? He goes, yeah, I'm Matej. It's like he's a founder, creator of Spark. It felt like a groupie. I'm like, I didn't do nothing but learn in Spark lately, so it's kind of funny. But he's a nice guy, and Databricks is a partner with IBM, so we work with them on a lot of projects, particularly GPU acceleration with our R&D. We've done quite a bit with those guys. So we're number two in contributions behind Databricks, but then if you look at all the rest, I mean, we have more contributions in the next five companies. Actually, these five, six companies here, one, two, three, four, five, six. Seven companies make up 70% of all the contributions to Apache Spark. 2.0. So, and the other thing I want to make note is, we're heavily invested in machine learning. We have been doing machine learning quite a while, and so we're contributing a lot in that space, and SQL, and R, and as well as Python. So Spark has been accelerating machine learning, and here's some of the reasons why. Spark is easy, you can code solutions much faster with Spark. There's less lines of code, there's multiple programming languages, so you can have a Python person get up to speed quite quickly, you can also get Java people up to speed quickly. Spark's also agile, and I look at that typo in there. Quickly, you can build pipelines quickly, and it's got a unified API library that is accessible and easy to use, and it supports notebooks, which make it even more usable. And then Spark's really fast, you know? You can iterate and train models quicker. I should have proofread this. And then in memory processing at scales, it's just, it's much faster.  So there's been, I'm telling you, the market right now around machine learning. If you're a developer and you want to get into data science space, start to play with machine learning, because it's just everybody's looking at machine learning right now. So what is machine learning? I was a programmer, so for me, I've programmed everything explicitly. So machine learning's really the use of data to where you can have computers act in without things being explicitly programmed. You're just using a formula. So it's really just, you guys probably all know this, but sometimes it's good to talk about so people understand it. And then the process to create a machine learning model, this is kind of the standard basic way to do it, but it's all about you bring your data in, and you cleanse and transform your data, then you train a model, you piled out some of that data as a train, you have some of that data for tests and cross-validation to get an error rate, and then you run it against the algorithm, you train that data for a predictor field, so you're really trying to identify one field that says, hey, here's all my data, here's one field, I'm trying to see what algorithm, how accurate it is to predict it. So it trains to get that algorithm against 80% of your data, and then 10% of your data, or you can pick any amount, 70%, 60% of the data, but you train the data against there, and you try to build that model, and then you validate and test it and get an error rate. So it's quite basic model, and the reason I'm going through this is because we've been putting together application development interfaces to let people do this simply so that they can quickly embrace machine learning and build an endpoint that they can quickly deploy, which I'll show you guys. So to data scientists, while it's a sexiest job, it's also one of the toughest jobs right now. I've known a lot of guys who get hired and quickly quit and go to another place because it's just a lot of the problem is more than just the data scientists. So there's the tool sets, there's so many tool sets out there, so it's really difficult for them to be successful, and their approach is really limited by the tools that the company's kind of embraced and used at the end, which has been a challenge. You have fragmented and time-consuming, a disjointed environment, so everything kind of takes forever to get your stuff together, and then your analytics silos, which you have different organizations, they have different data sets, and it's hard to bring that together. So data scientists has a tough job. So here comes the sales part. So our mission is really to make data and analytics simple and accessible to all. So we built this thing called the Watson Data Platform, and in there we built a data science experience, which I'm gonna show you a demo of, and data science experience is really a collaborative space that lets you learn about data science, particular algorithms, Python libraries. You can create different analytical assets or different analysis, and then you can collaborate with other data scientists. So it's a pretty cool environment. And what it does is it really brings together three things, community, which is the place for tutorials, data sets, and different things for education. Open source, we bring together Spark, Jupyter Notebooks, Python, R, we have Shiny and RStudio, and then we added some of our IBM products, the capabilities that were done over the years, and things like data shaping, piping UI to simplify the process, auto data preparation, and advanced visualization. So we kind of pull that all together into one platform. And what this has allowed us to do, and kind of my pitch to you guys is that, if you're a developer, you really can become more embedded in that team with the data scientists, where you can actually supplement them and do some capabilities and deploy them out into applications without the truly need for that data scientist team. You kind of become the data scientist. But I'm not saying, I have had a lot of clients yell at me if I say, we can automate the data, application developers can automate data scientists and everything. No, you can't. So it depends on how you view it. I think really once you begin to learn to do some of the stuff you are a data scientist, so it's kind of like back in the day, I don't know if any of you are old enough, but you remember back in the day when everybody was webmaster? You remember? Everybody was a webmaster.  Any questions? I'm gonna do a quick demo or what do you guys got? Thoughts? I got a hoodie here. Are pricing for the tool? No, it's just, so we have a couple of ways that we price it. So for companies, we have enterprise license where you have five seats and so much storage is on the cloud fully managed. You have a 30 node spark cluster and you just pay so much per month. And then we also have individual plans. Right now it's free, so data science experience, you go out there it's free, you get like a little bit of storage and you can try it out and we're working on a freemium model and then if you wanna like add more computation and more storage, you kind of just kind of put a credit card in there and it kind of is up by consumption. So we got a few models. If our big clients, we kind of just give it to them and say, here, pound at it and tell us what you think so we can grow a product. Cause a big part of what we're trying to do is make sure that we get people to use this open source technologies and start to embrace it and put it into their applications. So we're big, I'm the global sales leader for this. So I work with OM and Dev and drive in our go to market. And right now we have quite a few clients using it but we just went to market with it late last year, November. And now we're just ended this month we're releasing our Watson machine learning which is what I'm gonna demo for you and some other capabilities. So we're expecting that Q2 and three, we're gonna hit quite a few deals that clients are gonna, right now I got, I think I got like 40 customers on it. So it's grown pretty quick. Let me show you it. So for each of you, go to data science experience.com or I know it's just, it's just, it's just, sorry. Data science.ibm.com and you'll go right here to this landing page and you just do sign up and you get a 30 day trial right now and eventually that's gonna stay free for education and just usage. And then as you do more enterprise or larger volume projects, it'll cost you something but I'm gonna just sign in. So it's a fully managed environment. And when you land into DSX you'll see you go to community page and here you can search on articles like I wrote a few labs on Spark. So if you wanna learn about Apache Spark you got Apache Spark lab one in here you can quickly create, use in PySpark, create some RDDs, some quick transformations and actions it'll teach you about Spark but there's also lab two right here which is query. So you spark SQL and lab three is all about machine learning. So I recommend you go in there and in those labs if you look at them this, you'll see it has a little bit of a description but it also has a notebook which has all of this description which as well as code in here to show you how to actually code with Spark. So it's a great tutorial and for some of our clients I'll do them on site too but you don't really need to you can do it on your own. So let me go back. And you can search on different data sets. So do you have a question? Did you have a question? You can, it's very stable to do, I mean a lot of people have Spark in production you know, but everything's kind of programmed so you mean, what do you mean by changing input? So if you, yeah. Yeah, yeah, you can you'll have the, you can add your data to the environment so we can actually have this new frame on this feature model. Yeah, so we have something that we yeah, we have the capability to set so the question was how do you refresh your models so that changing data refreshes to it? So we have intervals that we define where you can have it retrain every day or every couple of hours and we're also working on capability and this is part of the value out of IBM. Today with Spark you'd have to create a Spark submit job and you'd have to rerun it every day and schedule that as a cron or something. We kind of built some applications around that but you also have the, we're working on capability so as data goes through this in real time it can continue to score and improve and as well define some alerts that says if it goes out of a certain error rate once we have a feedback loop which says hey, we predicted this but then it ended up being say this value and we get that feedback loop we're gonna monitor the error rate and if it goes out of some type of KPI then we'll send an alert because this is all about our model management strategy because companies are building models and they're deploying these endpoints on the platform and then applications are just calling it and so we gotta keep up with that and monitor them and so we're working on a whole strategy around that and that's all coming out this year and I'll show you some of that capability. Do you want the hoodie?  All right. Did you want it? You got a question. All right, what was your question? So Spark, I'm working with companies like General Motors where they have 500 petabytes of data and we're able to do some Spark jobs so it's pretty mature for whether it comes to dealing with large volumes of data. Yeah, you can for sure that's why you gotta design your Spark cluster for the size of the data that you're kinda gonna be dealing with. There is some, so your cluster manager can manage some of that and spill some of the disk but you can't get memory errors if you blow up a small Spark cluster. We have some things we've built in there for optimization based on the data science experience so that people don't hit that but yeah, that's still part of it.  Yeah, I agree with you there, it's getting there. The catalyst which is an optimizer built inside of Spark allows data frames to be optimized and when you do Spark code in Spark and you stick with data frames and that catalyst will continue to grow I think and you'll see it but it's not a database, it's an application. So just like in C, if you code an application you could blow it up, I mean it's like it's really still more of an application than it is a database, that make sense? All right, so here, let me show you everything that we do, some reason this because of my resolution, my bars are different but everything we do is under projects so if I go to view projects, we've created a space that allows you to create a project and add collaborators, so like I have a project here I call it Focus 5 Machine Learning in five minutes but I've put in here a bunch of my, you'll see once you're in a project it's organized by analytic assets and this is kind of your notebooks which I'll show you what they look like models and flows which are really SPSS flows data assets which are really data files or data connections that we've kind of organized together here bookmarks are really articles, deployments these are endpoints that are built to of like the model that I built that I wanna call through an API and collaborators, here are folks that I've given them access rights to my project I created it and I gave editors and viewer access and then sedans and if you look here it's just some details around the project storage that's used, associated services like I have a Spark service in the background on our Bluemix platform I use as well as our IBM Watson Machine Learning service and you could even define a service here if you wanted to like an AWS EMR Spark service so you have some flexibility and then you have access tokens it's for security which I don't really deal with a lot no we got a whole team that deals with security then you got GitHub integration and I don't know what this is to be honest project scope but let me go show you something real quick with analytic assets so here I'm just gonna show you you guys familiar with TensorFlow trying to think of what notebooks show you so here is an example of deep learning with TensorFlow so this is what a notebook looks like so when you open up a notebook you'll see it's a space where you can put some documentation as well as have code and cells so right here I can create a code, a cell and let me show you what this looks like from scratch so if I add a notebook I can create a notebook and then here I could pick the language I can do Python, R, Scala or Python 3.5 I could pick a Spark version I pick my Spark service and then I could just create a space and now I've created a Jupyter Notebook and in the background I have automatically created for me an instance for a Spark cluster as well as some Python libraries that are installed and ready to go and if you can see the navigation is along the top here so if I go here I can say oh look I have, I can load data files in I can also define connections but here is a data file and then I'm gonna say, I'm gonna take this data file and I'm just gonna go ahead and create insert a Spark session data frame and it'll create the code for me and then if I run this I've now taken that data file it's now running against Spark cluster you see the ashtray it'll come in a minute and it'll have a data frame available and you can see the job right here is running Spark job so it's kind of a cool interface and it allows you to kind of iterate back with the Spark cluster as well as your data and do some data science it's just a Jupyter notebook but for those that know it but it kind of adds some simple easy to use capabilities the other thing that's nice is if you look here you'll see this is my notebook but the environment so I have a Spark environment run in that's accessible so you know SC tab you'll see the Spark context is already available I don't have to create it, I don't have to connect it and then here I have all these Python libraries so I have Skykit Learn I got Mapplotlib Pandas, they're all available I can even got PIP so I can do a PIP install and I could show you some for example, like Google's TensorFlow on one of my other notebooks I did a install of TensorFlow and then I did a style transfer which I can show you what that looks like any questions yet so far? No, all right let me show you what the style transfer looks like are they made familiar with style transfer in TensorFlow? All right, cool, this one's fun so here's a deep learning example using the TensorFlow library and but really my sessions around machine learning I got to get to that next four round of time I got 10 minutes there it goes the other thing you could do too I forgot to mention is you have the capability to share so I could share some of these you could also there's a scheduler which I have to go back to the other one and you can create chat like capabilities here I can add comments so that while I'm sharing so for example, you create a project I go in there I create an analysis as a data scientist say I'm trying to figure this out somebody else can look at it and say you could say I need more data sets and they're like okay and so we can collaborate and share and create checkpoints with the notebooks so it really creates it like an IDE for data scientists and developers but here in TensorFlow I don't ignore all the codes I'm gonna put that as imports eventually but basically it's using a deep learning algorithm to take two pixel images that doesn't know anything about them and other dots you know, the pixel image and it's saying okay using this deep learning algorithm take these two together and let's merge the styles so as you iterate you see 10 iterations you start to see the new image that's created just using an algorithm after 20 iterations it gets better and after 30 and 40 and then 50 and then 60 iterations you'll see and now I have the incredible Hulk with this style so I took this image with this style and made that and a lot of data scientists nowadays are doing really important work like this like taking a picture out of a cat and make it look like an art deco but it's just kind of it's kind of a neat use case here but let me show you a little more practical I'm sorry the other thing nice about the Eastense experience it really creates a space for you to load data in create connections so it's kind of organized your data for you so I encourage all of you guys to play with it and start to learn and do the tutorials but here's what I want to show you so under your analytic assets so if you were to code a machine learning model and deploy it you would have to you would have to create quite a bit of spark code it's not a lot but you have to create some spark code and here's a notebook that does a prediction of outdoor equipment that we collected through an API you know of purchases on a website so you'll see here it kind of you know import PySpark and then you'll and I'm going to go quickly through this but you create here you can see the scheme of the data that shows you the records you have product line, gender, age, material science and profession and based on that we want to do predictions so then you would create a ML model so we'd create a model a few lines of code we'd have to split that data into 80, 18 and 2% I don't know why they did that but so and and then you would train that data and test and validate and you kind of run that against your spark ML libraries which are right here PySpark ML and and then you can come down and you could print your schema let me see here and you can get your prediction your accuracy an error rate so so it's more accurate than flipping a coin it's like 58% accurate but it's not that great but anyway it's just a good way of showing how you can do that and then you have a way to with Watson Machine Learning you can create a deployable endpoint through our services so that you don't have to kind of redeploy the whole application yourself it simplifies it for you but let me show you how to do this simply with a GUI so we've created a way to do this with a GUI so here I can take and this is a sales predict so here it's part of the pipeline this is your pipeline on the left and you could just pick some sales data you can drop it in here if you wanted to within the data you can just drop files in it becomes accessible so here I created data I'm going to prep it you'll see it's the same data I could pick a auto data transformer or I could just pick some can transformers you have quite a bit of them that you can play with then I can do train and where this works is you pick a predictor field I'm kind of not in edit mode but I'll just talk to it and then once you pick the predictor field it gives you it kind of a it tells you an approach this one says multi-class classification you pick some estimators which really are nothing more than just the algorithms available and we'll continue to grow this this is right now in beta and it's used in all Spark ML and then here at the bottom you'll see you'll say I want to set a sixty percent train twenty percent test went to hold out so it's you don't have to kind of code that and then you can go to evaluate and it'll bring you back it'll train you run it's a couple minutes it'll come back and give you the the results of them and oh it says poor but again you get same error rate it was the same data you'll see that that's that's the best part once you get to where you're happy you click save and I have one saved here trying to go fast so once it's saved you'll see it says oh look I have a uh... IBM Watson she learned service product line I have a random for us uh... with spark ML classification of algorithm available and then I can do a one-click deployment kind of very simple just i want to do online deployment and what it does is it'll create you a and rest API that you can access with your applications so once you have that you have this scored endpoint that you can call your applications and it can do predictions for you and uh... it's it's quite uh... it's quite powerful and easy to use and then if you look here you just do a simple test so i'll say let's say a female age thirty kind of my import single as professional i run the uh... the against the uh... predictor and it'll say uh... sixty eight percent likely to buy personal accessories my okay cool so let's say if she's married right when she did it differently predict and it's less likely to do personal accessories and by other stuff so so it really is a cool way for you to just take pure just data taken in here and this is in beta it's coming out here in a few weeks yes right yeah we're right now we're using all the spark ML uh... algorithms but we're working on there's some projects out there we can plug in here that we can pull your own algorithms in but most people use the can ones right now yeah would be great for academia for sure and uh... i'll check because i know that uh... where we're adding algorithms to spark ML we're also including sbss algorithms that we have available to us in here that are well-known and usable so works continually expand the algorithms that are available and then you can just see right here i actually have a gooey so this is just a dashboard shows that here's the uh... service and you know he kind of just showed some customers and generate predictions and for alexander uh... yes for alexander who's a male thirty six single you buy camping equipment this is all kids really for an outdoor store is what the data was not a great use case but it's kind of cool but this is just in a simple no-jus application so with my last minute any questions no more amount of hoody i got three in my room i'll bring that i should have run down uh... i gotta get rid of it i gotta go fly to san francisco thursday and i'm doing a briefing in san francisco they're gonna fly all the way back home the florida tampon my wife's birthday saturday yeah absolutely yes well you could do there's a couple ways most people uh... share notebooks like i i have on get hub a ton of notebooks but you can uh... one you can download them uh... right here into i pipe python notebooks and you just hit this download button and if you'll see it'll save it as a standard it's just i p y n b file which then you can import into any jupiter notebook so it's all the open source stuff and you could also share it as a just a uh... url right here that people can look at now you have to take it from html to pdf uh... but you get it you can send an html the other thing that i forgot to show you is that there's a lot of cool visualizations i've got that i promised uh... david he's got a talk tomorrow at eleven on pixie dust pixie dust is a visualization library he's going to go much deeper into visualization libraries on uh... in notebooks but you can see here here's an example of brunel these are these are quite extensive i mean you could just build this this lot of these visualizations in jupiter notebooks uh... we've actually open source the brunel libraries which are came out of cognose pixie dust is another library that we've open source which are visualizations on jupiter notebook so you can build these visualizations and then share them as html pages so uh... it's quite interesting the nice thing about it is you have the code uh... and the code that was to generate it as well as together as a visualization of the results that's kind of what's the power of of these notebooks uh... within the uh... the state of science experience and then all of the computation analysis happens on the back end in the sparkluster did you have another question yeah i'm at a time thank you guys


OK, I've just showed you scenario one. We're going to move on to scenario one A right now. Onscreen, I still have scenario one that showed so we can compare the noise values. So here, if you remember, in my previous example, scenario one, we had assumed that I had about 100 conversions per bucket. So what this means is roughly about 100 users per day have converted for campaign one, geography Europe, product category shoes, and about 100 users have converted for campaign one, geography Europe, product category t-shirts, and so on. What if my campaign was a little bit less impactful? So let's assume that instead, I've got about five conversions per bucket and per day. So I'm going to set my average daily attributable conversion count per bucket to five, and I'm going to go ahead and run my simulation again with these new numbers. Those app automatically scrolls down to the newest simulation. So this is my new simulation here on the bottom. And here, this is my previous one. So you can see here that the APE was one at 5% for purchase value, and previously APE was even smaller for purchase counts. And what happens here is we can see that the average percentage are really increased up to 42% for purchase value, and about 3% for purchase count. You can see a bit of our ability between APE and RMSP, but they're kind of in the same order of magnitude. We'll come to that later. So why are these noise values here higher for that new simulations? Well, that makes sense because here, if you see what we've done is we've made our buckets smaller. So the blue part of each of my entry in my summary report is going to be smaller. And remember, the aggregation service and NoiseLab as a simulation tool is drawing noise from the exact same distribution.  So here, in my second simulation, I end up with more noise relative to the signal. So it makes sense that my noise ratios here are higher for that second simulation.


So we're now going to take a look at equations that enable us to determine the exergy within a system. And we've looked at two types of systems in this course. We're looking at fixed mass or closed systems, and we're also looking at open systems. So let's begin with the fixed mass system. And writing out the first law, we see that we have our energy over on the right-hand side. And we were saying that there was energy or sorry, exergy associated with the different forms of energy. So the exergy is the exergy associated with these different forms. And the exergy for this type of fixed mass system we will use phi to represent. And it will be equal to the exergy of the internal energy plus exergy due to kinetic energy plus exergy due to potential energy. So we can sub in the values that we just derived previously. And it's the first one for internal energy that's a little more complex. And for kinetic energy and for potential energy, it is relatively simple. Basically a one-to-one relationship between the energy itself and the exergy because all of that energy theoretically can be converted into useful work. Now usually with a process or any kind of system we're interested in the change in exergy between two states. So let's take a look at that. And if we're looking at change in exergy of fixed mass, we will have delta phi. That would be phi 2 minus phi 1. And then we'll just evaluate the change of the exergy phi term that we have. And so that's what we end up with for the change in exergy of fixed mass. And we can also have on a per mass basis delta capital X would then be equal to the mass times our delta phi or the change in exergy. Looking now for an open system or control volume, we'll again begin by writing out the first law. And again, the terms on the right hand side represent energy. And so we've come up with expressions for the exergy associated with those different energy types. So let's write out the exergy. And for this we're going to give it the symbol psi. So we have exergy associated with the enthalpy plus exergy associated with kinetic energy plus exergy associated with potential. Plugging in the values. Now we're usually interested in a change in exergy. And for that we have delta psi. So just evaluating the change. So those are equations that we can use to evaluate the change in exergy for either a fixed mass system, a closed system, or for an open system with a control volume. Now you'll notice I haven't given you any expressions yet for the heat transfer on the left or the work. And so what we'll do now is we'll take a look at how we can account for the exergy associated with both heat transfer and work. And then we will have all the things we need in order to start doing exergy analysis.


Hey there friends, how's it going? My name is Kevin and today we're playing some hit man. We're going in blind into the China level. We're gonna try and kill everyone on the map without really knowing the level Which is gonna be weird because I usually kind of know the ins and outs of the level a bit before I do these challenges But we're going in blind and we're gonna see what we can do I just hope you know what you're doing 47. Imagine seeing him like on video call with that outfit Like I hope to God you know what you're doing. All right. Well may as well get started now All right one down probably like 299 to go. Well, that's a good start. That is a solid start, you know starting strong That's right. It's just like uh I'm gonna go. I should probably hear what these people have to say but I'm a busy man I got a children's birthday party after this as well. Yeah, I'm gonna kill all of them also So far this has been really nice. I know that's a weird thing to say given the circumstances But like it's just been very easy with body found how oh a dumpling cook I don't know how to cook a dumpling, but it should be fine. Whoa What is this this are dumplings are made the doors locked. I don't suppose I know no I don't know the keypad. Do I one two? three four All right one two three five All right one two three six All right that doors bugged or something I don't know and might be something to do with the washing machines the room might be bugged out No one saw that right Come here absolutely terrifying No one will suspect a thing unless they follow the trail of dumplings right to me I think I can take care of all these people Okay, this is fine. Oh no Reload oh no, how did you get in there without any code don't don't don't I don't know the code I can't get in here if you all right. This is fine.  I knew that room was bugged for me All right Don't suggest nothing to me. Oh a Sniper I can take our loads of people with this. I feel like I'm causing a bit of panic in the neighborhood. Oh Jesus that was an impressive leap get a 360 for the montage. All right.  I'll edit out this clip. It'll just be the the shot that hits All right Maybe montage is there in my strong suit. Hold on Okay, that'll do it was a bit slow, but we'll use editing to make it look really really slick This thing is hooked up to Royce Pings her if it's damaged. I don't think I need to follow this. I have my own plan in mind Just feel like what do you mean? She just sees me pull up the wrench. What's this a garden fork? Oh, no, he's gonna be pissed. He's gonna be absolutely pissed if they realize there might be gardeners around Gardener power. This is why he's a fear of gardeners. I understand it now Okay, not bad.  Wait, how's he alive? Oh Well it passes the dead bodies and it's fine with it, but then when it sees me doing something, it's pissed Down nearly had it. What are they talking about? I've got my own plan going on here We can listen in on Royce. Who is Royce coming up there myself? Nice shot. Thanks Was she mocking me? I missed him and she said nice shot. Hold on. This is base.  Oh She's just backs away Probably for the best. All right, um, dude come back when you're done. Okay. He's like why no reason just come back You're gonna be needed again. Oh Jesus. Oh, no. Oh, no, she's gonna snitch come back here Oh, that's the target Anyway, I started blasting Quite the bet apparently I don't think it matters anymore. Nothing a hammer didn't fix Where am I in some lab?  I Accidentally stumbled my way in here for some reason. There's a lot of suspicious people like these are just civilians Why are they suspicious of me? I've done nothing wrong There's gonna be a lack of chefs by the time I'm done here. I'm gonna have a lot of work as a dumpling chef Oh, no, she heard something Hold on come back. You're not allowed in the kitchen. You're not staff. Jesus. He's fast I'll be right back What are you doing? Are you a customer that just came in to solve the issue? Don't be a hero, buddy Well, there's no point me lecturing you now. It's over chef. I am the only dumpling chef These people haven't realized anything is wrong. Hold on. Can I take your order you fine folks? I need some assistance here. What? What?  You don't like the menu or something? I was heard on these dumplings Literally the only thing on the menu dumplings. That is it. Look on the dumpling chef. It's all I know, okay? The board member I've been promoted straight from dumpling chef to board member. Those were good dumplings, okay? What would they have done without my dumplings? Wait, what is this prepare? Yes. Oh My god, these aunt dumplings bone apple tea everyone. I don't think there's anyone left to eat it. Unfortunately. Ah Here we go. This one surely is fine, right? This game is so buggy. It's ridiculous. I actually hate it. How was that body found? Is there someone there if they just came around the corner and they see this guy in a suit standing there with a lump of Meat on a bone like they're gonna think it's human meat. It's rust all over again. Oh, sorry I didn't even know you're there. There is no sound effect. It's like in a badly acted out play He just got before it hits them. No one's looking at me. No, we're fine. Don't judge me I'm just a dude out with his meaty bone. All right. I'm going for a walk. This makes me want to go on a meaty bone rampage There's no sound effect. It's the most unsatisfying. Oh, okay. It's the most unsatisfying weapon to use though because it just It was nothing.  Sir, this place isn't for you Angry. I am youtuber. I go where I want. How does this person know that I'm bad? How do you know I'm not a board member? I don't understand. Maybe they just have a keen eye I don't know or maybe it's the meaty bone hanging out of my pocket. That doesn't sound good Clip that do not clip that it's out of context Even with full context say if you play the clip I'll just claim that it was out of context because it doesn't sound good at all. They're doing some weird experiment I want to ruin the experiment. I am the only experiment allowed here.   All right, this was easier than expected One of them just slept through the whole thing. Oh, this is awkward. Oh My soul broke.  I feel a little better cyberpunk really looks a lot better It doesn't get a cyberpunk vibes doesn't it well when I saw those dishwashers earlier anyway It's strong strong cyberpunk vibes. We're looking at one naked civilian naked. Oh the other guy I thought he was on about me. I changed my clothes because I thought he was suspicious of my disguise I know he was just suspicious about the fact that the hallway has loads of bodies in it We're no I mean, that's normal everywhere. I go. There's loads of bodies Let's imagine an invisible barrier between me and you that extends out to about an arms length All right, that's it. I was gonna sneak around, but I actually can't take your sarcasm.  I'm so sweaty right now. I'm so sweaty and moist Just to make you uncomfortable. Okay, the researchers here. I brought the screwdriver to open his head everyone Hopefully they didn't hear that Test subject. Yeah, I'll be the test subject test me There's definitely something wrong with me.  Can I kill these people? You think I can get away with it? Oh, he's not even looking I might have to hide the bodies though. Do I I'll just dump them out the window Oh, no, he's coming in Sheer panic I Can't I can't get back in the building. I don't know the keypad number I've got myself into a horrible situation here. Should I just try and look at the Intel one second?  He's like Kevin I Feel like I should have sabotaged the machines before saying yeah, let's do the experiment, you know plus you seem a bit short staffed So many of you have died. They're pulling in bodies literally as we speak. Oh, no They're looking into something The thing is she I when she's behind me and she just said hey, you're armed, right? I was like no to be fair Where would I be hiding all these things like I don't have any pockets or anything and I've got a lot of stuff on me I'm particularly worried about the crowbar and the meaty bone I'm particularly worried about those ones. God. I hope they don't cavity search before this test All right, the guy who thinks I might be suspicious is gone. We ready. Have we forgotten about the dead bodies? So, okay, we're just gonna continue.  What is this hoverhand? It's like me saying goodbye to you at the end of the streams Okay, what am I supposed to do? I was confused and now I don't know get rid of the assistant. How am I gonna do this? I'll have to come up with something really dastardly for this one. Oh God this is bad. Oh, no, and there's someone coming down the hall On the door can't close sneak 100 sneak one and a hundred That's what that was. We just have to wait. This is so awkward with him. Just staring at me Oh, I was hoping there was like a one of those gaps in the back of my gowns Just so you can turn away from them to just make him so uncomfortable that he would leave There's the gap that he could see everything you're hiding there I mean, I don't know how he's hiding all of this stuff in there But you're most concerned about the apple you're hiding There we go. All right, can I eat it? Do 120 120, okay seems kind of weird that you'd have a hundred percent if the machine can go higher But who am I to question it?  All right. I think they just shoot me at that point eat that meat It's a stick out the meat bone. Do you mind if I? Okay, come on Yes, the massive payoff he went sleepy.  All right. It's a bit overkill Well that happened what was the point? Nothing we've all wasted our lives today. I Just wanted to kill him with his story away, but it wasn't very fun I thought maybe he's going over the balcony. Come here. Come here. No, don't go limp. Come on We're going come on. There you go cavity hurts very angry patient. All right This is the laboratory as it go. This is the whole other side of the map that I've yet to get to Now it just looks like a badass doctor. This is who I want to vaccinate me Well, maybe not that's actually terrifying. He just looks cool. All right. I just want to be vaccinated This is what I'm saying. All right going loud Not loud enough apparently this person in the next room didn't even hear me me soldier now Haha gonna go bro. I'm a serious hit man. Okay, please hire me. Oh my god. He's venting He's so skies among us mean is it funny though cuz I said he's sus and he went in the vent So it's like among us. Do you get it? There's no way you're getting through that door 47. All right The signal is encrypted without a dongle. We can't hack it. Okay, so I needed a dongle Just need a meat bone and a dongle and I can complete the rest of this mission Oh, I found a dongle everyone. I'm gonna go dongle my way over here Put the dongle in the dongle and dongle's your uncle mission accomplished good. You're alone. Oh, okay Okay, wait, she's gonna explain it. I'm sealing the room and dimming the windows 47 wait, but I'm in here No need to worry about intruders. Okay. Okay good. I thought she's like gonna turn off the oxygen or something It's like, do you know I'm in here? I don't know what is going on in the story by the way I like how these people put in so much effort in this game to create like a Story branching cars three games, and I don't know what's going on and I'm at the end of the third one I'm just running around going haha put the dongle in the dongle. I can hold the doors for a little while I don't use the fence to get out. I don't think you have to I'm pretty sure like 90% of the staff are dead. That means they'll shoot on sight. I mean that's what I've been doing It doesn't matter. They don't pay attention to me at all This is why I thought she was sealing me in the room. Oh guys He's sus Sus guys No one's laughing in the chat. Oh, no, huh?  They said that's what the boss told me you guys I'm legit just a scientist. I'm just confused. Ah, he's shooting on site. You madman. I like how it's playing the heroic music I'm just killing everyone for no reason. I haven't the villain. I am not a villain I am the protagonist to die lady with umbrella Look, these people probably did bad stuff like murder or littering look at this You Know all right. She seemed lovely. It doesn't matter. I'm gonna keep going anyway I was expecting her to drop something or maybe say something rude, but oh now I look bad I guess she is kind of bad for making me look bad. That was pretty inconsiderate, right horrible person Oh robbery great. No, I'm a thief Draw the line here. Should I leave him? He's freaking out sabotage. Oh wait, hold on. Did that save him? Oh that saved him. Oh No, what am I done?  They put him literally in a bulletproof pod. He is not even bothered. He's not even threatened by this He just saw me murdering people and now I'm shooting him through the glass and oh my god He's so smug. I'm not a good person. Stop it.  Let us never speak of this again Let's exit the mission knowing we did one good thing in our lives. That makes up for it, right? It balances out


This is TFNN, the Tiger Financial News Network, News Update. Hi everyone, Basel Chapman here for the Tiger Financial News Network, 10am market update with a down of 280 points on this Monday 8th of August. This is really good action in the Chapman Way methodology as you know for months now we've been looking at the chances, a very strong chance of the buy signal which was upgraded to a buy mode going to at least a peak D in the Chapman Way methodology. We've got that doji candle which suggests that we could store for a little while for the five days. All of last week we saw today big breakout to the upside and to the 33,083 level I've been saying 33,170, 33,220 areas should be strong resistance and then we should continue this sideways choppy move. We're not complaining, we're holding on, it can go higher, no problem there. What we're all looking at here is that the S&P has also now gone to that leg E and it's just, it actually touched today at 41,86, the 200 period expansion moving average and our target of 41,77.51, that was the higher ground about the 2nd of June before it tumbled down to the 36,36 level. So all of all of the targets have been met. I'm going to be discussing this in great detail on Wednesday in my five hour webinar based on the Chapman Way methodology. How you can add, how you can enter positions, how you can add, when you start taking off a little bit.  There's a possibility this could be F slash B. B would be really positive but you've got the 200 period moving average of three, I think is 327 coming up and we're at 325,97. Fabulous move going to the Chapman Way inside track. I'll be discussing this particular technique on Wednesday as well. Look at the IWM. The IWM has spiraled. This is one of the best moves it's had in a while. Two days of a huge move up to the 200 period moving average and that's now trading at 194,19. This is really good because it's suggesting now that the small caps are beginning to find favor. We wanted to see that. Gold is up nicely. It's up 8 at 17.99. The dollar is, let's see what the dollar's doing. Dollar's pulling back a little bit at 33 ticks and we've got crude oil. So folks, stay tuned. We've got great programming here. I'll be coming up for the Tiger Technicians Hour. Who is sitting on this 200 period moving average. We've got Larry, Larry Percevento taking the one o'clock time frame and Steve will be at 11. I'll be back in a moment. Hope you


Welcome to Toffie TV, it is the Everton News Daily. Everton are interested in Stuttgart striker, Sasha Kaladžik, the 23-year-old striker has scored 13 goals in 24 games in the Bundesliga this season and got himself four assists in the process. Not the only Stuttgart forward Everton are being linked with, Everton heavily linked to Silas Waminga Tuka, who also bleeds for Stuttgart, but according to sports witness Everton are interested in signing the new Eiland Harland, even though he's a bit older than him. This summer, 23 years of age, Austria striker, yet he looks like a good player being linked with Westam Liverpool as well as a couple of clubs in Italy and Red Bull Leipzig as well. So Everton clearly looking at adding another forward, you know, goals are being hard to come by at times this season despite Dominic Alderlun scoring 19 and Retialas scoring 11. Everton want to add more firepower to their squad. Looks like a good player, this fella. He's been quoted or sorry, the fee reported is around 20 million euros. So, decent price bracket as well. So, interesting to see whether Everton actually follow that up. But quite clearly they've been present at a few of the Stuttgart matches. So, one of those two forwards looks like Everton are interested in. Former Everton goalkeeper coach Hugo Oliveira has said that Jal Virginia could be one of the best goalkeepers in the world. He says he's got all the attributes to do it. He said he can play with both feet so he can help the team out there, but he just needs to work hard and progress the way he's done. Oliveira thinks that Virginia needs a full season on loan somewhere to get used to playing and making big decisions every week, but he has no doubt he can become one of the best keepers in the world. So, there you go. Apparently Everton have seen a 6,000% share sale increase in Colombia. Unsurprising really following the signing of Hammers Rodriguez back in the summer. Everton already had done OK with Yeri Mina in that part of the world, but obviously adding Hammers Rodriguez has massively increased that. That's a real positive. Obviously Hammers Rodriguez not being fit too often or not as much as we'd like rather, but obviously Everton's popularity has grown in Colombia and in America as well. So, hopefully that will continue if you get Hammers fitting and back for these final 10 games of the season. Just following on from that, Colombia manager Jorge Luis Pinto has criticised and questioned Hammers Rodriguez and his preparation for his physical condition. He said that 29-year-old has had far too many muscular injuries this season and says you have to look at his physical condition and his preparation. He has to rethink his personal things and the way he does things and assess with his coach and the physiotherapist to ensure that these muscle injuries reduce. Is it the way he's preparing for games? So, he's some words there for Hammers Rodriguez. I'm sure Everton just wanting fitting healthy.  If Everton had to get into Europe, they're going to need him fit for the majority of those to try and turn these frustrating games, particularly at Goulders and Park, into victories. Hammers might be key to that, so hopefully he will be back and healthy very, very soon. That is it for the Everton News Day. Make sure you subscribe if you haven't. Give the video a thumbs up if you like it and if you want more videos, join us on Patreon. See you later. Thank you.


The best way to improve functional flexibility is to lift weights. All right, let's talk about this a little bit. All right, well, let's define functional flexibility and how is it different than regular flexibility? I heard that term in a while. It used to be the buzz term of the day. Yes, so there's a myth out there that resistance training makes you tight. You lose mobility, right? The myth of the stiff, you know, tight bodybuilder who's got really bad flexibility. So, you know, what you said, Adam, I think it's important. Let's define functional flexibility. So, regular flexibility is your ability to move through or be moved through a wide range of motion. So, if I'm on the floor, let's say, and let's say Justin grabs my leg and tests my hamstring flexibility, passively he would bring my leg up and bring it, you know, far back or whatever. And that would be my flexibility. Oh, yeah. Functional flexibility is different. Functional flexibility is flexibility You can control it through that. You own flexibility that you have strength through. So, I like to use this example, right? I have a 15-month-old son. He's extremely flexible.  But he's got almost no functional flexibility, right? Because he has no strength. Flexibility without control and strength is instability. It's actually one of the highest risks of injury is being hyper-flexible but also being weak. Now, proper resistance training, and I say proper because this is the important thing to understand, you train in a very full range of motion and the goal is to increase your ranges of motion through proper mobility training and proper application of exercise. But when you train in those full ranges of motion, you improve your flexibility, but you're stronger in it. So, not only can you sit in a squat, but you can do it with weight and you can get out of it if you need to very quickly versus just being flexible without strength, which is totally unstable. And again, a recipe for injury. The best example of this that I have ever seen in myself that like blew my mind was one of the first times that we hung out with Dr. Brink.   Yeah, almost to hit your butt. All the way, like I was looking at it. So I'm sitting here in the 90-90 position.   And it's like, I, you know, like a half an inch, I could bring it off the ground and that was it. So that was flexibility versus what you actually own. Right, right. So, and it just kind of blew my mind that I technically have that flexibility, but I've lost all the control and strength. It's not usable. In that range of motion. And that's, I was like, wow. And there's an examples of that with every joint, right? Everybody has that, but that was the greatest expression that I'd ever seen someone or had someone show to me how much I was lacking in that area. He did the same thing to me and it was strange because I was same thing. He brought my foot way up here and I looked at it and I felt detached. It was like I was looking at another foot. Now to take it a step further, imagine being put in that position and then someone jump on you or you have to get out of that position real quick on your own. You will tear your hip. Injury. You will injure your knee. You'll injure yourself. So, and if you, you know, think about this, right? Imagine somebody stretching your, just your pack. This is an area that most people don't really think about, but imagine bringing your arm back as far as you possibly can or someone bring it back and then you have to explode out of that position or move, right? You wouldn't do it because you know, you know, instinctively that would tear, that would hurt my, that's the difference between flexibility and functional flexibility. This is why a lot of, you know, like if we go back to our certifications and we look at all the limitations in terms of the degrees of range of motion where they stop you because of safety protocol, it like had to throw all that out because like that really wasn't preparing a lot of the athletes I was training for success on the field. In fact, it was limiting their abilities substantially in terms of them because when you're actually moving and aggressively moving on the field and all these different variables and different ways that you're controlling your body, you're in pretty crazy angles. And you have to be able to know how to navigate and have access to that range of motion in order and be strong in that range of motion in order to thrive as an athlete. Yeah, I remember when there were some breakthrough studies that came out that flipped what we thought we knew on its head in regards to warming up. In the 80s and I'd say early to mid 90s, the way you warmed up before a game or before a competition was you did static stretching.  And this was just standard, you know, in PE class is how we warmed up and this is what you would see athletes doing before a competition. Then a study came out that showed that static stretching before competition increases risk. Increases risk of injury. Reduces performance and increases risk of injury. And you think to yourself, how is this possible? Like I get looser when I static stretch, how am I hurting myself? Cause this is what happens. When you stretch a muscle and you hold a stretch, the muscle isn't becoming looser or longer. You're not, it's not like a rubber where a rubber's cold and it's not flexible and you warm it up and it gets more elastic. That's not how it works. What it literally is is your central nervous system keeps a muscle tight. And when you hold a stretch long enough it sends a signal to the CNS, tells it to relax. And then it kind of loosens its grip on that muscle allowing it to stretch out further. So static stretching temporarily increases your flexibility and your range of motion by shutting off the CNS a little bit. So now you go off on the field and you run and you're kicking or you're jumping and you move in a new range of motion you normally wouldn't because your CNS now has allowed that muscle to elongate a little more but you have no strength in that new range of motion. Boom, injuries are more common. So they found like stretching the hamstrings, static stretching caused more hamstring pulls than when people did nothing at all. This was compared to nothing at all. Now of course there's a superior way to warm up and that's dynamic and priming which is turning the CNS on. There's just a massive difference between passive and active. Yes. Which is where the active is really where they found the most benefit when you're approaching a lot of these types of like ranges of motion. Can I have access to that? Can I control my body to go in and out of these degrees of angles that are a little bit like further than these 90 degrees sort of stops? Yes and also when you think about everyday life you don't need lots of flexibility you need functional flexibility. To be able to get into the splits it's not really valuable in everyday life but being able to squat down or rotate well let's say your kid spills something in the back at the car and you got a turnbill fast or you're walking and you step off a curb or you lose your footing a little bit and you move in a new range of motion but you got to control it and have some connection to that so you don't hurt yourself. That's the important kind of flexibility that you need. Now the super long ranges of motion they can be valuable for certain sports but in those sports you still need like for example if you're a taekwondo you know if you practice taekwondo for example and you need to do these really high kicks you need that range of motion but that range of motion is worthless if you can't bring your leg up yourself and you can't control that range of motion. So static stretching does have a place in improving functional flexibility but it has to be combined with some kind of resistance but if you compare head to head traditional flexibility type exercises and programs and traditional being mostly static stretching to good full range of motion appropriate resistance training the functional flexibility from resistance training is superior will result in less injuries will make people feel more stable in their everyday lives will decrease the pain in everyday life more than just improving flexibility. So there's the myth of by the way the reason why you see some people who do a lot of resistance training who are very tight because I know people are watching right now saying well that's true that's part of that is not true because I know bodybuilders and let me tell you I know a guy can't even wipe his own butt he's so tight. Part of it is there's muscle gets in the way when you're massive but here's the other part when people train and shorten ranges of motion which a lot of bodybuilders do. That's right when you do that you build strength in a short range of motion which means it's disproportionate to the range of motion that you maybe don't own but that you have so you essentially make yourself tighter. In other words if I only do quarter squats and I get really strong with quarter squats I am gonna move very tightly because my body's gonna know you get outside that quarter squat position you have no strength, you have no stability. So when I say functional flexibility with resistance training I'm referring to appropriate and full ranges of motion and making that a priority with your training. That's what'll give you that functional flexibility. It also build a lot more muscle that way too. That's true. And you have to, I remember when I, so I used to be the 90 degree bench press guy for a long time. That's what we learned in our service. Yeah so for a long time and then I remember reading and learning how important it was for me to take these joints for full range of motion and then getting to the place where I would do a bench press where I brought like dumbbells all the way down when I was, and initially I was weaker. So initially doing that I had to pull back. I was able to, back then I was doing like 110 pound dumbbells. I had to scale all the way back to like 80 pound dumbbells was now my, like my new max but it didn't take long for that to catch up. And then when I finally got to the place where I was now pressing that same weight that I was pressing before in the shortened range of motion I had more muscle than I had built. So there's tremendous value besides like health and protecting your joints. There's also those that are paying attention that like all I wanna do is build this amazing physique. Well, you'll actually build a better physique if you take your body through it's fullest range of motion also. Yeah there was a debate maybe 20 years ago where they would say, you know maybe partial reps are more effective, true. You can't get a full range of motion but because you could load partial reps so much more that offsets the fact that you're not doing a full range of motion and therefore you should be able to build more muscle. In fact, there was a whole book that was sold on this and it was about, you know lifting in these quarter half ranges of motion but just maxing out the weight and they tried to sell it. No, studies actually show even when you do that, it doesn't matter fuller ranges of motion if you compare head to head just build more muscle. Head to head in long term because you could show a study that shows, you know somebody who's training in this light full range of motion and you put them in a small six week window where you overload, you know more than 110% of what they would normally be doing through full range of motion. Absolutely novelty. The body is going to adapt it's going to show it's going to build some muscle. So that's how they cherry pick stuff like that and to try and prove their point but the truth is over an extended period of time training through a full range of motion is going to benefit you not only health wise but then also for building muscle. Hey, if you enjoyed that clip you can find the full episode here or you can find other clips over here and be sure to subscribe.