What's good everybody? It's Chris from jachescaseface.com. Happy Saturday! First break of the day, gonna be a mystery crate spot giveaway the last one we're doing. 2023 Bowman Baseball Blast, a random team number 22. So a few random spots to do. First off, we're gonna give away six free spots in this break itself. Then we'll randomize our teams in the break and at the very very end that last mystery crate spot being given away. So let's get started here with that spot giveaway. So our 26th, 24th name, excuse me, Allen, Chad, Edward P., Carl, Kiam, and Carl. We'll roll the dice and randomize four to one five times for a sixth spot giveaway. Good luck y'all. Thanks again for getting this one filled out. One, two, three, four, and five. Fifth and final time. Top six get those free spots. So Carl, Allen, Carl, Edward, Carl, and Carl. Very nice. Top six right there. So our fresh dice are now full. Fresh list of 30. With the arrow next to your main, meaning you won those spots. We'll put that last spot mojo in there for Carl. And our 30 teams from the D-backs down to the Nats, let's roll this dice and randomize three and three six times. Good luck. One, two, three, four, five, and six. Six and final time. Chad, down to Carl. And six times for our teams. One, two, three, four, five, and six. Six and final time. Rangers down to Blue Jays. So since this is just a filler, I'll pause the, you can pause the video if you like when I show it off. We'll show how the dice rolled out, how everything goes right here. So Allen with the D-backs, all they down to Carl in the Nats. Pause there if you'd like. But we do got some work to do. So let's go do the work. The switch screens, our bow and blaster. No paper ships in this break. Gonna be just the chrome cars, but any of our bigger rookies or prospects. We'll keep it on obviously. So number 22 at the top. Allen with the D-backs, Carl and the Nats. All right, this is for you. Well, that's the basketball news. So we'll have the free agent show on in the background, but lots of trades, lots of surprise signings. We'll be topping just treated about 30 minutes ago to the Indiana Pacers. What's here we got here in our Bowman Blaster. How about Jackson Holiday? Paperata. Look at that. On the top prospects in baseball. Going to the Baltimore Orioles and Carl. Not too bad. From a packed filler. Had the important Carroll in there. Doing the top rookies from this class. Justin Crawford on the top. Chases in this one for the Phillies. Also going to Chad. All right, there we go. Oh, Jackson Holiday Auto. We'll take that, but let's switch screens. Let's get to the real fun stuff here. Let's grab these names one more time from Allen down to Carl. And our last fresh dice and our last fresh list. Top name after this giveaway or this randomizer will win that last mystery crate spot. All right, let's roll the dice and randomize. Two and three, five times. Good luck again, everyone. Thanks. Let's do it. One, two, three, four, and five fifth and final time. So from two down to 30 sad times. Thanks for getting in there. Thanks for giving it a shot. But congrats to our top spot at number one. Gonna be Chad. Nice, Chad. Congrats. Let's grab this one. We'll call it. There it is. Right there, Chad. Congrats again when you see that spot in the next break. Gonna be our mystery crate high roller number eight. You know you want it from this break right here. All right, thanks again, y'all. jassescasebreaks.com. Bye-bye.


So I'm Rajesh Kasthoyar Angar, so that's me and that's Anand over here. And welcome everybody to this Thinking with Data course. It's the first for Nias in several ways. So if you've never been to the National Institute for Advanced Studies, well you are at the National Institute for Advanced Studies. We don't do anything here which is either elementary or medium. We only do advanced studies over here, right? So if you don't want to do advanced studies, I suggest that you get up and leave right away, right? But if you are gung-ho about advanced studying, then this is the place to be. Our course on Thinking with Data is a first for Nias in several ways. First of all, I think the first time we are doing something that's open to the public, not as a one-time event, but as an entire course. So those of you who have registered for the course, well, you're part of this experiment and thanks for participating. Those of you who are from Nias, well I hope that having so many people brings a new dynamic to the way courses are run. The second thing that we are doing, so Anand here who works for a company called Hasgeek, is going to be videotaping these sessions and we're going to put them online. And there are many people across the country who are interested in this course. So there will be participation in the course from people who are not in the room right now. So that's the second thing. What I am going to do today primarily, let's get started with that in a minute, is to lay out the framework of the course as a whole. Many of you have probably not been in school for a while. So how many of you have not taken a course in the last, let's say, three years? I course you with me. Meaning like you have not sat in a room every week for say 10 weeks? Oh yes. So that's most of the people here. So as a result, you know at Nias that's what we do, we are an academic institution and so we run courses and which means that there are a few cardinal rules that we always follow. In which everybody in this room did today because we are going to start at 10.30 and everybody was here by 10.30. But it's a course that starts at 10.30 every Monday. So let me start with the logistics here. It will not be in this room from the next week onward. It will be in the main lecture hall in that building which is actually a much better room for instruction but unfortunately today it was double booked so we couldn't get that room. And so that's where it's going to be. It will be two hours of lecture come discussion. So if you are not used to sitting still for two hours, take your written in or whatever drug that you need to and make sure that you can sit in one spot for two hours. The primary way in which the course is going to be organized is lectures here and discussions and projects offline and online. So the projects are really the main thing in the course. The course is really an excuse for getting some interesting project stuff. And we have a very diverse range of backgrounds here. So many of you know write code for a living and many of you write words for a living. Hopefully the people who write code will talk to the people who write words and do something interesting in combination.  So pictures, videos, words and broadly speaking. So every one of you here is going to be assigned to a project. We still haven't decided on the final projects partly because we didn't know how many people were going to show up. And by speaking of between, let's say 30 people in this room and some of you might be coming in and out. So we have a lot of live projects before the project. And the way we want to do it is to mix people of different backgrounds so that some people who have interesting questions get combined with people who have interesting ways of addressing the questions. So projects are the real test of learning. It will happen as much online as in this room because once you leave the room you can of course go and check back on the lectures. We will potentially be hosting this class in another location at the Center for Internet and Society at the other end of Bangalore. You don't know. Where if you can't make it during the day but I hope all of you who have signed up are going to come here on Monday mornings. But if you know people who are interested but just cannot come on Monday morning we are hoping that it will be hosted on a weekend or on a weekday evening at the Center for Internet and Society. So that's the other thing which means that there will be people who want to participate in the course or not coming here. And so some of the online discussions will be important. So the first thing you need to do if you haven't done so already not register for the course but to register on the site. Because once the project groups get divided and you're all assigned to a project group we want that group to be documenting its progress. And the only way we can do that is by doing it in my mind. So please do document your go to the website and register. So there's a register right there on the front page so you should be able to find that out. And we will use those registrations to drive the projects. So hopefully by next week you should have a project and every week from then on we hope that you will submit as a group, not as an individual, something pre-requisites and Anand is going to talk about this later as well. We don't expect too much in the way of pre-requisite knowledge but we do want to warn you that some exposure to Python and HTML will be desirable. So if you want to get the maximum out of the course now this is not a course except for the Nias people who are taking it for credit. Everybody else is doing it for fun or some other noble cause. So really you don't need to do anything but if you want to get maximum benefit out of the course you do need to know enough Python and HTML to do the assignments. Ideally you are part of a group where there are other people in the group who know some of this stuff so they will be able to help you out. But we are going to organize a crash course next weekend at Grammar in Indra Nagar. Anand again mentioned that. And if you therefore are interested in getting that crash course I especially urge the Nias people who I am almost certain none of you have actually written a line of code in your life so if that is the case please do sign up for the crash course. So these groups will be mixed so that you complement each other's expertise. Let's put it this way that 500 bucks for an intensive workshop in Python and HTML you are not going to get that deal again. So the perks of this course are going to astonish you as the course goes on. Okay so what are our goals in doing this course? So it's really an experiment in several things. The first is at least from my perspective I think of this as a core course. So for people who are not from Nias and also the first year Nias students you know that we have these two foundation courses at Nias that everybody is supposed to take. So I think of this course as a kind of foundation course meaning these are skills that everybody should have and unfortunately because they are new or they are packaged in a way that are not done in the same way elsewhere you are unlikely to get these skills as part of a single package. So extracting data, analyzing it, visualizing it thinking about it as you do all of those is something which is a standard skill. It's like learning how to do quadratic equations. And I'm sure that people here who don't know how to do quadratic equations either so please add quadratic equations to your list of things that you should know. But in this course we will be essentially teaching you the thinking skills that will hopefully be useful whatever you do. And the mark of that success is how you can execute some interesting projects. And I'll come to what kind of projects which will be interesting. When I say interesting I don't mean that they are just interesting to a classroom. I mean projects that if you do well will be either publishable if you're an academic or will have some value to you as a business person if you're an industry or if you're in a civil society group it will allow you to do your campaigns in ways that you cannot do otherwise. So the interesting is defined by the world outside not by this solar system here. So the projects combined with the skills are really a way to bootstrap a community. Now Anand here has been organizing data needs for a while. So working with big data is now something that lots and lots of people are interested in. But not too many people know how to do it. And I would venture that knowing how to think who data is an even rarer skill partly because as I will say in a bit the nerds and the geeks don't talk to each other. So what do I mean by that? So in case you want to understand the sophisticated terminology geeks are people who write code for a living and nerds are people who write words for a living. I mean this is my very very simple way of distinguishing those two. So probably these two don't talk to each other. And my guess is that most of you who came from the geek world until you signed up for this course and you came to Nias you probably did not even know what Nias was. And in the nerd world that wouldn't be that uncommon knowledge. And the other way around, we actually don't know what you guys do in Nianagar and Jainagar and all those places. So the idea is that the people who write code will find interesting problems to work on with people who write words and vice versa. So this really is where the proof of the pudding is going to be. So let me start with examples of things that I feel are thinking with data kind of examples. So they have certain characteristics. So let me start with an election campaign.  And incidentally if you are working for an NGO who wants to run a campaign same kind of ideas. How would you do that? And how would you use data to do that? Now to give an example, there was a graduate, my friend Ashwin, grant for the graduate constituency in Bangalore. Now Bangalore has 1.1 million graduates. So the constituency in principle had a million and 1.1 million people in it. It turns out that only 23,000 of those voted. So look at the percentage. And the winner won by 400 votes. So which means that 400 out of a million was the deciding percentage which is what points 0 to 5. Slightly less than that actually. So which means that a very minuscule percentage of people decided the winner. And incidentally it turns out, in fact I think there is a lawsuit that's going to happen. The person who came second can convincingly argue that votes that were disqualified because somebody instead of just so ticking the right candidate's number ticked their name and then put a circle around it or put a circle around the whole candidate's name. So if you just take these kind of what you call completely certain but disqualified votes. So people whose intention was absolutely clear but they just did something that got them disqualified because of the rules. Are something like 750 and most of them went to the person who came second. So actually it turns out therefore that the difference between the winner and the loser can be covered by just these disqualified votes. I mean if you remember there was a US election which now increasingly decides the way the world has run for the last 10 years was also decided in the same way. Rats of 500 votes in Florida was the difference between Bush and Gore. So what this is telling you is that if you want to run a good campaign and in a winner take all kind of system where the winner gets everything the loser gets nothing.  And I think that Obama-Romney election for example will be decided in exactly that way. In fact already people are saying that there are about 400,000 voters in a few districts in perhaps Ohio, Florida, Colorado and a couple of other states who are going to be the difference between Obama's winning and Romney's winning. Again out of a possible electorate of about 160 million people. So these very very, you can't just look at the data and ask how to run a campaign to make your drive win because it's very unlikely that you will without prior hypotheses about how to understand the data you're not going to be able to pick out those 400,000 people or those 400 people who are the difference between winning and losing. So how does one run a campaign in a way that you get exactly the people that you know you're going to get. I'm sure it's going to happen so you know later that you'll have Facebook ads for vote for so and so. And it may, if they're smart, what you see when you vote for so and so would be different from what the next guy sees when they see vote for so and so. And that kind of smart analytics is not too far but if you don't know how to do it or you think that it's magical then you're going to be suffered. So if nothing else we need to know this how so that you're not being propagandized. Now the second thing I want to talk about, so this was not quite in the deep world but at least it's something that a lot of you might easily understand the microbiome. So how many people in this room have heard the term microbiome and who haven't heard it from me? So the microbiome is the bacteria that live in your gut. It's as simple as that. And it turns out that there are 10 times as many bacteria in your gut than there are cells in your entire body. So in that sense, you are justifiably just the house in which those bacteria live. As far as they're concerned, you are not a person but this entity which houses them. Now it turns out again that these, so if you have 10 times as many bacteria in your gut as your cells you can imagine that these interact in very, very interesting ways with you. It also turns out that all of us have a unique microbiomic signature. So every single person has a unique microbiome and this is amazing. So you should read this as an economist in the latest one if you haven't done it already. If you have identical twins, identical twins may still have differentiated microbiomes and that might be the difference in let's say nutrient intake. So it may turn out that people who are 10 are different from people who are not not because of their genes so to speak but actually because of how their genes interact with their microbiome. So this kind of personalized medicine of actually analyzing this microbiome is going to become huge. I mean the microbiome project just released its data about a month ago and India for example will be fantastic to do a large scale microbiome project in India. So you can imagine the kind of stuff that lives in our gut and the microbiome diversity of India if anything will rival the human diversity of India. So that's the case. You can imagine the kind of interventions in public health that you can do in a country like India if you understand this microbiome well and that can only happen if you take people who know how to diagnose diseases or other medical interventions with people who know how to analyze data. Ideally it should be the same person but right now your average doctor who writes that scribble on a piece of paper wouldn't know standard deviation from clearance. Correct me if I'm wrong but typically they wouldn't. So therefore this kind of skill I'm saying is standard. It should be something that all of us know and once we do we'll be able to do our jobs better. So addressing these questions requires what I call thinking with data. It's not just data, it's not just analysis but it's thinking with data. What do I mean by that? So there's a kind of bottom up way of understanding data and when I say the bottom up it means collecting the data, run some statistics on it, show some significance and then call it quits. This is what a lot of us do even in the academic world when we are collecting data on let's say A's make up something but A's tuberculosis will correlate it with caste to take two things. Now you may collect a lot of statistics about who has TB in India and what caste they do but typically the kind of analysis that will happen are of a bottom up kind which is to say the correlations between these two categories TB and caste. Now of course there will be something significant there. You can be pretty much sure that lower caste people in India are more likely to get TB than upper caste people but unless you talk to a sociologist or to an anthropologist you won't really understand why is it that upper caste... I mean we have some intuitions as to why upper caste people are more less likely to get TB but to do a genuine public health analysis you would need to do something more than just what the data in front of you tells. You need to embed that data in a broader understanding of how society works and that is not what comes from machine learning. I mean at some point in the future there might be a machine that crutches political science and anthropology and sociology and just figures all of this out but until then it's people who really understand how experientially the world works who will have to talk to people who know how machine learning works. So the data is actually much more interesting if you can tell a story. So again if you take caste and TB you can tell some very interesting stories about the relationship between caste discrimination and public health. So one very obvious kind of story to be told is about the access to water that somebody who is of an upper caste background has versus somebody who is from a lower caste background has. Now that kind of story about how access to water, clean water, I mean determines public health is pretty clear but you need to plot carefully across India let's say what kind of water do people have access to and what is the caste distribution of that and that would give you a much better story about how TB and caste are related. So which is why some data points are more relevant than others and we need to figure out where that relevance comes from. Where do you get what is more valuable data? You can't get it from the data itself right now. So there are learning algorithms that are beginning to get those out of the data itself right now. You need to be able to make why some data is more relevant than some other data. You need to be able to explain the significance of a fine. You need to be able to convince me that this particular data point is actually far more important than some other data point. Now let me give you an example which we all sort of think of as zero. You know that the earth goes around the sun instead of the sun going around the earth. Every single day you get one data point which suggests that the sun goes around the earth. You wake up and it looks like the sun is moving and the earth is still. So every single day we are collecting data on why the sun is going around the earth and yet at least our current science suggests that it's the other way around. It's the earth that goes around the sun. So how do you ever show that? What is the reason why you believe that the earth going around the sun data which was painstakingly collected by Copernicus by looking at the epicycle regressive motion of the planets is more relevant than your everyday experience? I mean that suggests that there's some theory in the background that tells you know this data is more important than that data. And that's what you could argue that Sherlock Holmes sort of taught us. I mean there's a very famous quote in one of the Sherlock Holmes stories. I don't know which one it was, maybe somebody here knows. If from the improbable you take away that which is impossible what you're left with is the solution. I don't know if Anand is probably online so he can Google it. But that really is should be what I call the data actives method. You need to know what kind of data is relevant and what is it. Incidentally again if you remember the beginning of Sherlock Holmes Watson makes this list of things that Sherlock Holmes is good at. He doesn't care about the earth going around the sun or the earth going around the sun. So he has no interest in broader culture but he's extremely good at identifying where each soil fragment comes from. So that kind of real eye for detail and knowing which things matter to your particular form of expertise I think is one of the great skills of a good data scientist. So do people actually think with data? This is where my work as a cognitive scientist becomes interesting. We all would like to believe that we are rational, smart, data driven, analytic people. But actually people are not. So a very interesting experiment by Lerapur Ditsky who is at Stanford showed the following thing. So suppose I take a group of people who are given a story, a description which either has some statistics in it or has a metaphorical description in it or both. So an example would be crime in L.A. grows by 17% last year versus so that's the statistical fact or there was a crime wave in L.A. last year. It turns out that if you do a control experiment and they did a very interesting control experiment it turns out that people who are given the data and the metaphor perform much more like the people who are given only the metaphor versus people who are given only the data except that they all say that they use the statistics. So they think that they are thinking with statistics but they are actually thinking using the metaphor. So that goes back to the previous thing, telling the story metaphors help you tell a story much better than raw numbers do. And therefore even if you want to be data driven what you do with the data and how you represent it either visually or numerically or whatever other modality you use will actually be far more relevant to the end user of that data than the raw numbers will be. And my own bet therefore would be that diagrams so if somebody draws a diagram which goes probably like this these are the two axes and somebody shows that that's how crime is going. So it's like an S shaped curve that would be far more convincing. The numbers on it would not be convincing but the shape of the curve would be far more convincing than an S. So what I'm saying therefore is that thinking with data and doing it in a way that takes all of the sensory modalities that we have whether visualization of course is the most important one but other sensory modalities is actually not a bad idea. So there's a way that the stories are told with data to learn as much from storytelling in other kinds of stories as with statistical analysis data. Can I go ahead? As you're talking about this, my mind is going towards Gandhi man the use of heuristics and decision making that we made. This is all tied to that. So if you're thinking with data you're just using data as a tool for your thought rather than being the end. So unless you're a statistician data is something that you use for other purposes rather than just crunching it. You should be able to synthesize your findings, deliver the story. So in that sense thinking with data is not about artificial intelligence it's not really about machine learning as much as it is about how humans can do the things that they are already doing but with data in a better way. So for example if you want to persuade somebody if you have a business which wants to say buy my product over the next guys you are actually communicating a story. So how do you do that better with data?  So if you look at diagrams in science journals I often have a very hard time understanding figures in science journals because they're so opaque. So I would say that a huge revolution in science can happen just by if everybody is forced to read Edward Tufti's books by three times. All of this and which actually has been severely questioned by the Kaiman, Turskis and others in the world but nevertheless the ideal model is that of the rational agent so a rational agent is somebody who thinks with data by extracting as much information as possible from the data and communicating it to the world. So the idea is there is data, you can extract information and you can convey what you extract as succinctly as possible and the rational agent is someone who maximizes both of those. Who maximizes both the extraction and the communication with minimal resources. So broadly speaking any kind of information designer would try to design systems which help you extract as much as possible and communicate as much of that extractive information in as efficient a manner as possible. So people may not be perfectly rational so again this Lera Balditsky work that I mentioned and work by Kaiman and Tursky about heuristic biases have shown that we are actually not rational beings but nevertheless we can aspire to be one. Rationality is something which is a desirable goal even if we actually are not rational and you could argue that because the data if it is collected accurately without error doesn't lie you would be able to predict the world better than the guy who is making up stories that are not grounded in the data. So just because other people are irrational doesn't mean that we should be. So for example when I say RA I am an rational agent so suppose you are a government investigator and so here is something that would be in India very very important public health intervention to be done. We all know that there are all kinds of fake drugs in India you want to know where those fake drugs come from or not in the fake drug domain we know that in India doctors often over prescribe antibiotics so you want to know where so if you are finding out that there are new TB Basili that are more and more resistant to drugs you want to find out where is the over prescription to antibiotics coming from so that you can do something about it. So how do you use data to figure out where in India is the source either of these furious drugs or of the antibiotic over prescription. Now similarly at the other end imagine a Kirana store so just walk up and down the Matikire road over here you will see lots and lots of small stores. Now they all have to make a living their living depends on having only those things in stock that they sell and as few of those things in stock that don't sell except that of course you don't want to be completely out of stock of things that are only rarely sold. So how do you again maximize your inventory in such a manner that it depends on the data that you are getting from the world about interest in those products. So you are a stationary store outside you have various thicknesses of books some are hard bound, some are paper bag on the one hand you want to make sure that people buy the most expensive hardback book that you have but most people don't want that so how do you market how do you structure your store so somebody comes in they need to see your store how do you lay out your store so that people buy the most expensive thing that's on your stock and yet they also get what they want if they know exactly what they want. So this is a Kirana store you might think that they actually don't want to spend too much time thinking about layout design or data collection but actually if anything their business model will be far more efficient if they did so then a large chain like food world reliance timeout or something like that where they have people on the payroll who are doing that so how do you make data so transparent that a Kirana store owner can also use data to maximize their revenue that's sort of a small business model and note how imperfect your knowledge of the world is and how little time you might have to make sense of it so take again that Kirana store guy he is only getting input from the customers in front of him unlike reliance timeout he doesn't have access to data from across the country or even from his neighbors who are telling him what's selling and what's not so you have to make decisions on the basis of extremely impoverished information and you want to do it in such a way that you can react to the world immediately rather than many years in the future in the long run they are all dead so you have to make the invisible visible this is what mathematics is supposed to do mathematics makes the numbers and quantification makes the invisible visible so you want to be able to measure the world, you want to analyze it and visualize it all while being a rational agent and by doing so after the end product of the visualization should be something that makes transparent what was completely inaccessible before so for example it may turn out that if you take three or four products that are in stock in a Kirana store there is a clear pattern as to what rate they are being consumed and therefore what rate you need to order it so thinking of data if you take these three things here as the goals therefore you have to think so I am again coming as a theorist where the thinking comes first and the data comes last and I might want to flip that a little bit but we will play a good cup and a bad cup so if you think first you extract data on the basis of the hypotheses that you derive and from that you combine the thinking and the hypotheses you have a reasonable chance of success and incidentally this is what happens in what's called Bayesian reasoning you have a hypothesis, you have a hypothesis that says the sun goes around the earth and another hypothesis that says the earth goes around the sun and then I go test the world in some way and then I combine the outcome of that test with the hypothesis that I already have and say oh it's the earth that goes around the sun so that's what people think an ideal researcher should be doing so what is thinking? now there are many many definitions and you know if you are a professional student of concept formation and reasoning and so on you have many different ideas of what it was to think but for us it's just a capacity to form beliefs to reason about those beliefs once you have formed them and to test those beliefs against the world that's probably speaking what I consider thinking it's something that starts with a belief and ends with a hypothesis that you can test against the world ok so suppose that you leave your house in the morning so this is the monsoon season you leave your house and you see water on the pavement in front of your house was it rain or water mean that burst the gardener spraying water all over which one of those things is it so thinking is about forming one of these beliefs one belief would be it rained yesterday night another belief would be a water mean burst yesterday night a third hypothesis would be the gardener was watching the plants 10 minutes ago right so these beliefs are the first part here so we have the capacity to form beliefs then you reason about those beliefs and then finally test those beliefs against the world right so how did that happen now it's August we all know it's been raining this year it wasn't raining until recently so you see water in front of your house and you form the belief that it must have rained yesterday night you even heard some thunder potentially so you did not form this belief on the basis of explicit reasoning but most of us when we leave our house with an umbrella or something like that explicitly reasoning in our head saying oh I saw lightning and therefore I must leave with thunder no it's like I'm subconscious you hear thunder you pick up the umbrella and so therefore this first step is sort of it's not explicitly rational so we need to first convert in order to do data science you need to convert these subconscious intuitions into explicit beliefs that you can reason with how would you reason explicitly so the first kind of check in your reasoning system would be is it plausible to assume that it rained yesterday night now the way I want to defend it incidentally this is what Indian logical systems do so the famous famous example in the Indian philosophical logic is it you see some smoke and you conclude that there was fire and so where there is smoke there was fire how do you do that? you say that well wherever you have seen smoke there was fire like for example in the Chula and therefore in this case because there is smoke there must be fire so that's an example of reasoning with data there is a middle point which is data that whenever I cook chapatis or whatever in my house and there was smoke it was caused by fire so that thinking with data is souped up is what really we are talking about so we are saying ok it's August it's cloudy there's water on the ground and therefore it must have rained now if you are a rational agent again you have to test that belief so for example there might be water in front of your house where there is no water in front of any other house again you have a tested understanding that if the rain fell from the sky it wouldn't have scattered rainfalls so non uniformly that there is water in front of your house and not in front of the next sky so think about it this way right therefore tacitly there is an understanding of the probability distribution of water on the ground given that there has been rainfall ok there is no smell of rain yes you all know there is a distinct smell of rain when it first comes on the ground and therefore you can prove that maybe rain is not the correct belief that even though it is August and it could be rain it actually is not rain then you go around and you notice that there is water gurgling next to a drain right and therefore because there is water that's gurgling from a drain no other drain is gurgling and therefore you conclude that it must be the water in your drain that I mean the drain in clog that must have led to the water in front of your house I mean most of us are really not that rational but this is what it should be actually even this need not really be true because it's the rainy season and there is a lot of water it's much more likely now that a drain is clogged because of excessive water than in some other parts of the year so actually the real reason could be a combination of both rainfall and water blockage right which is to say that this particular drain got blocked because of excessive pressure on that particular system as opposed to some other drain which were able to handle the rainfall that came about incidentally this is how medical reasoning goes right so if you're trying to diagnose what disease somebody has this is exactly how you would go about doing it so suppose you want to find out somebody is having that's a pain in their left arm a lot is that due to a heart condition is that due to a pinched nerve or is that due to muscle strain somewhere else in the body all of these are plausible hypotheses in fact the real world is always like this that conditions don't all come to you cleanly saying this is what it must be because we are complex systems both our bodies and the world outside symptoms are clustered in ways that it's often hard to find out what is the actual cause of affairs in the world and therefore one shouldn't be one should be very careful about jumping to conclusions and that's where actually big data helps a lot because what big data helps you do is to repeatedly test your hypotheses against the world as opposed to those doing it once right so if all of this sounds familiar it's nothing but the scientific method that's what actually science is supposed to be a scientist is somebody who performs hypotheses goes and tests those hypotheses and then tells you whether that hypothesis is valid or not so what I'm trying to tell you the beings in this room and everybody else is that it actually will benefit you a great deal if you just understand how the scientific method works so all this thinking of data is nothing more than the scientific method but just done more carefully and thoroughly than most scientists normally do right so the scientific method in some sense was created with small data in mind so if you look at how many data points Copernicus had before he concluded that the you know the earth goes around the sun my guess is that it would not pass single journal, current journals statistical significance test right that doesn't mean that it wasn't a fantastic piece of science just telling you that our standards for how much data is actually relevant have changed so can we scale the small data mentality of science so from my perspective what's really really fascinating about the big data business is that you now have the capacity if nothing else to test your hypotheses against much larger data sets than you ever were able to before and that's as much true for a businessman or for a public official or anybody that is for a scientist right so what we need is techniques not just to cut the data but how to understand it right so another way of putting it is what's the science in data science data science is the fancy number of statistics well that's actually not such a bad thing statistics is a fascinating fascinating subject and it arose precisely to quantify how to understand large data sets so if you look at insurance policy well mathematicians discovered the normal distribution and that drives insurance policy right so you could argue that what's happening in the big data world is that we are finding out that the old normal distribution the bell curve so to speak doesn't work anymore the most real world data sets don't fit a bell curve and therefore if you want to be able to reason accurately about large data sets you need other kinds of statistics than normal statistics but broadly speaking there are two kinds of statistics one is what's called frequentist so you count the number of times something happens so you toss a coin 100 times and you find out that it came heads 47 times and tails 53 times and you keep doing more and more of those and you find out that as the number of observations grows larger and larger the coin more or less settles to 49 heads and 51 tails so you conclude it's a biased coin with a probability of heads being 0.49 and a probability of tails being 0.51 so it's not an ideal coin anymore right so that's how one derives the distribution but a Bayesian doesn't do it that way they don't get their data their distributions from the world they start with an assumption so for example you can assume that the coin is fair so you start with the assumption that it's 50-50 you make 100 observations and you find out that again 47 and 53 and then you recalibrate on the basis of that as to what is the most likely distribution that could have generated 47 heads and 53 tails and you keep doing that again and again until you settle down upon something now ideally in the long long run the two should converge so if the coin is indeed biased your beliefs about how the coin works should converge to actually how the coin works but in the real world you never get close large n number of observations you have to make your decisions now and that's the hard problem so big data actually shifts the debate away from long n decision making to very very rapid decision making so if you want to do science with statistics and you want to do all kinds of sciences with statistics what if we derive our science on the basis of statistical rather than deterministic assumptions becomes a very important kind of science to do which in practice social science disciplines are like that or biology also unlike physics we are not based on deterministic assumptions we don't have strict rules that say people only go from Delhi to Mysore there will be some distribution of people who go from Delhi to Mysore and you don't throw away your theory that Mysore is say the center of let's say yoga in India because there are some people who are going to learn yoga in Indore rather than Mysore so the kind of science that we do is often driven by statistics rather than deterministic assumptions but the more we do and the more statistics we collect I think there are different kinds of science we can do now so to just give you a brief idea Newton invented the calculus to solve this problem how do you explain the fact that the earth goes around the sun in such a way incidentally these are Kepler's laws that in a certain given period of time say one month independent of where the earth is or independent of where some other planet is it always covers the same area in the same period of time so in one month whether it is January to February to November it's still the same area this is what Kepler discovered in his laws and Newton was trying to figure out how to do that and he of course invented the law of gravitation and he invented calculus and he put the two together and he said this explains how one can get the Kepler's laws so we are essentially trying to do something here which is not that difficult we have huge huge data sets and we want to explain quantitative as well as qualitative behavior on the basis of those data sets but we don't quite have the calculus that helps you convert the data into insights we are still groping the dark as to what the calculus is and the reason here is partly because thinking with data is not like thinking with I mean thinking with big data isn't like thinking with small data sets so if you take very very large data sets which are dynamic so let's take Google's servers that are processing huge amounts of data every second or AirTel which is processing I don't know how many millions of calls per second it's very different from the Copernican model of data collection where you stare at the same thing every year and you can go back and you can go back and it's always going to be pretty much the same so where the moon was on January 27th of 2001 is a lot like where the moon is going to be on January 27th of 2002 but in these very very large dynamic data sets that kind of regularity is simply not there so if you think that data science is about extracting the patterns that underlie the data generation we need to have much more sophisticated idea what patterns are so what I mean is if you take a flashlight so this is the famous Bevel story remember Bevel was asked by Akbar find me the 10 stupidest people in Agra and come back the next day and so Bevel is walking around and somebody is there's a lamp somewhere and this person is frantically looking for something and Bevel says well what are you looking for oh my wife gave me this very valuable gold ring and I lost it and I can't find it anymore and so Bevel also helps him for like an hour and then Bevel finally says wow I can't find it where did he lose it and the man says oh well over there and they say why are we looking for it over here they said well at least there's some light here so there's a chance that we might find it but over there it's impossible so a lot of data for signs is like that you're shining flashlights in the dark and you're hoping that you'll hit something that's interesting but that's not the way our minds and brains work when you think about it every single time you open your eyes in the daylight you see the world it doesn't appear to you as flashlights that are shining in the dark I mean in fact we take it so much for granted that the world is what you get when you open your eyes I mean just imagine what your sensory experience of the world would be if it was like sporadic flashlights that are shining like that so our brains and minds are the original big data thinkers I mean every single second you're processing billions in the trillions of photons that are hitting your retina then going through your primary visual areas and then being processed further so on and so forth all the way till you get to the higher cognitive areas let's say oh this tiger is chasing me or something like that and it all has to be happened like that right so our brains therefore are actually geared to process data quickly efficiently and hopefully accurately right that kind of dynamic continuous data I think is what's going to be the future of where big data is going to be in a sense that's what excites me because what it tells me is that the object of study of my science which is the mind and the brain seems to dovetail really well with the evolution of technology as we see it right now so that big data technology will hopefully tell us and help us understand how our brains and minds work and the other way around that more and more understanding of our minds and brains work will also help us design better data analysis techniques so this I think of as the AI challenge of the 21st century I think way back when in the small data days people wanted to build AI computers or robots that would think like human beings famous Turing test where the idea is that if you can design a computer that is indistinguishable from a person it's as intelligent as we are so imagine what the big data version of the Turing test is going to be which is that if you can take a computer which can process visual information in a way that is indistinguishable from our visual system you've cracked what it is to understand how human vision works right and vice versa if you understand more of how human vision works you might be able to build better image processing or other kinds of visualization technology so this combination of the true is what I think is going to is already is and is even more in the future is going to be where the intersection of science and technologies are so to I'm not going to start summarizing today's session so the idea in this class as I said is to dress up the scientific method but in a way that is compatible with large data sets like you how to think with data so most scientists actually don't think about the scientific method they just do the experiments but what I'm saying is that with big data becoming more and more common you have to think about the method as much as the domain of study so what do I mean by that if you're a biologist and you want to study the microbiome it's not enough to just know what kind of species of bacteria are there one by one because you will never be able to understand a hundred you know billion bacteria in your gut by studying them one by one so something called metagenomics where the idea is you sample a little bit of bacteria from somebody's gut or some other like or ponds come from from some water and through very rapid testing you simultaneously identify what the bacterial species are in that sample so instead of doing you know genome sequencing one by one by one you do it simultaneously for the entire sample that's actually where a lot of this is going to go and to do that well you need to understand data analytics as much as how microorganisms work so that kind of heavily analytic data processing will I think be very very crucial in pretty much any kind of inquiry so and it will be as important I hope and I think for people who are studying you know conflict or caste or you know Sanskrit texts or any of those things okay so to summarize we'll have to build something like an additional sense order right so think of data analysis machine as giving you an additional sense order it's like your sixth sense okay we're not anywhere close to that we'll at least help you suppose that that was going to be your grand challenge well this will give you the you know class one version of that you have a very very long way to go so this course is therefore about developing the cognitive tools thinking tools to help us understand data we will develop these ideas in a structured way so from next week on so next week will be Anand and then every week we will have a specific topic that we're going to engage with the syllabus will be posted online it's already there mostly in the page of the course and with the projects and the structure of course where hopefully you'll get some interesting but remember again people don't think of data we actually are not data driven thinkers we are actually story driven thinkers or metaphor driven thinkers so one of the challenges of visualization communication analysis is how do you make data more presentable to people in such a way that they understand it and that's really going to be our challenge


this argument to convolution. So suppose we take the convolution of two sequences, X1n without the loss of generality has nonzero samples only from only between 0 and n1 minus 1 and X2n has nonzero samples only between 0 and n2 minus 1. In other words, the so-called length of sequence X1n is capital N1 and the so-called length of sequence X2n is capital N2. The question is what happens when you convolve these? What length would it have? Let us see. It is very simple. When we convolve, you see you would have 0 to n1 minus 1 for X1 and X2 is moved around on this. So X2 has n2 samples which move and the movement begins with this sample here. You see the output starts only when this sample reaches here and the output ends when this sample reaches here. In other words, the output would be nonzero only in the range 0 to n2 plus n1 minus 2. You see for this to reach here and to move all the way when this reaches here, you have essentially gone over n1 minus 1 plus n2 minus 1 samples. That is n1 plus n2 minus 2. The movement has been between 0 and n1 plus n2 minus 2. In general, I leave this to you as an exercise. Suppose X1 occupies the range. Now when I say occupies, what I mean is that the nonzero samples are only in this range. Occupies the range capital N1 to capital N2. That is the nonzero samples are only in this range. Similarly, X2n occupies n going from n3 to n4. It should be noted that when we say occupies or when we are saying the nonzero samples are between these numbers, we are not saying that all those samples need to be nonzero. We are saying that if there are any nonzero samples, they are confined to between n1 and n2 for X1 and between n3 and n4 for X2. Some of the samples in between could also be 0. When occupies n1 to n2, X2 occupies n3 to n4, then X1 convolved with X2. That is X1 convolved with X2 occupies n1 plus n3 to n2 plus n4. This is I leave it to you as an exercise to prove. It is simple. Just write down the convolution expression and you would see it fairly in a straightforward way or you could do it graphically. In fact, I would encourage you to use both approaches, the algebraic approach and the graphical approach as well and reaffirm the same result. Therefore, in a convolution of two sequences with n1 and n2 samples respectively, we have n1 plus n2 minus 1 samples. The convolution occupies length X1 plus length X2 minus 1 samples. And this gives us a hint as to how we should deal with convolution in the frequency domain if you want to sample. You see, it is quite adequate if X1n has a length of n1. It is adequate to sample the frequency domain for X1 with only n1 samples. And similarly, if we wish to look at the frequency domain or if we wish to avoid time domain eliciting when only X2 is involved, it is sufficient to take capital N2 samples. But if we convolve X1 and X2 and if we wish to multiply, therefore the discrete time Fourier transform as you would, convolution can be obtained by multiplying the discrete time Fourier transforms and then taking an inverse discrete time Fourier transforms. So, if you wish to use that route, then it is not adequate to sample with only capital N1 samples or capital N2 samples. You need to sample with capital N1 plus capital N2 minus 1 samples on all of them. That is very, very important. If we wish to, let us make a note of that. If we wish to carry out convolution by sampling the DTFT, we must take little n1 or other lengths X1 plus length X2 minus 1 samples in the DTFT. And this is true for all the 3, the DTFT of X1, the DTFT of X2 and the DTFT of X1 convolved with X2. You need to work with this many samples. To take an example, suppose we had X1 of length 3 and similarly for X2, also length 3. Then X1 convolved with X2 would be of length 3 plus 3 minus 1, which is 5. We need to sample X1 and 2 omega with a spacing of 2 pi by 5, not 2 pi by 3. You would need to take 5 samples of X1 omega and X2 omega and multiply the 5 samples, sample by sample and then do whatever you wish to. But if you really take only 3 samples, then what would happen? Suppose we violate this and take only 3 samples of X1 and X2. What would happen? That is, you try and use only 3 samples. You see, how would you convolve? You would convolve 2 sequences by multiplying the discrete time Fourier transforms. See, convolution X1 convolved with X2 means multiplication of the DTFTs. So if you have only 3 samples of this and if you wish to get back X1 convolved with X2, what you are actually going to get, let us call X1 convolved with X2 as Y. What you are going to get is not Y shifted by every multiple of 5 samples but Y shifted by every multiple of 3 samples. So we are going to get summation R going from minus to plus infinity Y in plus 3 R and let us write down this convolution to understand it better. You see, you have X1 0, X1 1, X1 2, the 3 samples of X1 and you would have 3 samples of X2 which move around this, the platform and the train. So the output would be X1 0, X2 0 here at 1. So you see Y of 0 is X1 0, X2 0. Y of 1, you can keep writing it. It is X1 of 1, X2 of 0 plus X1 of 0, X2 of 1. Let us write down all the 5 samples. Y2 is X1 of 2, X2 of 0 plus X1 of 1, X2 of 1 plus X1 0, X2 2 and you can continue. Y3 is X1 of 3 and X1 of 3 is 0 so that does not exist at all. So you see, you have only 2 samples now. You can see what is happening here. When you have 3 then this has reached a 0 sample. So X2 of 1 overlaps with X1 of 1 and X2 of 2 with X1 of 1. So X1 2, X2 1 plus X1 1, X2 2 and finally Y of 4 is simply X1 2 times X2 2. Is that correct? Now carry out this time domain, Elias. What is going to happen?  At 0 you would have usually had just Y0 and then starting from 0, Y0, Y1, Y2, Y3 and Y4 but you are going to shift this by every multiple of 3 and add them. So Y0 is also going to come here. Y0, Y1 and so on. And this is also going to go backwards. So you see it is going to go backwards by 3. So 4 would also come to 1 and as expected this is going to be periodic with period 3. You see the sequence obtained by taking the original sequence, shifting it by every multiple of 3 and adding these is going to be periodic with period 3. That is very easy to show. Let us put n plus 3 here and it gives you back the same sequence. So it is expected that this is going to be periodic with period 3. So we need to take only the principal period of 3 here. Let us call this Y till day 0, Y till day 1 and Y till day 2. And now we have an expression for Y till day 0, Y till day 1 and Y till day 2. Let us write down that expression. Y till day 0 is essentially Y0 plus Y3. That becomes, if you look at the, we derive the expressions for Y0 and Y3. We have an expression for Y0 here and we have an expression for Y3 there. So that is X1 0, X2 0 plus X1 2, X2 2, X2 2, X2 2, X2 2, X2 2, X2 2, X2 2. Y till day 1 is similarly Y1 plus Y4 and that is very easily seen to be X1 0. Let us fix, you know, let us do one thing. Let us write in the same order for X1. So X1 0, X2 1 plus X1 2, X2 2, X1 1 and of course you have, of course we very easily see that Y till day 2 is the same as Y2. Y till day 2 is equal to Y2. And that is of course we have divided before. So we write in the same order as I said.  So X1 1, X2 1. So you have taken care to write the same order. X1 0, X1 2, X1 1, X1 0, X1 2, X1 1. Here also X1 0, X1 2, X1 1. And of course you notice that it is different samples that are associated with these every time. To understand what is happening better, let us write down the samples not on a straight line but on the surface of a circle. So let us fix the outer circle with the samples of X1. So we have X1 0, go and count the clockwise order. X1 1 and X1 2 and we take an inner circle and put on it the samples of X2. So let us put the samples the way they associate for Y till day 0. So X1 0 associates with, so you know we have taken X1 0, X1 2, X1 1. That is essentially the C-Samp is going in this order. X1 0, X1 2, X1 1. So X1 0 associates with X2 0. So let us put X2 0 here. X1 2 associates with X2 of 1. So let us put X2 of 1 here. X1 1 associates with X2 of 2. So let us put X2 of 2 here. Now when we come to 1 till day 1 these are fixed. So this is the, we call, let us call it the rotor. The inner one we call the rotor and the outer we call the stator. Taking a queue from the terminology for machines. So for 1 till day 1, X1 0 now gets associated with X2 1. There is a movement by one step. You see X1 0 gets associated with X2 of 1. So X1 1 is going to get associated with X2 of 0 as is expected here. And of course X1 2, so the other, you see, so you have, you see X1 0, you move this here, isn't it? For 1 till day 1, X1 0 is got associated with X2 of 1. So this is rotated one step here. So X2 0 gets associated with X1 1, X2 2 gets associated with X1 2. When we come to 1 till day 2, X1 0 is associated with X2 of 2. So this is rotated 2 steps. This is rotated here. This is now rotated. So it is rotated by 2 steps and the associations are after rotation by 2 steps. So it is as if we were convolving not on a straight line. It is as if we had a train and passengers in and out of the train not on a straight platform but on a circular platform. And therefore what we have got here is what we call circular convolution. Call the circular convolution because it is as if you were doing convolution with the sequences put on the surface of a circle not on a straight line. And of course as expected circular convolution is bound to be periodic. I leave it here as an exercise to work out what happens in case you took 4 samples and not 5. So m equal to 4, 5, 6 samples worked for all the 3 cases. Of course of course you took 4, 5 and 6 samples respectively of the sequence X1 and X2 and then worked in the frequency domain. We are assuming that we have a way to go back after sampling but we need to complete that one little step. How do we go back? After we have sampled the frequency axis how do we go back to the original time domain expression? Now we can of course do that only if we have no time domain aliasing. Or even if you have time domain aliasing whatever we do is going to give us the aliased version of the sequence. Either way we must have a way of going back after sampling. Now to do that of course we can again you know exploit or invoke the idea of vectors. So what we are saying is we must now put some terminology. The m samples omega, omega evaluated at 2 pi by n times k, k going from 0 to n minus 1 of the DTFT. These m samples are called the discrete Fourier transform for DFT of the sequence Xn. Now of course we should be more precise. We should call it the n point discrete Fourier transform because we could have taken less for more samples too. And here we are assuming the sequence X of n has utmost n samples. So if it does not of course you could possibly take a discrete Fourier transform but then there would be time domain aliasing or you may be taking more samples than required either way. If you take more samples than required there is no problem. So even if a sequence has only n non-zero samples you could be very well taking more than n samples in the frequency domain. And that you deal as you can see you need to do it when you are trying to convolve two sequences. When you convolve two sequences you need to take more than the number of samples in either of the sequences. It is not unusual to do that. If you have a discrete Fourier transform you need to have an inverse transform. So the inverse discrete Fourier transform of, now we shall use X square bracket k to denote X omega sample that omega equal to 2 power by n times k. The inverse discrete Fourier transform X of k is essentially, you see now here it is very interesting. You have capital X of k to n sample. So it is as if you had capital N dimensions along which you are trying to represent the original sequence. So what have you really done? You have gone from potentially n samples to n samples in a different domain. And each of these samples in the frequency domain corresponds to a vector, a vector created by a rotating complex number, rotating with these frequencies here, 2 by n times k. So in the next lecture we shall see how we would reconstruct the original sequence or at least try or potentially reconstruct the original sequence by using this idea of vectors. Not only that we also see how to do this. You see all this is useful if it is going to make our computation efficient. And therefore after having established that we can reconstruct we also need to see if we are going to get a computational advantage by discretization which we shall also see in the next lecture. Thank you.


This is stock number 11220. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck and RAM headquarters. Today we are checking out this super clean 2015 RAM 2500 mega cab short box. This truck has a 6.7 liter Cummins diesel. This truck has been fully safety and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. This truck has been gone through mechanically 100% and is 100% ready to go. Western brown over white gold is the color scheme. We shoot all of our videos in 1080p 60 frames per second. So if you have HD capabilities on your computer tablet or smartphone device turn them on right now because it is definitely your best way to check out the quality and condition of the vehicle before seeing it in person. And if you'd like to check out all the photos on this vehicle in the upper right hand part of the screen is a link to our website. Click that and check us out there. This one has the 18 inch painted and polished aluminum Laramie Longhorn rims. Michelin Defender LTX tires. These are LT27570 R18s and they have probably about 70 to 80% of the tread left on them. Take a look at how clean that frame is. Very little to no corrosion on it. Very nice condition. The front fender is in excellent shape. I didn't see any dents or dings on there. It does come with the projector lamp headlamps, the LED running lights, front painted bumper, factory fog lights. No major dents or dings on that. And the hood is in excellent condition. No dents or dings on there. Looks really good. You do get the chrome trimmed grille and the headlight lenses are nice and clear as well. Passenger side front fender is in excellent shape too. No dents or dings on that and the passenger side rim is in pretty nice condition as well. No scuffs or scrapes, a couple little water marks. They wipe right off. As you go down this side of the vehicle, take note of how clean the body is, how reflective and mirror like that paint is. We take these HD videos so if you are far away or even if you're close by just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here there's absolutely no surprises. You get the cargo cam, roof is in nice shape, and that's why we offer the Summit Auto Fastpass option in the upper right hand part of your screen. A link right to our website where you can apply for financing. Get pre-approved, see a payment quote, even appraise the trade off from the comfort of your own home. So when you get here all you gotta do is drive the truck, love it, and you'll be all set to go. Back rim is in excellent shape as well, and the back tires are just as new as those front tires. See the rest of the frame and under body is in fantastic condition. It's like that all the way underneath. It does have all the remaining factory exhaust and the lower rockers and step bars are in really nice shape. As we come around to the back of the vehicle, rear bumpers in great condition too. No major dents or dings on there. It does have the back of parking sensors, full towing package which includes the receiver hitch, four pin, and seven pin wiring. The tailgate is in really nice shape as well, and it does come with a tonneau pro fold up soft tonneau color. You do get the spray and bed liner, fifth wheel, and goose neck prep package back here. That's in pretty nice shape. That bed liner is in great condition, and you do get a seven pin wiring hookup back here as well. Tailgate shuts nice and solidly. It's a pretty cool looking badge. And as you go down the driver's side just as clean as that passenger's side, no dents or dings on the box, and for full disclosure this back rim is in pretty nice condition as well. Down the rest of this side looks really good. You get the enter and go system. You get the heated fold out tow mirrors. They fold out like so, and I believe they power fold in as well. I always like showing both sides so that you know both sides are working good. Inside the Laramie Longhorn package gives you the saddle brown leather interior. You get the black piping on the sides, no rips or tears on the seats, memory driver's seat, factory all-weather floor mats, auto headlamps, power pedals, power windows, power locks, power mirrors. You get the real leather handle, the real wood grain trim, the leather bolsters on there. Very nice interior on these vehicles. We'll turn the ignition on here so you can see the miles and everything. You do get the Longhorn animation right there, and you can see that this truck has 56,854 miles. Instrument cluster is very nice and clean, and that's a special instrument cluster for the Laramie Longhorn Edition. You also get the compass outside temp display, and you can change those corners to read whatever you'd like. It has the heated and wood grain trim steering wheel, cruise controls on the right, Bluetooth and information center controls on the left, and you do get audio controls on the back of that steering wheel. That's a really nice shape. Six-speed automatic transmission has the 8.4 3C radio, AMFM and Sirius XM radio capabilities. Also has the factory navigation system on it as well. You can do your climate controls here, including your heated and cooled seat buttons, heated steering wheel, and then you do get a cargo cam on this vehicle. I'll show you how that works. So if you didn't have the tonneau cover on there, you could see what's going on in the bed. It also has a backup camera, and that shows up in your rearview mirror. You get the 911 and assist buttons in that rearview mirror as well. Up here you'd get your home link buttons for your garage door security systems and lighting systems. Headliner is in really nice shape. Passenger side seat is in excellent condition as well. I don't think this truck's ever been smoked in. It smells very clean inside. You get all other formats on that side. That special trim on the upper glove box there. You get the brown stitching throughout the wood grain trim up top, and this does have the Alpine premium sound system. We'll take a quick look at the back seats and then we'll check out under the hood. Back seats are just as clean as the front seats. No rips or tears back here. These seats are heated on the outboard side, meaning the driver and passenger side seats are heated. Power sliding rear window with the built-in rear defrost. You get that black piping back here as well. That all-weather floor mat. There are your heated seat buttons, your saddle bags on the back, and these seats fold down two different ways.  It's a nice flat place to lay. If you got pets or something and then you can put just the backrest down, and that'll give you a higher load floor to easy access whatever you may want. You also get storage back here. That's pretty nice. You get latched tether anchors, and just very, very clean inside and out on this truck. The door trim is in nice shape and you do get child safety locks on the back doors. We'll take a quick look under the hood and start it up for you. You can hear it start right up. I would personally like to thank you for checking out the video today and hopefully from this HD video. You will be able to tell just how clean this truck is all the way around inside and out. Under the hood we have the 6.7 liter Cummins diesel engine bay. It's very clean, runs very smooth. This truck has been fully safetyed and inspected by our service shop as a fresh oil and filter change. All the fluids have been checked and topped off. And this truck is 100% ready to go. See more pictures of this truck or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers, you name it, we got it. Go to our website www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at summitauto.com. And if you'd like to check out more HD videos, you can go to youtube.com. Remember to like, subscribe, and share on this video and all the videos that you see there. In fact, in a second you will see a link to subscribe to our YouTube channel on your left. A link to more Ram 2500 truck videos like this one on your right. If you have not been to our website, on the bottom link to this vehicle on our website, click those, check us out. And we're super excited to help you with this extremely clean 2015 Ram 2500 Mega Cab Short Box Laramie Longhorn Edition and Western Brown. Thanks again for checking out the video.


Colleagues, I want to take a few minutes of your time to talk about OCS, one operate system, and specifically about the time registration component of it. I know that many of you are already using OCS, and depending on where you work, you will continue to do so either in budget and finance or in procurement or in areas such as resource registration and so on. But time registration is going to involve all of us. So today I wanted to tell you a little bit about time registration and to answer at least one question, why do we need it? Well, we need it for two purposes. The first is for compliance, so we can more adequately bill our donors for the time you spend executing projects. Secondly, we need time registration to attempt the budget time outside of your research so that we can plan for this and adequately recognize you for the time you spend providing services to such things as representing the institution and meetings, serving on committees and so on. It should not take very long. Actually, it should take no more than three minutes of your time. But you must do it each week. You should do it for yourself and you should approve that of your staff. Without this, you can severely disrupt the processing of the institution's reports and that would have various consequences internally and externally. So time recording, while it's not an arduous task, is an essential task that we all must undertake. Let me give you a quick demo of what it entails. Assuming that you have logged on effectively, you will of course come to this screen which allows you to have a planned versus an actual time analysis. Once budgets are approved, you will enter your time sheets weekly. These time sheets include normal time, which are times that you bill to a budget court, and no charge time, time that you spend outside of the work you do on a particular project. So colleagues, this, as you see, it's not very difficult, nor does it take very long. But as I said earlier, it is critical. Whilst we do this individually, it has a great impact collectively on the institution as a whole. So I urge you to take this seriously. Attend the training that is being offered so that we can do this effectively. This can be our individual contribution to an overall institution of goal. Thank you for your support.


All right, sorry about that guys. I'm a doble over here. I forgot to do the giveaway for The $200 in break credit that we're giving away because of the buy two or more teams So two teams give you one entry four teams give you two entries six teams give you Three entries etc etc more spots and multiples of two give you more entries So again, we rolled the dice in the previous videos the same video we rolled in 11 We're gonna keep the 11 like we usually would have done and randomized customer names 11 times So Adam just the first one to grab two spots straight up and it only applies to the customer that bottom straight up to So Adam got one spot since he got two here Barack got to that equals one spot Jay got to that equals one spot X-Line got one two three four that equals two entries Yes, Logan. I have to do one more break and that's it Canton you just had a one Larry you have two so that equals one entry Matthew one two three four that gives you two entries Mitch you had one Ryan you had two and Tony had one. All right guys. Good luck. So again, we're gonna use the same dice roll as the other one we did For the unnumbered hits like you would have done. I just forgot in a separate video 11 times customer number one gets $200 and break credit one two three four five six seven eight nine ten And it wasn't 11 or 11 boom Boom 11 times one of Barack. There you go $200 and break where to come in your way check out in your email or a full number you provided with us So you use that on future breaks with us appreciate it guys Jaspies case breaks calm


As many of you know by now, Garrett Watts is officially back, maybe, and he's taken a few breaks from YouTube, and each time he says he's gonna come back for real. In this video, I wanna discuss depression, how it affects all of us, and maybe what we could do about it. 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, what I like to do is take a look at what's going on in the YouTube community and try to see what lessons we can pull from them to improve our own mental and emotional well-being. So if you're into that stuff, make sure you subscribe and ring that notification bell. And if you're not yet, make sure you're following me over on Instagram and Twitter at the Rewired Soul because my brand new book, Rewire Your Anxiety, is coming out very soon, and I'm giving away a bunch of free copies. I'm getting input from all of you on what you want me to cover in the book. And it's coming along amazingly, and there will be an audio version as well, so make sure you're following me on Instagram and Twitter at the Rewired Soul. All right, cool. Let's talk about Gareth Watts. So full disclaimer, I don't know Gareth Watts. That may come as a surprise to some of you. I do not know Gareth Watts, all right? But a lot of us have been watching him for a while, and something I try to do is like, we watch YouTubers because we can relate to them, all right? And I know I can relate to Gareth Watts. So another quick disclaimer. I'm not a therapist. I'm not a psychologist. I am somebody who has been diagnosed with depression, a generalized anxiety disorder, and I've had to find a lot of tools to use just so I can function in life. So I like to look at other YouTubers, kind of get context, see if I can relate, and share some of my experience, which will hopefully help some of you beautiful people out there. All right, so the first video I covered on Gareth Watts was when he cleaned his house and he had a problem with hoarding and things like that, and like Shane Dawson came over and Ryland Adams came over and Morgan and Andrew came over and everything like that. And like, it was amazing. It was phenomenal. And I loved making that video, just discussing all the things that we can learn from it. Like a lot of you really like that video. Like I love making it because there were so many just things that we could pull from it. But something I talked about in that video was that Gareth talked about how he wasn't really recording because of his house, right? Because it was a mess. And there was other things. There were friends because of his house. And something I said is like, yo, let's keep Gareth accountable. Like let's hope he makes a video like at least once or twice a month, right? And Gareth actually replied to that video like, check it out. Look, he replied to my video, right? And yeah, he made another video a while later about getting his ear pierced and then Gareth gone, right? But Gareth was still kind of around, you know, he pops up in Shane Dawson, like pictures and Instagram and he shows up in Ryland's videos that he's posting and everything like that. But anyways, yeah, he just came back with his brand new video about waking up at 5 a.m. every day for a week, all right? And I made a video covering that and seeing what we can learn from it. But something I just wanted to discuss was like that feeling that I can definitely relate to where Gareth talks about, he's not going to give the same old spiel about how this time he's back and he's going to make more videos and be more consistent, everything like that. He talks about how he's been going through a rough time. So I don't know what that means for Gareth. It could be a slew of things. I don't want to assume what's going on in his life, but I know what that's like for me on a few different levels. One of them is that I struggle with depression and sometimes it'll just hit seemingly when everything is all right. And I don't think some people get it, right? As one reason why I talk about it and try to create awareness and hopefully you like you share these videos to create awareness because like it's easy to look at Gareth Watts and be like, okay, this dude has everything, right? Like, why, why would he be depressed? Why would he not be doing that thing? Like, I know as a creator, like I look at it and I'm like, man, if I had the skill, the talent, maybe even the good looks of Gareth Watts, like, yo, I would be creating videos all the time, right, to get views and all these other things, right? But the reality is when depression hits us, it, it kills our motivation, it kills our creativity, all these other things, right? And then what happens to a lot of us is, is when we start down that road of not doing things, not creating, it just gets worse, right? Because next thing you know, it's been three days, it's been four days, it's been a week, it's been two weeks, it's been a month, right? And then we're beating ourselves up for what we haven't created, and then it makes us even more depressed, right? So this is a thought trap. And this is one of the reasons why I try to preach like meditation and mindfulness, because I now know when this stuff's happening, right? Like all mindfulness is, is paying attention to what's happening right now, all right?  So like, I have developed a skill that I'm much better at now, where I can catch these, these bad thoughts that are coming in, you know, that are telling me to not do anything, to not create anything, to not be motivated and you already screwed up and everybody probably hates you and all these other negative voices, right? Like I can catch them, right? And then I could start doing something about it. And this is one of the reasons why I recommend therapy to everybody. Like, I absolutely love my therapist. Like, when I was talking to her, not this last session, but the one before it, but anyways, like I was talking to her and she knows that, you know, I like to laugh at myself. So that, like, that's the only reason why she laughed too. But anyways, like I was just talking to her and I was telling her about the craziness going on in my head and she starts laughing and I'm like, what's so funny, right? I'm sensitive. And she's like, do you hear what you're saying? I'm like, oh my God, I do hear what I'm saying, right?  These thoughts that are not being useful to us. You know what I mean? And frankly, like, this is why I always recommend like, hang out with people who tell you what you need to hear, not what you want to hear. Like, I know sometimes we just want to be coddled and have people feel bad for us and everything like that, but something that saved my life multiple times was having my friends call me, text me, walk into my house, walk into my room and pick me up and be like, yo, we're getting out of the house today. We're going to do something today. We're going to make something today. We're going to get something done today. We're going to hang out today. All right. Like you cannot just sit here, right? And that's why it's awesome that Garrett Watts has, you know, amazing friends like Shane Dawson and Ryland Adams and Morgan and everything like that, right? So that is very, very, very good, right? But when we see what Garrett Watts is like, like when I'm watching him, right? Like Garrett, you know, this is his job. This is his primary thing. And I get worried for him. Like I get worried seeing his lack of creation because that is his livelihood.  Like he talked about like going to Japan for two weeks. I'm like, yo, how's he affording to go to Japan? He's not making videos. Like I get worried about that kind of stuff. But like that's the thing. And for the rest of us who are not, you know, YouTubers with millions of subscribers, like we have to ask ourselves that. Like how does our mental health affect our ability to work? Our ability to do anything? So using Garrett Watts's context, especially his last video where he had a goal every single day to just do something productive. And that's mainly why I wanted to make this video. Like if Garrett Watts, if you ever see this video, like this is what I would encourage you to do. But here's what I encourage all of you to do out there who are struggling with depression. Like as somebody who is struggling with depression, I can honestly say the worst thing I've ever heard anybody tell me as a part of advice is like just do something, right? Or snap out of it. But at a certain point, we have to, all right. And this is one of the reasons why antidepressants are so beneficial for some people.  But there comes a certain point where we have to do stuff even though we don't want to. Like something I try to teach people is maintaining good mental health isn't necessarily about what you want to do. It's about what you have to do, right. Like I made a blog post not that long ago about bathing every day.  And the message of that blog post was just like do something each day to take care of yourself, right. Some act of self love.  So my one thing a day that I am guaranteed going to do to treat myself like a human being is bathe. That's just me, right. Maybe for you it's like putting on some moisturizer or like doing your hair or whatever it is, right. But we do something. But once we see that we can do something, not everything because doing everything could be extremely overwhelming. But if we could train ourselves to do something, at least one thing every day, the next week we can start doing two things every day, right. The next week we can start doing three things every day. This is how we build resilience. Resilience is one of the major, major components to improving our mental health. Because we start to recognize, our brain starts to realize, oh my God, I can accomplish something, right. And celebrate your successes, right. Like maybe Garrett's celebration was like going on his vacation to Japan, right. But if you like got out of bed that day, like treat yourself, right. Have a scoop of ice cream, pat yourself on the back, give yourself a gold star, whatever it is. Like a lot of other people out there who do not struggle with mental illness they will never understand. But for people like you and me, sometimes those really small things, that is a huge deal for us, right. And we need to congratulate ourselves for it. Because what we're doing is we're giving ourselves positive reinforcement, so that way the next time something comes up, our brain automatically thinks, oh wait, when I get something done it makes me feel good, right. And I don't have time to dive into the whole neuroscience of that, right. But we want to train our brain to see that being productive is a good thing. So for somebody like Garrett Watts who's trying to get back in the groove, like here's what I would say. And this is just unsolicited advice from Chris Boutet to Mr. Garrett Watts and anybody else that might benefit from this. Especially if you're creative, if you're somebody who likes to write or draw or paint or dance or whatever it is, right. Just do something. Just create something. Like I will say it until the day I die and I know I'm not alone in this. Like how awesome would it be if Garrett Watts just did a daily vlog. Not too edited, nothing crazy, but like Garrett has just such an amazing personality.  I love his editing style. But anyways, like him just doing that, it would start to build that momentum of being productive. And then he could dial it back to once a week, once every other week and having kind of a bigger production. You know what I mean? But again, the goal isn't to do everything. The goal is to do something. All right, especially if you deal with anxiety and depression, doing everything can be overwhelming. So figure out something that you could do every single day to be productive. Like me, I'll share with you what I've been doing. I've been trying to do multiple things a day for my mental health, such as go to the gym, go to a 12 step meeting, talk to somebody in my support group, right?  You know what I mean? And trust me, there's many days where I just don't want to do it. But again, it's about what I have to do, not what I want to do, all right? But anyways, let me know your thoughts on this subject down below. What have you done? What do you do?  Let's share our experience down below in the comments, all right? But anyways, don't forget to follow me over on Instagram and Twitter at the Rewired Soul because Rewire Your Anxiety is coming out very soon.  So follow me so you don't miss anything, all right? But if you liked this video, please give it a thumbs up. If you're new, 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're all amazing. And if you would like to help support what I'm doing here, get some other perks and benefits, like free copies of all my books, you can click the top right there, all right? Thanks again so so much for watching. I'll see you next time.