The Applied Data Science Course is epic! | Learning Intelligence 42

We're about to flip and send it, dude. Ready? Good morning. You know, every day I want to, I want to get something done. Today's Sunday, but I still want to, I still want to do some things, right? I've already spent the morning relaxing, went for a walk with the boys, got some coffee, so I'm feeling good. So I write down a list. I want to do some things today, and the best way I stay organized is with this beautiful yellow book, right? I write down whatever I want on here. Random thoughts, diary notes, but usually I start off every day, top of the date, well, no, date at the top of the page, and then about 6 things, not too many things that I want to get done during the day. If you can see, write and workout are 2 non-negotiables for me every day. I write every day. Follow me on Quora and Medium and my blog, whatnot, if you already aren't. Workout every day, and that can be just going for a walk or doing some weights or something like that. But today I want to, I'm going to do DS course week 4. As you saw in the last video, finished assignment 3. We're up to week 4 this time. Beautiful. Really excited for that. Edit video. So edit the last video. You probably already see it's uploaded already. Video clips, which reminds me to actually record these clips today because sometimes I forget. Play, which is you got to have fun every day, right? And there's 7 things on here. So 6 is kind of a lie. And the wild card at the bottom here is Panda's book part 2. So I want to maybe read chapter 2, but if I don't get to that, that's all right. I'll just, I'll just put that in another day. Welcome to Learning Intelligence 41. Learning Intelligence 42. Let's get into some study. No, you know what? We're gonna change it up today. I'm gonna study in the dining room, 'cause look at that beautiful light source. Look at this. Look at this environment. You can see the outdoors. Got a little bird there for some background noise. Yeah, instead of being in the room all the time, I'm gonna sit out here. Check out the lectures in Week 4. I think there's only, it's only like 30 minutes of lectures, if that. What do we have? Introduction, Distributions, More Distributions, Hypothesis Testing in Python, End of Theory, not sure what that is, maybe an assignment or something, and then Discussion Prompt, Science Isn't Broken, p-hacking activity, and then we got a project, which, what do we have down here? Programming Assignment, there we go, and a post-course survey. Let's do it. I'm gonna get through these, this first set of lectures, and then we'll have a recap. Welcome to the third week of course. Alright, so I just finished all the lectures and now there's a little section here. I like these sections actually, which is like a case study. It's called End of Theory. So there we go, finished the lectures. Let's check out, we'll check out what we went through in a second. So End of Theory, there's an article written by Chris Alban, I believe. Chris Anderson, there we go. Chris Alban is, I think, a really cool data scientist. His website's awesome. Chris Anderson, The End of Theory, The Scientific, no, The Data Deluge, deluge? Deluge makes the scientific method obsolete. And what I was trying to do for these lectures is just watch the whole lecture through and then take screenshots where, where I thought there was something interesting and then copy the screenshots into the notes and then write notes on that. I got a cool resource I want to show you as well. So it's on distributions. Distributions: set of all possible random variables. Example: flipping a coin. If you did 10 million coin flips, what would be your distribution? He did a really good definition of standard deviation, one I hadn't really heard before. I need to get up on all these statistical terms. A measure of how different each item in our sample is from the mean. That, that was really clear to me. I can understand that. And there's a book here. It's a free book for program— Probability and Statistics for Programmers. I've checked it out briefly. It's all in Python. There's an awesome GitHub repository for it. I'll link that in the description. Check it out. All in all, this course is amazing. I'm going to read that article and then do the discussion piece. I think there is— I've got to answer some sort of question. There we go. Your thoughts, and then I gotta explain it after I read the article. It's like midday, it's time for some food. Delicious. Nice and simple meal. Whole ingredients, all cooked by me. Bacon, 4 eggs, greens, Brussels sprouts, walnuts, onions, drizzled with olive oil and salt and pepper. We keep it simple around here. Food's done, time to get back into some study. What? Hold on, let's go check out what the team want. What do you want? You know that one thing on the whiteboard you've been itching to get rid of? One thing? I got like 6 things. Yeah, this one. Did you just put that up there? Maybe. What is this? Look how big that square is. Yeah, because it's a big idea. Drift trikes times 2. Yeah, what it— what it's doing on this board, it has to get done. It has to get done. Look, I've got all these things that I'm gonna do today. This is me. DS course week for 4 lectures. Well, I've actually done that, so you can cross that off. No, what happens is with the drift trikes, it also interconnects into your one that you've been waiting to do. Yeet! Hahaha! What is this? We've been doing some research here and we found— Look, what is that? He has a second one out the back. Damo has 2. What are these? Drift trikes. Alright, so what's the plan? You going to get them? Yeah. How much are they? Well, he's asking $80 and $100. Do they work? Yeah, we offered him. I said I had $104 in my wallet. I'll come by 3. Time is almost 2, so we're gonna get there by 3. Alright, I'm going back to study. You guys let me know when you got the trikes, and then we'll trike it up. Sounds good. Never a dull moment in this place. This is a really cool activity. I just finished reading the article, The End of Theory, and the crux of the article essentially, it goes through and saying, is scientific theory, is it obsolete because we've got so much data right now? And what I mean by theory is imagining something in the world and then going out and trying to prove the theory. What the article stated, and it's interesting that it's written in 2008, so 10 years ago, right? And imagine how much data is there now, is that places like, like Google succeeded using just applied mathematics on large scales of data. It means they didn't have the, uh, didn't have the theory to begin with. Instead, they just collected the data, applied math to it, and pulled the theories out of the the data that was in the real world. And so what this activity is, is asks for the students to create a discussion around the article. Do we agree or disagree? And I wrote agree because I love data, because I love mathematics, because I love statistics and I love technology. But what they do, what the really cool thing is, is they show you examples of other students who disagree with you. A student from Canada disagrees with you. Google's going off there. I do not think we have come to the end of theory. And then you can give them feedback on their response. So, that's a really good answer. It's no accident that every good paper includes the phrase, "More study is needed." There is always more to learn. So right! This article agrees with what I said. Keep learning, right? There's almost always something else to learn, no matter how, how deep you get into something. Once new data arises, once it comes in, the world is always changing, right? We don't fully understand it and more information comes in. Where is, where is this coming from? I'm reading an article called Science is Broken. I'm not sure if you can see it, but Science Isn't Broken, sorry. And it's one of the discussions that came after the previous one I just did in week 4. And it goes through the concept of p-hacking. And there's a cool tool on the on the, in the article, I'll link it below, where you can change the results or change the inputs into a study and get a better p-value, which is whether it's statistically significant or not, based on the inputs you put in. The article talks about how a lot more papers have been retracted because they aren't statistically sound or the results aren't reproducible. And there's a lot of arguments saying how flawed science is, but the main argument is that science isn't broken, it's just really hard. If you're a researcher and you have an idea in your head, you almost have to believe that your idea is right, no matter what the idea is. Because if it's hard to research the wrong idea or hard to research something that's not true, right? If you're trying to learn something, you have to believe it's true. Otherwise, where will your motivation come from? That's what science is all about. It's about searching for the truth, accepting new data as it comes along. And sometimes you, most of the time actually, you will be wrong as you learn new things. That's what life's about actually. It's not just science. It's just going through the world. As you experience new things, your old ideas might have to die, right? It's hard and it sucks. And there's a quote here: "Once an idea becomes fixed, it's difficult to remove from conventional wisdom." So true. All right, assignment 4, hypothesis testing. Hold on, can you hear that? What do we have here? Let's go check that out. Assignment 4 can wait. What are those? Oh no way, you got your bikes! Daniel, what are you about to do? We're about to flip and send it, rock and roll this future mode of transportation. Ready? Yeah. The future of transport is very cool. We definitely ticked off Playton. Big day on tour. We get down to the beat, y'all. We get down, down to the beat, y'all. We get down to the beat, y'all. It's like we're not going anywhere.