Using Stack Overflow to Learn From the Best | Learning Intelligence 40

Good morning everybody, or evening, wherever you're watching from. It's been an epic morning in the Burke family household. We went out for breakfast for my brother's birthday. Happy birthday, Billsie. Thanks, yo. What are you studying right now here, man? Uh, Python programming for everybody. Yes, now he's studying that because we, a couple weeks ago, or about a week ago now, we started an applied data science course with Python. But it does require a little bit of Python knowledge. So he's, he's working on getting that foundation knowledge, and I'll link below the course that he's doing. Otherwise, my phone's away. We've got it set up here. We're getting some data science study going on in the room, getting that combined knowledge together. I'm on to week 3 of the Applied Data Science course with Python, which is some advanced Pandas stuff, which, as I've said in a previous video, is really akin to the stuff that we're working on at Max Kelsen. If you want something that's a course that's sort of close to the type of stuff you'll be working on, at least in my experience. It may be different elsewhere. This is a really good one so far. Now, as a warm-up, I'm getting started on my Anki flashcards, but I'll show you. I've only got 9 to do today because yesterday I did, did all that I had to do. So, as you get better at them, it won't ask you a certain card for a certain amount of time. So, these 9 are ones that I've struggled with in the past. You'll see here, Python, 9 cards. Let's give an example. What is the union method do? Returns all of the items in 2 sets from all of the sets. So, I like to use Anki as like a little bit of a warm-up into getting into study. As I said, only 9 flashcards today. As I add more, there'll be more over time or even less over time if I keep getting better and better at them. Harry Potter and the Chicks is here. This guy's wearing a wizard hat. What's he saying? Fuck. I just finished all the lectures in Week 3. Now there's a little section, I'll show you. It's a video they want us to watch. It's called Goddard's Law. There we go. Goddard's Law. You may have heard of it. There's a Data Skeptic episode. I believe they do a podcast on data science and whatnot. So I'm going to check out this post here. Looks like a pretty cool post and there's a video attached. So I'll link this below so you can check it out. Once I find out more about it, we'll discuss Goddard's Law. Good heart? Good heart. Good art. When a measure becomes a target, it ceases to be a good measure. Bonuses and salaries tied to it. That's great. for success, then good luck. This is a really good video. Really, really interesting. I'm just preparing some meals for the week. That's what I usually do if I have like a YouTube video or something I want to listen to, like a podcast or something like that. I'll cook my food and have it ready so if I want a meal during the week or something like that, if I'm at work, instead of buying food, I can eat what I want. Today, we've got something simple, just some chicken and broccolini. I'm going to put it in here, put them in the fridge, and we're all done. But essentially the, the crux of Goodhart's Law is when a, when a metric gets too over-measured, it becomes, becomes a bad metric. And one example I can give you that is in my day-to-day life is there's a pizza shop near me or a pizza chain called Domino's, and they always have a ridiculous amount of sales going on and coupons. And so the coupons are always like 20% off or half-price pizzas or something like that. And so, no one ever pays full price for Domino's. You, you never buy anything from Domino's full price because you can always get a coupon. So that, their metric of making sure revenue is going up by selling pizzas at a cheaper price means that people are used to buying the pizzas at cheaper prices. If you want to check out that video from, from Dataskeptic, That's it. They've got some really cool stuff actually. I'm surprised I haven't found it sooner. I'll leave it in the description for you. I'm going to finish preparing this food and then get back into Assignment 3. All right, I got a little distracted on Stack Overflow. It's really cool, but you can search on Stack Overflow for the, for the most upvoted questions in a certain language. I'm looking at— I'll show you. I'm looking at the one for pandas right now because that's That's one of the pieces of advice in this Assignment 3 is there's a few things in the assignment that we haven't used yet on Stack Overflow. Well, we'll need to ask questions on Stack Overflow to figure them out. So if we look up here, I've been going through these questions tagged pandas and look at all this goodness. Renaming columns in pandas, selecting rows from a DataFrame. All the, all the major things you'd want to do in pandas are here and they've got plenty of good upvotes. But what's the value of looking at all this? Well, you can go into them and then you can scroll down and you can see how some Pandas experts would implement it. That one there, there we go. It's renaming columns. And that's a pretty clean way of doing it. And you can repeat that for many different functions, of course, as you saw before, the page is full of questions. And I'm sure this would translate into other programming language. So if you're learning, whatever you're learning, Python, Pandas, NumPy, Is the camera gonna sit there? There we go, that'll do. Go to Stack Overflow, search top questions tagged in this, or just Google search top Stack Overflow questions for insert whatever you want, and then go through, see how some of the experts do it. I mean, if you need, who knows, it might directly relate to what you're working on. If it doesn't, hey, at least you'll get some sort of insight into how a really good answer, and a really good question for that fact, is structured. Now I'm definitely going to have to use Stack Overflow for some of the questions in Assignment 3. I've been going through it and oh boy, we're in for a treat. And we're starting now. Okay Google, stop the timer. All right, I think it's time for me to work out. Is it still sunny outside? Yeah, I like studying without glare, so keep the blind down. Let's go work out. All you kids want to work out? Yeah, yeah. What are we doing? Sit. Oh, there they are, a thing of beauty. And then what are we doing? Mat ride. Bark bark! What's going on, bandits? Nice hat. Look at that horizon, baby. Nice skateboarding, dude. Do something cool. Oh yeah. I love living around here. Coming here in the afternoon, water's so calm, it's like my favorite place in the world. And I know I definitely need a microphone because this wind is horrendous. Zeno, what's my catchphrase, bro? Keep learning. Keep learning. See you later, team. See you next week. Hey, look, here's some friends. Hey, what's going on, team?