Sean Tibor: Hello, and welcome to teaching Python. This is episode 104. It's what we're reading right now. My name is Sean Tybert and I'm a coder who teaches. Kelly Schuster Paredes: And my name's Kelly Schuster Perez and I'm a teacher who codes. Sean Tibor: Well, Kelly, it's been a couple weeks, but not a couple weeks since we recorded last. We actually have an episode on the Teacher Cast podcast that was recorded recently, and hopefully we'll be hitting the airwave soon. But for you and I, it's the first episode in a couple of weeks from the Teaching Python feed. So it's going to be fun to catch up with you today. Kelly Schuster Paredes: I know we didn't do our wins on the Teacher Cast, but it's like we were talking and I'm sure you had a good conversation afterwards about your 3D printers. Sean Tibor: True, but that is not my win. My win is so good this week. Kelly Schuster Paredes: Oh, my God. Sean Tibor: I'm excited to share. Kelly Schuster Paredes: Maybe you should go first then. Sean Tibor: No, I got to save it. I got to have that build up and let people get excited about it. What could it be? Kelly Schuster Paredes: What was my win? My win is that we had APIs on the Data science analytics boot camp. And I was like, oh, I know this. Sean Tibor: I've seen these before. Kelly Schuster Paredes: And every website that they were showing, I was like, yes, done that. Chuck Norris don't do that in the middle school. Sean Tibor: No, not middle school. Kelly Schuster Paredes: Appropriate weather API. And I tweeted to Michael, I was like, hey, this is from your ten apps. So it's quite funny that for the basic concept of APIs, the ones that my class used, they actually came and we used them in our classrooms at the one time. So pretty cool. Sean Tibor: Nice. That's always good. Like that familiar win of something that you're like, I know how to do this, and I'm going to take it a little bit further this time. Kelly Schuster Paredes: Oh, yeah, we definitely did a little bit further than what we do with the 8th graders. A little bit. Sean Tibor: Well, let me tell you a little story for my win, okay. My son has been begging me to go camping. And so camping in Florida is a very delicate affair. You have to find just the right time of year with just the right temperature. And we went to a place called Quiet Waters Park, which is here in Broward County. And they have some great campsites there with really nice tents, like way better than I'm used to. And we're driving into the campground. I've never been there before. We're driving in and there's a couple of teenagers that are blocking the road because they're learning how to ride one wheels in the middle of the campground, and one of them is helping the other. And then a third one comes over and he kind of tells him, hey, there's a car trying to get through. And he looks at me and I look at him and I roll down the window and I say Jonathan. And it turns out it's one of my former students who is camping there the same night and he would had wanted been wanting to camp there because they used to do it as a family and it was his 16th birthday, he's like, what do you want to do? I want to go camping. So they happened to camp there the same night and he had a bunch of friends coming to camp over and hang out and celebrate his birthday with him. So I end up seeing not one but two of my students while camping and we had just a great conversation and caught up for a little bit and talked about what they were doing. And both of them are taking computer science now and both of them are interested in going into computer Science as a major when they get to college in a couple of years. So I was so blown away and they were so excited to see me and I was so excited to see them and we just got to talk for 15 or 20 minutes and chat a little bit and it was a huge, huge win. I was so excited. Kelly Schuster Paredes: That's so huge. That's so cute. I think more and more the students are actually starting to realize that, hey, computer science is actually a very important thing now and I think that's become a big transformation at our school, at least from what the kids say. I haven't seen anyone come back from college yet and say, yes, I majored in compsi, but I'm hoping, knock on wood, that's going to happen more and more. I majored from my python. Sean Tibor: Well, I have to say I did meet up with a few college students from our school that were back for their break and they are majoring in Computer Science. It's just a really interesting conversation because they're in the middle of that transition from being students to being developers and having real skills. And so I'm learning as much from them as they are from me. Like, tell me more about that DSH shell customization that you did. That sounds awesome. Send me some code snippets. Right? Kelly Schuster Paredes: That's pretty cool. Super cool. Any major fails? Sean Tibor: Oh, plenty. There's been a lot of things. Nothing that's a major screw up or anything like that, but just lots of stuff that I wanted to be further along on. But life got in the way and so it's coming together and I'm regrouping and having some really cool new technology that I'm working with and really enjoying that. So it's been kind of fun to get there. Kelly Schuster Paredes: I had a really stupid fail. I was working with my tutor because we get a tutor once a week and it's just nice to talk through and I have a great tutor. He's amazing and he can hack through my code because I'm always trying something new. He's like, well, why are you doing that? Is that part of the assignment. I'm like, no. And so there's one assignment we had to go and plot 600 weather cities and pull the cities and all the information from the JSON file. And I'm going along and this is like really easy. And I'm playing with the date time module and trying to change the date from Unix to whatever date time. And I'm plotting and I'm like, plot number 15. And it's the same thing, linear regression. And he says to me, he goes, Can I ask you a question? He's like, yeah. He's like, how many of those did you do? And I'm like, oh, I know where he's going with. And I was like, yeah, like nine. And he goes, Why didn't you write a function? And I'm like, Shush. Sean Tibor: Yeah, I'm really trying to work on the whole thing. If I write it once, fine, if I write it twice, okay, I'm feeling a little weird, but if I write it three times, it's time for a function or some other modularization. Kelly Schuster Paredes: Yeah, I felt really dumb. I wasn't sure that he was prepared for me to understand the functions and we're putting in. I made a linear regression function with eight parameters and only had to change the data frame from which it was coming. And I was like, thank you for helping me refactor this. This is an assignment I would do in 8th grade. And it just comes to show, like how sometimes you can teach it, teach it and teach it, but you actually need a teacher to help you as a learner to remind you of the things that you know you should do. Sean Tibor: That extra set of eyes, write your doc strings, take your vitamins, that sort of thing. Kelly Schuster Paredes: I'm really excited about sharing stuff. Oh, you want to go into before. Sean Tibor: We get into sharing, we want to do a news announcement. So I know it's going to add a little bit of extra time, but Kelly and I are going to be the co chairs of the Education Summit this year at PyCon 2023 in Salt Lake City. We're working out exactly the details and the logistics of how that's going to work and who's going to be where and doing what. But if you were there last year, it will be in the same room, same location. We're working on getting some virtual streaming set up, so fingers crossed that'll work out. But I wanted to make it be known that the call for proposals is open so you can submit talk proposals for the Education Summit. It's live on Pycon.org. I believe it's the website. I will put the link in the show notes, but very exciting stuff. We had a great turnout last year with all the educators coming in from all over the world and looking forward to doing that again this year. Kelly and I will be officially the co chairs of it, but we are also looking for volunteers and for help. So if you're interested and you know that you're going to be at PyCon, or even if you just want to help out remotely, please get in touch with us. We'd love the and appreciate the help. And we're excited to hang out with a bunch of PyCon teachers in a few months. Kelly Schuster Paredes: Absolutely. Super excited. It's going to be fun, whether you're virtually or there. So we'll see. Sean Tibor: So let's get the word out. Let's get some good talk proposals, excited to see what's going to happen. It's going to be a good time. PyCon always is. Kelly Schuster Paredes: Yeah, absolutely. Can't wait. Sean Tibor: All right, so now our main topic. Let's go talk about what we're reading right now. Kelly Schuster Paredes: All right. And it's been really hard for me to pare down, so I didn't want to do all like, panda, panda NumPy. Sorry. Daniel Chen, if you're listening, I even picked up the book that you signed for, Sean, and I was like, yeah, I'm not going to share that one. That was on my list. But I was like, these people are going to think I don't read anything except for data, which is true right now. I'm going to let me start first. Sean Tibor: Yeah, why don't you go for it? Kelly Schuster Paredes: Well, since I've ruined the surprise, two of these books are actually one of the books that they recommended. And I have my stickers from London, love London, and they're all kind of old. This is definitely a lecture book, I think, but I love it. It's storytelling with data. And it's by Cole, Nussbaumber and Netflix. But what I like about this is and this is something that I think you can use in schools, definitely in science, in talking about what type of data you're going to have and how, for example, some of the data that you see on the news is just misinterpreted or mislabeled in the graph, which can skew the way that we read data. And it's really quite well written, very easy writing, I should say. And it's talking about presentations and just how are you taking the data that you've manipulated in a graph and actually telling a story and sort of like teaching? You don't want to just spew out a whole bunch of stuff, but you want to tell a story with what you're teaching. And this book is pretty cool. That's mine. Storytelling with data. Sean Tibor: And if you're interested in that book, I have a professor from the University of Miami that's written four books that would be amazing here. It's Alberto Cairo. He is amazing about visualization and data in terms of storytelling, and he focuses particularly on journalistic data, although, you know, there's that's a pretty wide range. Maybe there's other things that he does as well. I've read his book The Truthful Art, which is amazing. It's all about data charts and maps for communication. It's really fascinating. And he goes through just a whole range of visualizations throughout journalistic history that have been done, some really well, some not so well. It's very good. He's also got a book called The Functional Art, which is really about information, graphics and visualization. So it's more of an introductory. He's got one that I love, the title. It's called Nerd Journalism, and it's all about data driven journalism. But I think the one that's most relevant for students and something we should be teaching is one called How Charts Lie. And it's about misrepresenting data through visualizations. And I think the most egregious one that he shared, because I follow him on Twitter, it was one on Fox News where they actually inverted the graph so that the lower the bar was, the higher the actual value was. It was so weird, and I couldn't believe that they thought they could get away with it, but it was completely ridiculous. And it was something that these sorts of books do a really great job of exposing and explaining and helping you think critically about the way that data is presented to you. Kelly Schuster Paredes: That's really good. There's drawing a blank. Bowler the mathematician woman. Sorry, I'm trying to think, but she's she's really promotes data and talks about the idea of how we should use data in math and science in the K Twelve, and just stories like that, of showing kids graphs and just saying, what's wrong with this? And how can we change this? And why is this type of chart? Why is it a scatter plot versus a line graph or horizontal bar? I think all those things, those topics are easily implemented anywhere. History, math, even English. Let's write about this graph or something. Sean Tibor: Yeah, I mean, I would love to put up like a bogus chart of the week or bogus visualization of the week and have students talk about why it's bad and then throw them for a loop and put one that's actually good up there and see what they do with it. Right. Kelly Schuster Paredes: And be like, Why is this bad? Sean Tibor: It's not. Kelly Schuster Paredes: All right. Do you have a book? Sean Tibor: You just do five? Yeah. I'm trying to give related books, but this is an area that I love as well, so it's pretty easy for me to find good stuff. I'm going to bring up a book that I love and I've been really focused on over the last few months because it's important to bringing on new engineers. I've got a book called The Missing Read Me, which is about all of the things that they don't teach you in college, about how to be a software engineer that you really need to know. Like, how do you do a code review as both the one being reviewed as well as reviewing others? And it's really structured. This whole book is structured in a way that allows a nice progression that takes about a year from being a fresh college graduate to being an experienced, trusted, contributing member of a software engineering team. And that first year is all about learning. It's such a learning intensive and learning driven progression that people have to go through. And so this really structures it in a nice way and it gives you different chapters and you can kind of bounce between things depending on what is happening in your day job. So I really like this book. It's from no Starch Press, which is always one of my favorite publishers. But it's something that I go to myself because there are gaps in my knowledge and this helps fill that in. Kelly Schuster Paredes: I was talking to one of my mentee, my 10th grader, who comes into the class, my mini typer, I always call him, who loves hardware, is helping with the kids and helping to really push my exploratory robotics forward, because I tell the kids to do one thing, but he tells them the same thing, and they're like, Cool. And I'm like, I just told you to do that. But anyways, I was talking to him about some things in PyCon and I was trying to explain some concepts and he's like, well, it's like this. And I'm like, I don't know, let me check. And we were having this conversation about there's just so much information about computer science and all the stuff that you need to learn as a person who wasn't a computer science major, I have a huge learning curve. And he's just like, wow, it's so cool that it's related and there's just so much. And I was like, yeah, you can never stop learning. Here, go read this. That would be a good book for him. Sean Tibor: I think you'd probably like it, especially at the right time in your career when you're actually going to use this and apply the knowledge. It's very helpful. I always think about my computer science classes that I had and how many things would have been helpful if they had been designed backwards, right? So it was always like theory first and then practice. I always felt it would be better if they had put more practice first and then explained the theory behind it. The one I always think of is like polymorphism, which is the idea that you can have something with a single interface, like a the same function name or the same method name, but it could have different behaviors depending on the types of parameters that are passed to it, right? Not as like obvious in in Python we kind of do this already with different ways that we invoke our functions. But I always thought that that was something that was so hard for me to grasp when I was just learning it as a theory. But as soon as I had some practice with it and they showed me some examples of real world, here's how you would use this, and here's why that became so much more accessible and easy to understand. So as we're looking at this, it's always helpful to have the right information at the right time when you need to use it because it makes the learning happen faster. Kelly Schuster Paredes: 100%. I think we can attest to that of the stuff that we've done in the classroom. Definitely practice, practice and then can make those connections with the concepts. Well, my second book, I have to give a shout out to the incredible Deborah Jacoby, who is like the guru of computer science in the lower school. She sent me this link, and it's on my I haven't read it in entirety, but it's one of these books that when I saw it, I was like, oh, I love it. It is called the Fundamentals of Artificial Intelligence by Dr. Nisha Talagala and Dr. Sindu Ganta. And this book, it's definitely for the middle school and talking about concepts and things that can be applied and about all the stuff about computers, literally. I have to show this. Sean Tibor: I'm so glad there's so many great visualizations in there. Kelly Schuster Paredes: There's so many great visualizations, and it's where is data stored? The teacher's corner, unplugged activities, training the AI. It goes into explaining AI bias, which is so timely for chat GPT, going around there, talking about characteristics of good classification of AI, online activities, tons of Python exercises. I saw this book, and Deborah saw this book, and she was like, you should get this book. And I said yes. And there's even QR codes to go to curriculum. And I thought, wow. And I haven't played with a lot of it, but regression, I mean, this is all the stuff I'm learning in my teacher analytics course, and I'm just like, super. Sean Tibor: So you have a cheat sheet. You have an extra resource. Kelly Schuster Paredes: Yeah. Kndict. So if you don't know what a KN predict and I'm sure you do, Sean. Sean Tibor: Sure, sure. Kelly Schuster Paredes: You do. Sean Tibor: Totally know everything about everything. Kelly Schuster Paredes: Yeah. Regression types of problems. So this is perfect. So I'm doing a discussion right now on linear regression. So some of these topics, I've only started to skim the top of it, but it's on my desk. I carry it in my backpack. It's one of these ones that I'm digging into slowly because there's just so much to consume, and it's actually stuff that I'm going to be implementing into the curriculum. Sean Tibor: Nice. Well, my next book is another one that I'm finding useful in my day job. It's called accelerate building and scaling. High performing technology organizations. And this is not so much about teaching, but as I'm reading it, I'm trying to think about how would you use this in the classroom? How would you use these approaches in the classroom? One of the early ideas in here is that we are often in technology, optimizing for cost. So how do we make the lowest cost solution to this problem? And there are some parallels there, I think, with teaching, we're often optimizing for a test, or we're optimizing for a score, or what's the lowest effort I need to be able to communicate this idea and get people to learn it, right? So we're trying to make it as cheap as possible. And you can see, especially at an administrative level, a lot of the cost optimizations. How many kids can I cram into a classroom? Or how many subjects can I pack into a day with the same number of teachers and all of these things that optimize the cost of education. But the transformative part of this is optimizing for speed. So when you start to think about how can I make my processes for making delivery of software faster than what you end up is actually getting cheaper and you get the stability and you get all these things because you're optimizing for speed. And the only way you can be fast is if it's cheap and if it's stable, right? So how could we apply the same idea in the classroom instead of saying how do I optimize this for cost? What is the equivalent of optimizing for speed when you're teaching? And I don't know what that is yet. That's one of the things I'm hoping to discover as I read this book, is what are some ways that we can use this for making learning faster, right. Without sacrificing cost or stability, meaning like retention and understanding. So how can we accelerate that speed of learning so that people, whether they're middle school students or software engineers, can get to a more productive understanding of what we're teaching them as quickly as possible so they can go further? Kelly Schuster Paredes: That would be cool because we do a reflection and think about how can we make this better? How can we make it stick? But actually, can you imagine if we could iterate enough times and optimize the lessons enough times to actually increase the amount of curriculum that gets put in. And I think we've done that with like and I've done that, I think, with 6th grade. But now it's like how can I get faster? How can I get more? And you always want to optimize and perfect. So when you find the answer, let me know. Sean Tibor: I will certainly let you know. I don't know that it will come from this book, but one of the things I do like about this book is it's very data driven. It's written from the perspective of working professionals in the field through survey results, through analysis. So it's not just someone who's trying to sell something. This is really more of a research driven approach to understanding how to make your technology organizations more effective and faster. Kelly Schuster Paredes: It's funny because a lot of our books are data driven, which just hits the point of the fact that data is out there everywhere, everything's connected. And if you're reading a book that doesn't have something embedded with data, then they want to question a lot of research going on. Sean Tibor: Well, I think you and I both take an approach of being very evidence driven in our learning, right? Kelly Schuster Paredes: Oh, really? Sean Tibor: Oh, look at that. A book about evidence driven learning. How about that? Go ahead. Kelly Schuster Paredes: What a good segue. Everyone knows that we spend way too much time together. This is another book that I just started to read and it's from it was not a required but a recommended from my instructor and literally not far into it. But I love it. It's leading with AI and analytics, and it's actually going from the point of the business person, the person that's not necessarily the coder, which I liked right away, because this is something that we tell our students. You don't have to code, you don't have to be the developer, you don't have to be the person behind the computer making the code, but you need to understand what's happening with that code so that you can use it, comment on it, understand it, and help someone build it. And I think getting into this is the idea of a lot of businesses say we want data. We want someone to come in and take all this information and tell us what we're doing is right, or tell us the magic key to do something. And sometimes leaders are asking the wrong question, asking the wrong question of the data. So the data actually can be skewed by your question. And for example, if we say, how can we increase our sales of whatever umbrellas? And then the guy says, okay, well, on rainy days we sell more umbrellas, so how can I make it rain more? It's like silly questions like that. They just want the quick answer from the data. And I know that's really a lame example, but the idea is if we think about our questions and we guide our questions better and deeper and clearer, we can then look into the data and develop better answers. I don't know if that makes sense. I hope it's really quite interesting read. I love to see what it says on the inside. I haven't gotten to the AI part of my course, but just really digging into the data. I'm trying to think of an example that I just posted on LinkedIn. So much going on. Why are you thinking and comment? Sean Tibor: I'll find the post well, it definitely makes sense because so much of business growth right now and the business drivers are, can we move better and faster and cheaper? Right? But a lot of the times that means I need to make decisions. How do I make better decisions? How do I make those decisions faster? And how do I make it so they don't cost me as much? Right. And so having the right data at the right time is extremely helpful for this. I recall when I was working in marketing and in digital marketing about 15 years ago, I sat in on a review session where we were talking about media effectiveness. So we had spent this amount of money on buying banner ads and we wanted to see if those banner ads were effectively driving more sales or more demand for our products. It was amazing to me. We walked in the room and the time period that we were looking at for the effectiveness of this was a year and a half ago. Right. There are some really good reasons for that. I don't want to make it sound like we were just slow with the data. It takes time to really get the insights and see the effectiveness of those ads over time. But it makes it really hard to make decisions when you're basing your decisions on data that was acquired a year and a half ago. Right. So how do we make this happen sooner? How do we make it faster? And I'm sure that they've shortened that time down pretty considerably and the data's gotten better since then. But it's something that is in high demand, and especially when you're spending large amounts of money on marketing, sales, operations, supply chain optimizations, you want to make sure you have the best data and the most current and the most accurate. And that's not always easy to do. Kelly Schuster Paredes: Yeah, 100%. I remembered my post on LinkedIn, so it was about World War Two. I want to say, it was like looking at the fighter planes and all the fighter planes that came back had all the bullet holes and the same I'm sure this is like a common story, but had all the bullet holes in the same spots, and they were like, okay, so we're going to reinforce the places where the bullet holes are because that's where they all got hit. And I guess a mathematician was like, but that doesn't make sense because the planes that didn't come back, perhaps maybe they were hit in the places that don't have the bullet holes. So the story behind the concept was that the story behind the data is more important than the data itself. And that's where I get from the leading with AI analytics. Like, where are the business and the CEOs and the leaders of the company? They need to really look about the story behind the data than actually just the data itself. Sean Tibor: Yeah, there's that insight piece to it. So as advanced as we get with AI, and advanced as we are with analytics in real time, we still need these insights. And maybe at some point AI will help us come to those insights faster. But if we're not willing to see it or we're not able to see it, it means that analysis work and those insights aren't going to be useful to us. I love that example. It's the classic survivorship bias example that's given in statistics, and it's exactly right. And the person was exactly right. They reinforced those areas that hadn't been shot on the planes that were returned, and they saw more planes coming back and they saved lives because of that insight and had. A real practical effect. Kelly Schuster Paredes: Yes, that's true. That's why I posted. So there you go. You can read my LinkedIn. Sean Tibor: All right, so so my next book is one that I've been chiseling away at for a long time and I love it. It's a fantastic book and really, really well well done. It's called Fluent Python by Luciano Ramalho. It's at this .7 or eight years old, but it is still really, really good. So the idea of this book is to write better Python, right? More Pythonic. Python. And this is definitely more like the advanced level stuff. That's why I keep chiseling away at it. I go and I read a little bit and I make my code a little bit better and I find another chapter and I read a little bit and I make my code a little bit better. So we're thing from using decorators to using generators to all these advanced language features that can make your code a little bit faster, a little bit easier to understand, a little bit more efficient. It's really well done and it's a classic in the library and definitely is worth picking up. There are some other books that are in that same category. Sirius Python does a lot of the same things, but really it's designed to help people who are in that intermediate to advance Python stage, get even more advanced and write better, cleaner, more fluent Python code. Kelly Schuster Paredes: And I know that's a book that we have in the classroom. I almost want to say I have two copies of it as well. Maybe you bought a third one for yourself, but it's one of those books that I keep looking at and going, okay, one day I'll get tugged into it because it is it looks great when you have a picked it up and skimmed through it and just not there yet. Sean Tibor: It's pretty intimidating and I've been writing a lot of code lately. It's pretty intimidating, but I find it manageable if I can take it one little bit at a time, not trying to devour the whole thing or digest all of it. Just one little thing that I can make my code better with. Kelly Schuster Paredes: That's good and well, my last book, it wouldn't be a Kelly book list if it didn't have a self help book on there. Self improvement, self improvement, self improvement. So it's like tech, tech, tech, classroom, classroom, classroom curriculum, cricket curriculum. How to make things better, how do you Make You Smarter? And this book, it's pretty recent. It's a 2020. It's called Limitless, and it's upgrade your brain, learn Anything Faster and unlock your Exceptional life. And it's by Jim Quick, which is a New York it's a very appropriate. Sean Tibor: Name for learning faster. Jim quick. Kelly Schuster Paredes: Exactly. Well, I love it because he has tons. And this is one of these books that it's almost like a journal. I wanted to hold these pages so I don't lose it, but it's got like, places where you can write. I'm showing you the blank pages. Sean Tibor: Nice. Kelly Schuster Paredes: So it's not a book that you just read in one go. You have a lot of things called Quick Starts. So, yes, he used his name. Pretty cute. Think about a decision you need to make. Schedule some time to work on that decision without the use of any digital devices. And it's really cool because it's about using the most powerful technology in the world, and that's your brain in order to improve your learning, your life, your work. And I literally am still on the mindset, which is amazing. That's like the section two, but free your mind, limitless mindset, limitless motivation, limitless methods, and limitless you. And it's just nice. It's one of those things that you put on the bed stand and you read a short bit or even I mean, I read just little snippets of it, because everywhere you look in the book, it's like, Quick Start, what are you going to do? What are your current passions? What are your life's purpose? And it's almost like one of those goal setting books that is great. And it's like throughout the book and this is so self helping, but it's got all these great little quotes, like, whenever you want to achieve something, keep your eyes open, concentrate, and make sure you know exactly what it is you want. No one can hit their target with their eyes closed. It's one of those books that you have to have on your nightstand if you, like, make these self improvement books. Sean Tibor: Nice. I love those ideas of, like, trying to drop your preconceived notions or remove barriers to your own progress. Right? I always think of this whenever we were doing it. Whenever I'd workshop something with people and you could see that they're a little bit stuck in their ways, it's always like, okay, I'm going to give you a magic wand now. Right? Here's your magic wand. If you could change anything about this poof and it's changed, what would you change? Right? And that whole idea of a magic wand really works well because it gets everyone to drop the questions of, well, we can't do it because of this, or this would never work, or no one would go for this. It's like, no, we're going to change this because it's terrible and it needs to be better. Right? And that idea of dropping those boundaries and having a little bit of magic works really well because maybe you can't do exactly that thing that they ask for, but you can reframe the conversation away from the barriers to getting it done, too. So if we were going to get as close as possible to that, what would we need to do? How do we make it happen? And people start to open up and their possibilities open up when we do that. Kelly Schuster Paredes: Yeah, 100%. Love that. That's my list. Sean Tibor: All right. Kelly Schuster Paredes: I mean, I have a stack of books on my nightstand, but I haven't even started to read them. I could list them all, but they're just looming. Sean Tibor: I've got a couple of bonus books to add that I have not read yet, but I would love to get the chance too soon. There's a new addition of Python crash course out by Eric Mathis that just came out or is coming out right now. So if you are trying to learn Python from the beginning, it is one of the best books out there written by one of the best people out there, and it's a new addition that's got some new stuff in there, some new features, some new libraries. It's always up to date. Erica is fantastic about keeping it as modern as possible, so you'll be able to see a lot of this in the new edition. I'm looking forward to it. There's a lot of good stuff there. I haven't gotten my own copy yet, been busy with other stuff, but it was definitely something that caught my eye that I wanted to recommend to people. I also saw on the Nostarch Press website there is a new book that came out in September that is called let me find it here. It is called The Book of Dash, and it's all about building dashboards with python and plotly. And I think that would be a really fun book to dig into, especially in that no start style of being very tutorial oriented and step by step instructions for how to get things working and working on projects and then extending it further. So this is a pretty cool book that I think is going to be ordered and hitting my mailbox pretty soon. Kelly Schuster Paredes: I'd like to see a book. So I've been using Seaborne and instead of just matt plotlib, seaborne, if you don't know it, it is like I guess it's a wrapper. I don't know if that's the correct terminology, but it runs off of matplotlib, so it's like an enhancement. What's the correct terminology for that? But it runs off of matt plot lib and it's just pretty. But I'd love to see a book, so if anybody's listening that has all those graphing libraries in one and just kind of like analyzes the difference because I was doing plots and matt plotlive. Plots and plotly. No, not plotly. Pandas to jupiter pandas. Yeah, plot. Plotly and then seaborne. I probably said it wrong, but they wanted us to do the three different libraries to see the difference, and I just couldn't stop using Seaborne because the hues and the colors are just great and it's just an easy graph to you. Sean Tibor: I'm trying to remember. I think I saw it at a conference at some point, and I need to go back and look for this. There is like, a comparison of different graphs and different visualization libraries and how they each work along with the code behind them. So I'm going to see if I can find that. I think I remember seeing this. The other thing that's really fun to check out if you haven't seen it already is Matplotlib has a fantastic gallery of visualizations that people have created with Matplotlib, and it gives you code samples for each of them as well. So if you're kind of looking for that, I can only use Matt Plot Lib to do something and I need to make it look a certain way. That's a great resource as well. And we'll put the link to that in the Show Notes. Kelly Schuster Paredes: Yeah, and they updated the website. It's really pretty. I've visited a lot lately, so interested in hearing of any books that anyone else has something that I should add to the nightstand or to the classroom, because most of the books in the classroom are also sitting on my night before you put it up. Sean Tibor: There so much to read and do. Kelly Schuster Paredes: Yeah, so much to read and do. Sean Tibor: So I think that's it. That should cover everything. I've been looking for some good articles about learning as a professional, right? So adult learning and comparing adult learners with adolescent learners. So I have some more research that I'm doing there. So if anybody has good articles about that and I would appreciate it, I want to make sure that we're taking that evidence based approach to learning, especially in a professional setting. But I think other than that, the only major announcement I'm just going to recap real quick is the PyCon Education Summit, which will be taking place on April 20, 2023, in Salt Lake City. We're working on getting that to be virtual as well. Kelly and I are going to be co chairing that, and the Call for Proposals is open now. I think it lasts for about two weeks. So right until the end of January for those Calls for proposals. So we'll be going from there. If you're interested in helping out or volunteering, and if you're having massive Imposter Syndrome, thinking like, oh, I couldn't help with that, I don't know what I'm doing, or I'm brand new to this. We need everyone, so get over that. Come be with us. We'll all suffer Imposter Syndrome together, and we'll put together a great summit within the conference for all of us educators to get together and share best practices and war stories and learn from each other. And it should be a really nice event, 100%. Kelly Schuster Paredes: And if you've never spoken at PyCon, I think going and speaking at the educational summit is a good, easy for speaking, easy way in, because everyone in there, they're so incredibly nice. Not that anyone in PyCon is not nice, but the educators are completely supportive and just a great venue. I did want to add one document I forgot. Following up on our topic from last week about Chat GPT or last two weeks ago, Chat GPT in Education. I'm going to share this on the Show Notes as well. It's from the College of Education and University of Massachusetts, Amherst, and I just thought it was a great living document, something that they're continuing to build on. But one of the big tidbits that we did not discuss in our podcast was this idea of privacy. And upon further investigation from the University of Massachusetts is talking about the privacy policy that Chat GPT has in that long information that everyone kind of SKIMS, but it's saying that the tool should not be used by children under 13. And the terms of use, you must be 18 years or older and able to form a binding contract with OpenAI to use the services. So that just went to be like a whole kind of well, this is an interesting tidbit that we probably should know as educators that we need to be careful. We always need to be careful with technology. But the idea that we need to be careful with Copa compliance and safety with children, and the idea that a lot of the topics that might come back from an AI may not be the best suitable stuff for a child under the age of 13, that's just according to their terms and policies, not my opinions. So just throwing that out there, and I'm going to post that because it was really interesting. It's a great layout for educators trying to navigate the Chat GPT world. Sean Tibor: Yeah, that's a really great ad and definitely something to keep in mind as you're looking to incorporate it into your teaching. Kelly Schuster Paredes: Yeah. Sean Tibor: All right. So if you want to continue the conversation, where could you send all these book ideas and topics and thoughts? Come to our website, teaching. PyCon FM. There's a form you can fill out there that sends directly to Kelly and I. We are actually not that far behind on responding to listener emails, which is pretty amazing. We're also on Twitter at Teaching Python. Kelly is at Kellyparette on Twitter. I'm at Smtyber. Please feel free to reach out to us. We're always excited to hear from our audience. It's always amazing to us that we have an audience. So it's great to hear from you. I think that does it for this week. So for Teaching Python, this is Sean. Kelly Schuster Paredes: And this is Kelly signing off.