Talk Python to Me
Looks like we are on a bit of a roll with interviews over at TARTLE HQ. Recently, Alex and Jason had the chance to sit and talk with Michael Kennedy, founder of the Talk Python to Me podcast. If you are unfamiliar with it, Python is a programming language that is relatively easy to learn well enough for regular people to be able to do some cool things with it. Michael realized this years ago and started looking around for a podcast to tell the stories of people who learn to adapt Python to whatever field they are in to help answer some genuinely interesting questions. Unfortunately, back in 2015 no one was really doing that yet, so he had to start his own. Talk Python to Me is the result. Since then, Michael has been able to interview a wide variety of programmers and gotten some real insight into how to help people get more into the world of programming and data science.
One of the big effects of the rise of the Python programming language is the democratization of both coding and of data science. Because it is so easy to gain a working knowledge of, a vast number of people, from philosophers to economists have been able to use it to help in their given fields. Kennedy has noticed that many people who don’t consider themselves coders or software developers are making use of this language. It may be that Python’s greatest accomplishment that people who would normally never get into coding are making effective use of it.
Another insight that Michael talks about is that while educating people on the possibilities of both data science and coding it would be more to the point to say it comes down to inspiration. That is contrary to what the prevailing opinion was ten years ago. Back then, people were complaining that there weren’t nearly enough data scientists and we need to try to convince all kinds to be data scientists. However, that wasn’t working. They didn’t understand it and it just wasn’t interesting to them.
So, what does it mean to talk about inspiration instead of education? It means that instead of telling people how important things are, we need to actually show them. Demonstrate what data science can do and let people actually play around with it a bit instead of just cramming people’s heads full of information.
Also, don’t just shove data science down people’s throats. Instead, show them how it can benefit what they are already passionate about. If a high school student is passionate about volcanoes, show him how Python can help him better predict eruptions. If another is interested in tracing the evolution of language, show her how the programming language can be of use in showing how one language evolves into another. Doing that makes the concept of data science not just real, but interesting.
It isn’t just the sciences that benefit either. Guests on Michal’s podcast have included members of F1 and NASCAR racing teams. They’ve found that in switching to Python from Excel they’ve gained an edge on the race track.
Pointing out things like that makes it easier and more enticing to people to learn Python. Suddenly, they are able to really get into their own source data, maybe for the first time. That lets people save both time and money by avoiding third parties altogether. They no longer have to pay for some other service to analyze the data they’ve collected, or spend time sifting through reports on data someone else has collected. Now, a person can actually gather their own data and maintain control of it from beginning to end. Saving time and money while putting people in control of their own data? That’s something TARTLE can get behind.
What’s your data worth?
Speaker 1 (00:07):
Welcome to TARTLE Cast, with your host Alexander McCaig and Jason Rigby, where humanities gets into the future and source data defines the path.
Alexander McCaig (00:26):
Hello, everybody. Welcome back to TARTLE Cast. You're here joining us today with our special guest Michael Kennedy. He is Talk Python to Me. And we'll have him dive a little bit into what that means, but we brought him on to talk about data, Python, and some stories that are very pertinent to what's going on right now, especially in Mars with what's going on with the Rover. And also I want to talk about how Python and big data and machine learning also goes into scrolls, religious scrolls in the Middle East. So I think we got a little interesting podcast. So let's kick it off, Michael. I'd love to hear a little bit about Talk Python to Me, tell our guests what that means, how you've kind of brought that to fruition and what the focus is really about.
Michael Kennedy (01:09):
Yeah. So I wanted to create a podcast that told the stories of everyone doing Python. And so Python, programming language. It's really interesting. You think of a lot of the programming languages, they're kind of real serious C++, Java, and so on. And people have CS degrees and they go into them and so on. But Python is unique because so many people find their way over there as non-developers. They would probably introduce themselves as, "I'm an economist, definitely not a developer. I'm a biologist, definitely not a developer. Oh, but look at this amazing machine learning model I built." And that's like the kind of people that are over there, along with very serious developers who build like YouTube and Instagram and so on. But it has this really interesting mix of people coming from all these different disciplines and areas.
Michael Kennedy (01:57):
And so I really wanted to explore that and tell the stories of that. And honestly, when I was getting into Python, there were no Python podcasts, which was bizarre given it's one of the most popular languages, technical ecosystems out there. So I decided if I'm going to be able to hear these stories, I guess I'm going to have to create the podcast myself instead of just subscribing to someone else's, and really happy I did.
Jason Rigby (02:21):
I want to ask you real quick. I know you said this is probably pretty much the predominant only podcast that's out there. What led you to that? Were you working for a big tech company or a small tech company? What started this whole venue for you to be able to say I want to get the word out and I want to kind of be the authority on Python?
Michael Kennedy (02:41):
Yeah. Yeah. The original goal was not necessarily to be the authority, but just to be a meaningful participant. And it just grew over time. To be fair, now there are a couple other podcasts that are really good out there, but this was 2015 and there weren't any at the time, right? And so it's just been growing even since then.
Michael Kennedy (03:00):
But for me, I was working as a developer training person, professional development trainer, who would go around and teach courses. I would travel through Europe or to Asia, and I would spend a week with people teaching them stuff and inspiring them with technology, but it might be 15 people. I'm like, "Well, couldn't I do more on the internet? Couldn't I reach more people in a different venue? And so that was part of the motivation was, "I'm putting so much time and energy and love into this, but could it be more accessible to everyone else?" So I also wanted to kind of broaden out in that way. So it's something I did as a sort of nights, weekends project while I was working for a training company.
Alexander McCaig (03:46):
That makes sense. If we think about characteristically of where we're seeing the internet headed, especially with decentralization, when you talk about bringing the knowledge of Python and the power of it to just about everybody, because it lacked that medium for even any sort of discussion until you came on the scene in 2015, I think where this is mirrored is a very logical step that needs to happen with this information.
Alexander McCaig (04:13):
There's a lot of beauty in terms of research, democratization of data, people coming together building Python models collaboratively, or even doing it on their own to say, "Hey, with the onset of data and programming languages that are geared towards this type of analysis, whether it be machine learning or what have you, there is a lot of good that we can do without even having to be a person that works for some major corporation or have a specific PhD." I think that the democratization of that effort, that decentralization of everybody coming and working on this in a collaboration with collaborative data is where this thing starts to take leaps and bounds, wouldn't you say?
Michael Kennedy (04:53):
I would absolutely say. One of the areas that I think is really interesting to think about is... You don't hear it quite so much, but you think back five, 10 years ago, there used to be a lot of conversations around, we have all this huge skill gap around developers and data scientists and so on, and what we need to do is we need to start teaching kids computer science in school. Because there's a million computer science jobs that are not filled, we need to make a bunch of small computer scientists that grow up and go in those plugs, right? Like they fit into those spots.
Michael Kennedy (05:27):
And that completely misses the real value of what is out there and what this... Like being able to work with data, being able to work with code. The real value is whatever you're into, whatever you're super passionate about, like are you studying volcanoes? Well, you know what, if you know a little programming and a little bit of data science, you can just superpower what you're doing compared to people who don't have that. Are you a biologist? And you're trying to collect samples and analyze them in Excel? Well, if you could use a little bit of data science, programming, you could completely 10X what you're doing.
Michael Kennedy (06:05):
Look at everything that people care about. Are you into race cars? Like I had people both, one guy at my podcast who was on two F1 teams actually. And another guy who was in one of the top NASCAR teams. And they both told the same similar story in that they were using these old tools like Excel and other things to optimize their race strategy and their aerodynamics.
Michael Kennedy (06:27):
And they switched over to working with the data science tools and the Python ecosystem, and it just gave them a huge advantage on the racetrack, right? It was just like that story repeats over and over and over. And so what people should have been saying 10 years ago is not, "We need a bunch of computer scientists, we need to empower people to find whatever they're super passionate about and completely ramp it up with a small, tiny bit of code." And I think that that's why Python is so interesting, is you can be really effective with just a little understanding, you don't have to be a computer scientist.
Alexander McCaig (06:58):
No, I think that's amazing. And I also want to talk about this example of this gentleman in the Middle East with the scrolls, because if you're talking about empowering people-
Michael Kennedy (07:06):
He was actually in... where is it? Is it Denmark? Somewhere [crosstalk 00:07:10] generally, but he studies in the Middle East.
Alexander McCaig (07:12):
Whatever he's looking at, he's studying in the Middle East, right?
Michael Kennedy (07:13):
Yeah, it's so interesting. Absolutely. Yeah.
Alexander McCaig (07:14):
And this is the cool part, you can be in Denmark and study the Middle East if you chose to do so. It's fine. You don't actually have to be there. But you're talking about empowering and impassioning people, right? So he has a passion to study these specific things, and he's empowered through using Python and its models with the right data to come to some sort of answer or some sort of guidance for where he wants to bring his research. Could you tell us a little bit about that story?
Michael Kennedy (07:38):
Absolutely. The guy's name is Cornelius Van Lit, and he may be is tired of me telling his story at this point. Probably not. I share this a lot because it's such a contrast, right? It's just such an interesting contrast. He reached out to me and said, "Hey, Michael, I'm doing some fun stuff with Python. I'm a philosopher and humanities researcher." And I'm like, "That sounds kind of interesting, but I really I don't know." He's like, "No, let me just tell you what I'm doing." And I said to him like, "Okay, I have to tell the story. This is incredible."
Michael Kennedy (08:07):
And so what it is, is he and some other folks are studying these scrolls from philosophers from like medieval Islamic philosophy. What was that? Like 1580 AD or something? Maybe a little farther back. But anyway, they get these scrolls that are handwritten in a really flowery way, so that the text is not super clear, but there's a lot of other interesting things that they can study.
Michael Kennedy (08:32):
So in order to say, "I collaborated with so-and-so." Like if Socrates wanted to say he collaborated with Plato, I know they're not Middle Eastern folks, but that's just an example of philosophers, right? Instead of just saying their name, what they would do is they would have these really ornate stamps that are kind of like their official signature. And so they would put these stamps on the scroll.
Alexander McCaig (08:54):
Michael Kennedy (08:55):
Yeah. Similar. Yeah, exactly. But they're really ornate and complex. And so it's difficult to say, "Well, there's a bunch of stamps here, who has worked with whom on what?" So he actually use Python and computer vision to learn those stamps and then create a collaboration graph of all the philosophers.
Michael Kennedy (09:16):
The other thing is, if you look at the scrolls, for some reason, they had a very consistent way in which different times, different areas, they had more of a point or less of a point. And understanding the actual folding shape of the scroll would let you place it in history like, "This one looks like this, so it was probably from 900 AD rather than 780 AD."
Michael Kennedy (09:42):
Yeah. So this guy, he's like a librarian, sort of book historian. And he's not at all a programmer. And he's like, "You know what, I'm going to learn some Python. I'm going to learn a little computer vision." And within a year or two, he's going around talking to other people in his field saying, "You guys, you have to open your eyes to this. This has fundamentally changed the amount of research and the quality of research that I can do, because now I just run all these thousands of scrolls through the system and it just gives me the answer, which is, I mean...
Alexander McCaig (10:18):
So you're saying-
Michael Kennedy (10:19):
Thinking the power that guy has got, and from just a little bit of work, it's incredible.
Alexander McCaig (10:22):
So for a minimal amount of effort and him seeing the value in taking this primary data, these scrolls, and then being able to analyze it in efficient systems using Python, he was like, "This is incredible. I need to be an evangelist for this type of research." Because when you get primary data, okay, and this is an obvious bridge, the things we do at Turtle. It's always about that primary source data. When you have that, that affords you the opportunity to really look at things without the bias of someone else coming in, right?
Alexander McCaig (10:56):
So there are systems out there that just read papers all day long on research that people have already done. And so what it does is it comparatively looks at the second or third party research. But now when you're looking at the primary data in this primary research that's happening, and he can work with those models and other people can work with their own models on top of this, now it's an interesting dynamic that's actually occurring. It's a very empowering dynamic and one that removes a lot of the bias out of the equation. And I think that's a phenomenal thing about what's happening with this decentralized, democratized effort of people using Python to elevate data in their own understanding of the world.
Michael Kennedy (11:31):
It's really interesting that you call out the unbiased view. Yeah, I'm sure that at different stages of going through data, you may be at the beginning, you see patterns, and later you decide those patterns are not important. You've coded them in this way, you've classified them that way or whatever.
Michael Kennedy (11:49):
There was a really interesting set of guys I had on the podcast a while ago. Yev and David, I believe, were their names. They were in the UK. And they were astronomers studying exoplanets.
Alexander McCaig (12:04):
That's pretty cool.
Michael Kennedy (12:07):
Apparently the Kepler telescope had been out there gathering data for forever. And I believe it's now defunct or basically no longer really working. And you would think the science is over, but there's so much data that people haven't really been able to go through it all. And so they were the first people to use machine learning to discover exoplanets.
Alexander McCaig (12:28):
Michael Kennedy (12:29):
Yeah. Not only did they discover one exoplanet, they discovered 50. And they're just getting started.
Alexander McCaig (12:35):
This is a really interesting point is that there's a lot of data that's out there. But the question is, how do you use the right algorithm, program the right algorithm to look for something you don't know to look for? And I think that's a very incredible aspect to data science that needs to continue to be fostered. It's like, how do we solve for an unknown when we have no idea what the unknown or what direction we even need to take in the first place?
Alexander McCaig (13:01):
And I think being able to code that in without having the biases in place is going to be a very powerful thing for how we begin to analyze that data. And even going back and looking at old data, that's not even new stuff, just the old stuff, and reanalyzing it without the dogmas we had in the first place, wouldn't you say?
Michael Kennedy (13:17):
Yeah. I would say the reason I brought up that story is you talked about the bias and the influence. A lot of times bias is seen as this group or this person has a worldview that just it's not even necessarily conscious, but kind of skews them to see things in a way or other.
Michael Kennedy (13:34):
But when I was speaking to those two, they said, "On a different day or different times of days, fewer or more exoplanets would be discovered by hand." Like if you've just gone to the afternoon coffee at Oxford and you've had some cookies and you're kind of happy and relaxed, then you sit back down, there might be a different number of exoplanets than if you're tired in the morning or something like that.
Alexander McCaig (13:57):
Oh, snap, I'm totally on top of my game. There's 30 planets I missed here. You know what I mean? And then everybody at Oxford in their bow ties clapping.
Michael Kennedy (14:04):
Yeah, exactly. It's so crazy. I mean, you think of these situation... I mean, I would have never really thought like, oh, the time of day matters. Maybe different people, but not the time of day, but it apparently does. And so if you can teach unbiased or at least consistent machine to come up with the answers, then it doesn't really matter.
Alexander McCaig (14:27):
Yeah, no, at that point, and that's what we're looking for. And then if you start feeding that great information, I mean, now you have a balanced approach for this machine to actually work on.
Alexander McCaig (14:37):
Jason and I are always reinforcing. We need strong, fundamental foundations. Our focus here is on people, right? And we understand that people are the drivers for absolutely everything in society. And if we get that first part of information from them, and we can remind businesses, research centers, wherever it might be to not be dogmatic, to program things that aren't racist or biased, or put people into boxes they don't need to be in and let them speak for themselves and meet them where they are, now we're not going to miss those people. We'll find those 30 people, regardless if we have the cookie at noon at Oxford or not.
Alexander McCaig (15:14):
And I think that's the important part. It's not about my sugar rush to one day give me some... Just like epiphany that needed to be there. I'm like, "Oh my God, I didn't recognize there were 35 million people in Africa that we've missed that are just as important to get that data from." And I think that metaphor of an exoplanet is the same thing for why we need to go back and also look at how we've been using data and also classifying people because through that approach, it's been limited. And we've missed so much beauty within that data that can tell us about humanity and how humanity chooses to interact with businesses and systems and one another.
Michael Kennedy (15:51):
Yeah. There's so much data out there. I'm sure the vast majority of it contains all sorts of amazing secrets and information that nobody knows.
Alexander McCaig (16:01):
Jason Rigby (16:02):
Yeah. And Michael, I want to kind of get into this, because we had talked about this off air a little bit. Even on the political systems and understanding this. You had talked about some scientists getting together when Trump was coming into office.
Michael Kennedy (16:15):
Yeah. Putting aside the politics side of it, I think it's just a really interesting data story. So there were concerns that when Trump was elected, some of the public data that hadn't been available on like whitehouse.gov and other governmental sites, like some of the climate data and whatnot, which traditionally has been just open and public and published, I don't how, probably some dreadful CSV, like in a weird form. But public and it's good that it's out there.
Michael Kennedy (16:45):
There was concerns that some of that data would be taken down or shared less or whatever. So there was this big rush in, I guess, 2015, it probably was, before he was inaugurated, to do some safekeeping of this public data. And so there was this big move by a bunch of data scientists and other programmers around the world to get together in what you might think of like a little bit like a hackathon. But instead the goal was to web scrape and download as much data as they could.
Michael Kennedy (17:19):
So I believe there was a group of 30 people in Denver or Boulder who got together. There was a group at UCLA who had gotten together and they were like, "We have three days, we have to web scrape as much as we can." And they had sucked it all down, and I believe they put it somewhere in Europe like Switzerland or something like that, to sort of safeguard it.
Alexander McCaig (17:40):
Some sort of neutral territory?
Michael Kennedy (17:42):
Yeah, exactly. Definitely not in the US.
Alexander McCaig (17:45):
Well, I mean-
Michael Kennedy (17:46):
At the time. So yeah, that was pretty interesting.
Alexander McCaig (17:48):
Well, it is interesting because what happens is when you centralize data, when one authority has control over all of it, it actually weakens the system and the value of that information, right?
Michael Kennedy (17:59):
Alexander McCaig (18:00):
And the people who are creating it need to have responsibility over it. And if it is public data, then the public should own that. So they have the ability to manipulate it whenever they choose to do so, and choose to store it wherever they choose to do that.
Alexander McCaig (18:10):
And so when I look at data or the rest of Turtle, were like, if you were the person creating that data, whether you're a researcher or you're a normal person just saying, "These are my shopping habits." It's important for me to say, "I'm the one in control of it, and I'm going to release it and store it where I want to do so." Because if I give it to somebody else, one, I'm passing off the responsibility, and two, when something happens, who am I going to point to for a resolve? What am I going to do? Because I've essentially handed the keys to the castle over to them, and then I'm left with nothing in return. You essentially have no value left in that. And they're the one that retains all the value.
Alexander McCaig (18:47):
And so the same thing happens with education knowledge, even when you talk about Python itself and empowering individuals to learn Python, you're bringing that power back to them. You're impassioning those people to look at that data, look at their own data, look at what they care about. And that's the thing that is going to elevate.
Alexander McCaig (19:05):
So what I would be curious to ask you, Michael, is that when you see the future and you see where this data is headed, and you see the availability of these machine learning algorithms through Python and people understanding not even from a very academic background, but from a very layman background, how to use this technology, what do you see the future looking like? How do you see those changes? Do you see it as a more egalitarian future? What is that vision for you?
Michael Kennedy (19:35):
I definitely see it as a set of tensions, right? I see on one hand, there's this egalitarian view, on the other, there's these sort of attention aggregating places like Facebook and Google and so on, that sort of are with the abundance of data, they become almost the gatekeepers, not of data, but of what is relevant data. What's important data to pay attention to, right? Because you can't look at it all.
Michael Kennedy (20:02):
So I see there's like this tension here. I don't know which way it's going to go. But what I do think is pretty clear is people are becoming much more aware that these trade-offs exist. I think they're becoming much more aware of what privacy means in the sense of, I control my data, or have I given over control to these other people? I mean, we're seeing interesting things on the web around tracking. We're seeing stuff around third-party cookies. Firefox has come out and said, "We're going to basically disable third party cookies completely," which has the ad side of the world all freaked out.
Michael Kennedy (20:42):
Google said they're going to disable it as well and not add back anything, which is both, I think, good and bad on their side. But there seems to be all this attention and interest around that, but where it goes? I don't know. I do think we're going to see a lot more people become creators and not just consumers of information, and more of information processing and creating the tools and using the tools to understand data. Like the philosopher guy that I mentioned, right?
Michael Kennedy (21:15):
Instead of just going, "Well, I'm just going to go through what's out there and whatnot," he's building tools other people could build on top of that. And he can say, "Well, now we know these relationships to our philosophers, now what can we study now? What should the next PhD be about?" Or whatever it is they care about studying.
Michael Kennedy (21:31):
But I think there's going to be more creators in the processing data space because of this. But yeah, the tension is interesting and I'm not sure where it's going to go.
Jason Rigby (21:42):
Yeah. Michael, I want to look at a global perspective with Python. What are you seeing? I kind of want to piggyback on what Alex said. What are you seeing in the future when you look at the global aspect of it? In the sense of, is it just catching on? I know it's been around for quite a while. But in these other third world countries, these development, what are you seeing when you see a global aspect of Python?
Michael Kennedy (22:09):
I'm seeing that there's a lot of interest in using things that are in the realm of Python or the general sort of really open source space to empower people. Places in Africa, they're doing a lot of education around Python and advocacy around Python to help people get these digital skills and become programmers in some form or another. I think there's a energy [crosstalk 00:22:42]. Yeah.
Jason Rigby (22:43):
I know you had said earlier that you traveled the globe and were doing education and stuff like that. Do you think education is what is needed? I mean, you mentioned it even here in the United States with children in learning computer sciences in school, but do you think education is the answer globally, or what are you seeing? Or just not being able to have the tools?
Michael Kennedy (23:01):
Yeah. I think it's more education. I think what's maybe even more important than education is inspiration. Let me give you an example. I'll give you an outside of data example, and then I'll give you a data example.
Michael Kennedy (23:22):
I remember taking American history in high school or middle school or whatever it was. And I think I actually fell asleep in one of the classes and got in trouble. Like it was not my favorite subject. I worked on it or whatever. And then Hamilton, the musical comes along, and my daughters are going around rapping like [inaudible 00:23:41] how did we get to this world, right?
Michael Kennedy (23:44):
It's the same information but it's packaged in a way that is like, "Oh, this is my world, right?" Here's the data side of story. So the UK did this thing with the BBC microbit. Are you guys familiar with this thing?
Alexander McCaig (23:55):
Jason Rigby (23:56):
Alexander McCaig (23:56):
No, no. Please enlighten us.
Michael Kennedy (24:00):
The BBC microbit is one of the early ones of these little tiny devices, right? Like really, really small. Now you would find many of them at a place called Adafruit. There's an organization company that makes these things.
Michael Kennedy (24:13):
They get down to as cheap as like $5 for a little microchip that maybe has a few sensors and whatnot on it. And the BBC microbit was this little device and it had like some LEDs and you could put your hand over and it would sense it, I believe. It had a motion detection thing. Those were a few things it could do. Not very many.
Michael Kennedy (24:31):
But what they did is they gave it to... In the UK, I believe, they gave it to every single seventh grader or every single eighth grader, some middle school age. Every one of them. And after that, before they asked all the kids, "Hey, how likely are you to see yourself as working in data science or a programmer? How interesting is this to you?"
Michael Kennedy (24:56):
On the other side of that year, they asked them again and the numbers went up across the board, I believe. But what was really interesting is the groups who typically didn't see themselves as participating in this world as saying, "Oh yeah, I would definitely want to be a developer." Like young girls, for example, I think was particularly what they had studied. And it went from something like 30% to 60% or 70% of the girls said, "I could totally see doing something like this."
Alexander McCaig (25:23):
Michael Kennedy (25:24):
Yeah. Just by getting this little exposure, this little inspiration. And I think that there's many, many, many opportunities to recast what we're doing in a way that captures people's imagination and interest.
Alexander McCaig (25:35):
No, I agree. And that capturing the imagination and the interest. When I look at them handing them that small little micro-processing board, that's something physical, right? But if you tell the kid, "Hey, you want to become a data scientist?" And they're like, "Well, that's so nebulous." Right? It's not like you're actually playing with something physical. But when you hand them something physical or show people that data can have physical impacts, the connection that we make with this data is then a real, tangible one, and a very emotional one that would encapsulate people.
Alexander McCaig (26:06):
So when I look at how we even look at our marketplace, is the data that you are creating, gives you back something tangible. It gives you money. And then you can also share that data to enhance towards causes, enhance these not-for-profits or these activism groups around the world to do very immaterial things with it. And showing people that bridge is I think a major difference.
Alexander McCaig (26:32):
And when I consider this future, and tell me if I'm off base, is that the more people that become their own budding data scientists using Python or whatever programming language it might be, with the onset of them owning their own data, there's no longer going to be just that reliance on academia to publish that research. It will be the public coming together in this co-creative unifying aspect to say, "We, as the public data scientists, with our public data that we own, are telling you what we have found, in comparison to what you and the centralized effort," which may be albeit quite weak and untruthful. We are saying that this is the path we want to take, because what you have shown is not really a truthful one.
Alexander McCaig (27:17):
And we have come together as a billion data scientists around the world, and I hope it gets to that point to say, this is what our data is defining. This is what we have found. And I want everybody to come together and co-create in that future. And that's how I see that occurring. That's how I see that naturally evolving.
Michael Kennedy (27:35):
Yeah, I agree with that. I think you could easily have a billion "data scientists" in the sense that we have people who are fluent in the language of working with this stuff, right?
Michael Kennedy (27:48):
I mean, we educate kids in all sorts of areas that are not super practical. My personal wish would be that there's some form of this kind of working with data, not even necessarily as a separate topic for people, but kind of woven into things you study. You go to biology class, and oh, you got to open a Jupiter notebook and do a little bit of stuff as part of your lab.
Michael Kennedy (28:15):
I remember having a lab book and I have to write stuff up in my little actual lab book and I'd spill stuff on it and whatnot. How much more fun than would it be if it was visual and interactive and you could explore what you just did?
Michael Kennedy (28:28):
I think having that kind of stuff woven through all of these experiences will lead to people just going, "Well, yeah." I mean, there are tons of people who open Excel and try to answer a little data questions with that. Or some sort of Google Sheets. I think we'll get there with this data science stuff.
Alexander McCaig (28:46):
I think that's amazing.
Jason Rigby (28:48):
Yeah, I kind of wanted to get into a little bit, if you don't mind, I want to get into a little bit with your podcast and then kind of what is your website and then are you on Spotify, Apple?
Michael Kennedy (28:59):
I'm on all the open platforms, so iTunes, Overcast, Pocket Casts, Google Play, all those kinds of things. We also stream live on YouTube. You can find the podcast over at talkpython.fm. And yeah, we're doing that basically every week. I've been doing it for a long time and telling stories like this to a large degree.
Alexander McCaig (29:22):
That that's amazing. I would like to personally thank you on behalf of being a data champion. And that's how I would define that. One, you're evangelical about the story. You talk about the stories and victories of other people that have worked in this in a very decentralized manner. And I think this is just going to become more commonplace in the future, and I'm glad that you are a torchbearer for this new framework of society.
Michael Kennedy (29:47):
Thank you so much. It's so interesting because it really reaches into so many different parts of society and what people are doing and what people are interested in, and that's fascinating.
Jason Rigby (30:02):
Alex was just touching on this, the hero's journey. I believe you're a hero in what you're doing and continue to do. And this ability, I saw the passion that came from you, especially when it comes to education and making that.
Jason Rigby (30:20):
We have a big seven at Turtle that you can give your data to these not-for-profits. And number two for us is educational access.
Michael Kennedy (30:28):
Yeah, that's cool.
Jason Rigby (30:30):
Yeah. When we look at what you're doing, and we look at the rest of the globe and we look at this ability to be able to take these children and have them learn, just as we teach them French, we teach Spanish, all this stuff, this should be just a required learning in every-
Alexander McCaig (30:47):
Teach them lizard tongue, Python.
Michael Kennedy (30:47):
Alexander McCaig (30:47):
Jason Rigby (30:56):
We're past our time here, but can you tell us a little bit about what's the future of your podcast and then do you have any plans or is there anything, or are you just going to continue like you have been, just being that hero? What's going on next with you?
Michael Kennedy (31:14):
Well, just trying to broaden all of that stuff. So I have the Talk Python to Me podcast. I have another podcast called Python Bites actually. The Talk Python is about these evergreen, deep stories, right? Like the philosopher story doesn't change that much over two years. It's still interesting, but what happened this week or what you got to get on is totally different.
Michael Kennedy (31:34):
So the other one's more like a newsletter, so I'm trying to expand the way that we get information out there, and co-host that other one with Brian Okken. And then also we're trying to bring more people in, on the live side of things, so do more live streaming on YouTube and places like that and try to get the guest, the host... Sorry, the viewers to participate with the hosts and the guests on the show to sort of make it a little more the community has a say in the show and not just listening.
Alexander McCaig (32:03):
I love that.
Michael Kennedy (32:04):
Yeah. And then finally I'm creating a whole bunch of courses for educating Python developers, right? Like Python podcasts and stuff like that. They're inspirational but they're not actionable. People could listen to this interview and they're like, "Oh, I love this stuff, data is so interesting." It doesn't help you really get any further and being able to do it in the day-to-day. So I'm also working on that side of things, trying to actually help people like, "Here's something you can do for five hours. Be able to do more after that."
Alexander McCaig (32:37):
Well, I think striking that balance is important. And I think you found an equilibrium with Talk Python to Me and all the content you're delivering.
Michael Kennedy (32:45):
Thanks. I really appreciate it.
Alexander McCaig (32:46):
Yeah, thank you very much for coming on the show. We're here to support you in your efforts and we look forward to being in touch with you again soon.
Michael Kennedy (32:52):
Yeah. It's been great to be on the show. Real honor. Thank you guys for inviting me.
Speaker 1 (32:59):
Thank you for listening to TARTLE Cast with your hosts, Alexander McCaig and Jason Rigby, where humanities steps into the future and source data defines the path. What's your data worth?