Welcome to episode #159 of the Super Data Science Podcast. Here we go!
With all the hype we’re hearing today about data science – data scientists taking big roles in growing industries, it’s hard not to pass on the possibility of a career in data science. On today’s episode, Mike Taveirne will share his experiences on his journey getting into data science.
About Mike Taveirne
Mike Taveirne spent the past years working with consulting and database software industries. He is recently taking a big step to enter and grow his skills in data science, specifically in the Data Warehousing and Data Analysis space. He is also a high-performance driving and travel enthusiast.
It’s the 159th episode of Super Data Science Podcast! This will be very enticing for people who are skeptical on jumping to data science. I got the right person here to convince you all about this. Mike Taveirne, a consultant and database administrator, talks about his story on why he decided to pursue his interest in data science.
I met Mike in Dojo Bali, a co-working place near the Echo Beach in Indonesia. We had a chit-chat and I was really surprised that he’s studying data science through online courses. The odds! From there, I decided to get him in the show and talk about his exploration.
To start the episode, we talked about his work background and the classes he’s taking to get him exposed to the field. There’s a lot going on for Mike which is absolutely outstanding. He was a sales engineer for IBM, a database administrator for Netezza, a dashboard developer for Allstate among others. He talked about few of his interests in Kaggle, Python, and YOLO. He integrates them into his passions and interests. He’s very passionate in helping to alleviate poverty and lending loans through Kiva. Aside from this, he’s very enthusiastic about performance driving and thrifty traveling.
We also got ourselves familiar again with the five stages of data science process. When dealing with various industries, learn why the first stage is crucial to the success of a project. Also, take note of the tips we’ve exchanged about transitioning to data science on this episode. We’ll discuss upwork.com, a known platform for freelancing data scientists, newbies or not.
Believe us, there’s nothing to be worried about in making this leap. There are so many resources that are very cost-efficient since they can be available online. The couple of hours we allot for learning should be a good investment. There are many of you out there that are not even aware that you have already started walking through your learning path towards data science. Just ‘playing’ with it because of curiosity is a very good start. Knowing the mathematics or the other stuff behind some data science ideas is another step you could choose to or not take.
Well, better click that play button to know more about this!
In this episode you will learn:
- Mike Taveirne gives a background of his professional career and tells on why he chooses to jump onto data science. (04:10)
- How Mike uses his knowledge in kernels and python in working on data to help Kiva. (09:00)
- Mike talks about Kaggle Challenge and his third kernel project, the Kiva Poverty Targeting. (12:43)
- Mike shares his future plans. (21:40)
- Your hobby could motivate you to be engrossed in a new field. For Mike, it was his interest in YOLO. (23:51)
- The five stages of the data science process. (33:57)
- Fear is a normal occurrence when jumping onto new stuff. (39:48)
- Discover the possibilities of freelancing as a data scientist. (41:00)
- For people who are able to work remotely, it’s easy to enjoy the benefits of exploring the world and interests. (44:40)
- Tips for people who want to transition to data science. (56:40)
Items mentioned in this podcast:
Mike Taveirne: This is episode number 159 with aspiring data scientist, Mike Taveirne.
Kirill Eremenko: Welcome to the Super Data Science Podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today, and now, let's make the complex simple.
Welcome back to the Super Data Science Podcast. Ladies and gentlemen, very excited to have you on the show. Today, we've got Mike Taveirne on the episode, and it was very interesting encounter. I actually met Mike by accident here in Bali a few days ago in the coworking space called The Dojo in Canggu. In Bali, there's really two great coworking spaces, which are very highly rated in the whole of southeast Asia. One is called Hubud in Ubud. The other one is called The Dojo in Canggu. I've been to both, and I actually like The Dojo a bit more. It's very social. You can approach anybody. You can talk to anyone. They have social events. It's really a great place to be, so if you're every in Bali, make sure to check out Canggu. It's a beach city. You can go surfing here, and you can do yoga, and also they have the Dojo where I just love hanging out all the time.
And so one of the people that I met through one of their events where people could just get together and talk about what they're doing was Mike. It was really funny because we had to ... we're sitting all at a table and we had what to say what we were doing, and Mike was, I think the first one to step, and we said that he's actually studying data science. And then yeah, I came up to him and talked, so this is how this podcast came to be. So he's not actually taking any of our courses, in the super data sense, he's studying through a course on [inaudible 00:02:17] on Udacity right now, as you'll learn from the podcast, and also through some of his other work. But I thought it was very interesting to get his perspective, into data science and especially in the case where I had not known him, before I knew nothing, about his journey, his career.
And it was very interesting to discover and I invite you to jump on this journey together with me and see how, somebody who's brand new to data science is going about this. So Mike is coming into data science, from a developer slash database administrator background, and this podcast, you'll find out what he's taking on first, what programming language he's learning, how he's going about machine learning, he already jumped into Kaggle competitions, some other things that he's doing, computer vision and how much easier it is, than it actually seemed at the start.
Also, I'll share a few tips with Mike, from what I've seen, about how people get into data science and you'll hear his comments on that, and at the end, he'll share some of his tips as well. So, it's a very exciting podcast, filled with quite a lot of interesting plot twists, and passion, different passions that come up unexpectedly, stay tuned for that and without further a due, I bring to you, inspiring data science, Mike Taveirne.
Welcome ladies and gentlemen to the super data science podcast, I've got an exciting guest Mike here, Mike welcome to the show.
Mike Taveirne: Thanks, good to be here.
Kirill Eremenko: So, mike is ... very exciting to me, because we've just literally met, two days ago right? We're in Bali, at The Dojo, in Canggu. And accidentally, ran into each other, so Mike's studying data science, right? So what's your profession now, and why are you deciding to move into data science?
Mike Taveirne: So, my profession or my back round's, basically in business intelligence, data warehousing, consulting. I had a prior job as a sales engineer, for a very large database system, Natisa, and then I joined a customer in the US, and I became the Natisa database administrator, sis admin, it's kind of like a lead developer for when people are working on it, and they had tough questions, or how to make things a lot faster, it's a really powerful database, so I really helped people get the most out of it.
Kirill Eremenko: Yeah.
Mike Taveirne: Yeah and then I got into data scientists, or data science, it's a hot, new, interesting field. I have got some friends that went to a data science company, I'm worried my skills are getting a little stale, by just doing the same kind of, data warehousing things, over and over, so I'd like to learn a lot more, and that's when I started looking for a course to take myself.
Kirill Eremenko: Nice and that you brought Bali? What are you doing in Bali?
Mike Taveirne: Yeah, so, one of the pluses at the job, was they allowed people to work remotely in the US, and I kind of negotiated to be able work remotely from anywhere, which most of the time, I did work in the US, but, once the holidays were over, and it's cold, I'm from Chicago, I don't want to shovel snow anymore, that's when I'd leave, so generally, January through May, this is my third year, just leaving the country, escaping, second time in Bali.
Kirill Eremenko: So just to clarify, you're still with Natisa?
Mike Taveirne: No, I was a sales engineer at IBM, and I sold Natisa, I was at US bank, I'm no longer with the bank, the area of the company I was in, they missed their financial numbers last year, and in January, my position got eliminated, so I've been exploring other options since then. Don't necessarily want to jump into the same kind of Natisa position somewhere else, so I'm seeing what other interesting stuff I can do.
Kirill Eremenko: Okay cool. So right now, that's why you're studying data science and you can hang out in Bali? Do some surfing.
Mike Taveirne: Yeah, I never changed my itinerary at all, based on this, but I do have more free time, to explore things. One of those things is potentially pursuing something in data science, it's cool, I want to see how things work anyway, but then I also have some friends at Data Robot. I know that's a real hot place, it would be cool to be there, I've got an interview next week, with a crypto currency exchange, that's pretty big. Maybe some of this stuff could come into play there. I'm sure they've got interesting problems, where it could help. Primarily, that wouldn't be the title of that role. I'm still interested in learning the tech. The course I did end taking, is one that was marked as, heaving a long introduction to Python, I've never coded in Python before, that's how I ended up choosing that one and I'm sure Python will be used in probably any other path I take.
Kirill Eremenko: Nice and where are you taking this course?
Mike Taveirne: This one's on Udacity.
Kirill Eremenko: Udacity, okay cool. Well that's exciting. Python's a great language to start with, it's quite simple, is this your first programming language?
Mike Taveirne: I've got a degree in computer science, but the reality is, I don't do too much. I mean maybe a little scripting here and there, for some other things, a little PHP, for web stuff, but primary my professional back round, it's almost all SQL or procedural SQL. So, more true programming kind of things, are a little rusty, cleaning up the data, in that world, usually it's done through SQL or some kind of ETL tool, whereas, in Python, there's a lot more programming type of work, to clean up the dirty stuff.
Kirill Eremenko: Yeah, that's true. And it's just a nice language. How are you finding it?
Mike Taveirne: Yeah I like it so far. I got to think about some things a little bit differently. I like that, anything I do, I can Google. And there's a usually stack overflow, telling me exactly what I want to know. So, it's great, my skills have really improved since I've found Kaggle and starting doing some, basically, in the course, I got to the point where, we're beyond the basics. And we're starting to graph some data. And the homework exercises were all about becoming more familiar with that. I'm a key valender, I was familiar with Kaggle, I haven't done it anything on it, with before, but they had a Kaggle data set, for about a month, when I stumbled on it, over there. And it was pretty much the same thing.
Kirill Eremenko: That's the one we were talking about just now, yeah?
Mike Taveirne: Yeah and they had a few different tiers of prizes. The first one was based on colonels and write ups and votes. And was pretty much, exploring the data, and visualizing it, right? So it's just like my homework, only it's a topic I find really interesting. I've actually lent out, $25,000 on Keyva and-
Kirill Eremenko: What is Keyva, tell us-
Mike Taveirne: So yeah so Keyva is a micro finance platform, it's for crowdfunding loans to poor countries, somebody's in Cambodia, they want a $1000, to buy some pigs, and raise those pigs, so on the platform, you can think of it as, a bunch of people all put in their contributions, typically, it's about $25, you had your risk, if you had $100, you put it across four loans, and then once the loans, funded, conceptually, this is the easy way to think of it. Once the loans funded, the loans dispersed, then your repayments are tied to this person. The default rate is actually really low, it's about one point five percent. That's really prime credit, most places, so most of your capital, you're able to take it back and out, or you can lend it someone else. And that's my default rate, too, as well.
After that $25,000 dollars, only one point five percent is what I've lost.
Kirill Eremenko: Or is it no interest?
Mike Taveirne: So the field partner has an interest rate, you're kind of capitalizing the loan, you don't get any interest back, the field partner, they do, Keyva, works to make sure these guys are charging competitive market rates, and are better than some of the other sources of credit, that people may have. Right? So it's better than going to the local strongman money lender in town, he's going to give you a little bit tougher rates, and if you can't pay him back, it's probably gonna more of a problem.
Kirill Eremenko: Yeah, it's like, what's the incentive? Why would you ever lend anybody money through Keyva?
Mike Taveirne: So politically, I'm libertarian, I really like the idea, it's generally, entrepreneurs, most of these.
Kirill Eremenko: Just to help out people.
Mike Taveirne: Yeah, so I like the idea of helping people grow out of poverty. And if you do some research, there's a lot of stuff that says, that helping people grow their way out, capitalism, stronger, market are, what really helps lift a lot of people out of poverty.
Kirill Eremenko: Yeah, okay, I guess it's probably better than, maybe in some cases, than just giving money? You lend money and then they feel like they have to work and do something?
Mike Taveirne: Yeah, I mean one of the things I like from a social aspect, is there's lending groups. So in these groups, there might be five people, and one person's borrowing for the pigs. But the other people, there's some social pressure there, some social assistance too, if somebody's having some trouble, they can help out, 'cause they're in this group and they generally take out loans, as the group. The other concept of giving, it kind of depends on what it's for. So loans are good to solve some problems, bad for others, right? If somebody has just food insecurity, no food, you don't want to finance your purchase of food, with that. That's a situation, where, it would be better give 'em food. If you actually look at some of the research around giving these, particularly products, there's a concept called "dead aid."
Where you actually harm the local populous, by giving them some of these products. So when you buy these shoes, from, I think it's Tom's, and they donate a pair of shoes there, that actually hurts the local economy, because you put the local Cobbler out of business, that guy. That guy would have typically gone to him to makes those shoes, but now they're just given, so. If you go to an area, and you give a lot of, finished products that like, it's actually pretty problematic, versus if they were just given straight money, whether it is lent or given, that generally has a better outcome.
Kirill Eremenko: Okay, and so that's cool, so what's this Kaggle challenge?
Mike Taveirne: So the Kaggle challenge is with the Keyva data, they've got a set of loan's there, and they've got some poverty data around these loans, and it's to say, how are we doing with the data we have? What data can you add to enrich the data we do have? So we better understand how we're targeting these people. And they have some data that's at the national level, and then some data that's at the next region below it level. One of the data sets they use, is the multi poverty index. It indexes things such as, level of education and health. Excess to markets, and it's ... finance is a part of that too, but since Keyva only has the financial lever to pull, there's some other data sets that could be interesting, more meaningful, especially if you get them at granular level.
So they're really looking to say, what interesting stuff can all these data scientists bring to us, from the world. Sets they find all over, to merge into this, and help us target these people, even better for aid.
Kirill Eremenko: What's the prize for challenge?
Mike Taveirne: So there's a few different prize tracks. One of them was on the colonel. And I think this is a little different than most Kaggle competitions, where you're just hyper tuning an algorithm to predict something, so one set of prizes was for the colonels, so you're doing a write up here, analyzing the data, your stuffs interesting-
Kirill Eremenko: Well what's the colonel?
Mike Taveirne: Colonel's like a ... Jupiter notepad. You can do it in Python and it's just a write up in a story of-
Kirill Eremenko: Oh so, okay.
Mike Taveirne: ... yeah here's what the data looks like, and here's something I brought in and here's something interesting that I found. Or here's an outlier, let's go investigate it, see what's going on here. So from that approach, it was a little bit of a popularity contest I'd say too, 'cause you definitely wanted to have your colonel in, to actually win this. Some lessons learned for me. You want to be in here, have it published. Ideally you get some friends that are following you, you're following them, because they'll give you the votes. And the whole first part of the contest was vote based. Also, they were awarded prizes for contributing a data set, that a lot of people used. So if you're the first to ad some consumption data, that you've found for a lot of Africans countries, where this is a problem, and challenge, then a lot of colonels would have also used that data, so the top three, I think, data sets, were also awarded prizes.
And then the final prize track, is directly from Keyva. They're going to take a look at the colonels and choose the one that ... chose the set of five, that they like the best.
Kirill Eremenko: Okay, but they haven't announced the value of the prize?
Mike Taveirne: So the prizes are, I think all the prior ones were, $1000 dollars each. The last set, I believe it's, 6-4-1-1-1, for the top five prizes.
Kirill Eremenko: Six thousand, four thousand, okay, gotcha. And so you just got into data science and you jumped into this already?
Mike Taveirne: Like I mentioned, I really like the topic, it's really interesting to me, just to explore the data, and it was better than my homework. I didn't expect that I would be in any prize track, in reality I think my first bit, if we look at that as the exploratory data analysis, that acronyms very common on there. I think I did a pretty good job of it, but I got a new account, no friends, and started in late, so I got a decent amount of votes, but not enough to put me up in the prize track. I was still in the top chunk, where some guys were tracking it, but it didn't hit up there. My data sets, I ended up adding up a lot of geospatial stuff, that's a thing too, it was interesting, because the first part of this, it was really on a lot of expiatory data analysis.
And then the actual poverty part, or trying to model, or show something different, area of the contest. Like different people are in there, and different people are active. I brought in some geospatial data. And it was my first time playing with that. But I got some pretty good use out of that. So I'm not sure what methodology that Keyva used already, to place loan positions, on their lat and long, but they had some data there. It had already been discovered by a lot of people, that it was pretty off, like countries away for some of that data, they also had ... the region, where they specified someone was in, would also be off. So these are regions where they did have that multi dimensional, poverty index. But they think they're in the city, and they're really somewhere rural or the opposite.
So they really had the wrong number there. So using some geospatial data, I first did it with the Philippines, which has, their pretty large number of loans there too, on Keyva. So it took a while for this. But I got some geospatial data, with the regions, where the poverty index, had values, and then was able to take the loans, those pandas, geo data frames, and take the polygons for the regions, take the points for the loans, and say "Are these loans in the region?" And assign them to the correct regions. And then assign the poverty index values, more correctly. And then I was also able to find some other data, around consumption for these regions or even smaller regions, for some countries. And this where my data got a lot more granular and a lot more accurate, at looking at the problems that Keyva can actually solve. That are financial, in relation, and then granular to actually say "This small area is better or worse, than the small area next to it."
Kirill Eremenko: That's pretty cool. Sounds like a big chunk of the work was, in hands the data preparation, where you-
Mike Taveirne: Yeah, it was a lot of learning and a lot of interesting things to go along with that. And I really liked how things have turned out. One of my colonels kind of got corrupted or broken, I couldn't edit it anymore, so it kinda broke apart, into three parts, where my first colonel is pretty much exploratory, data analysis on that one. The second colonel, I was hoping that one get fixed, so I kind deviated from the original mission, somebody else had uploaded a lot of data, about the lenders, and I'm a lender, I think this is interesting, so I did a colonel, just exploring, the lenders. And I would find things like, where do most people live in the US? How active are they? Who are the ones who make really big contributions?
Those were the surprising things. I wasn't surprised that there were a fair amount of people, who just try it once. I was surprised that some of the whales were as big as they are, so the top guy on there, who's really still much larger than the second, who's also big, but the top guy on there, had over 88,000 loans, this is millions of dollars, at a minimum.
Kirill Eremenko: Wait, $88,000 dollars of loans?
Mike Taveirne: 88,000 loans with a minium of $25 dollars, each.
Kirill Eremenko: Wow.
Mike Taveirne: There were also some people I found, who were huge contributors, to the point where, there's one, $9000 dollar loan, and this person funded the entire thing. So it was interesting to see, how many of these, kind of, Keyva whales were on there, too. And what their behavior was like.
Kirill Eremenko: That's so cool, it's like, you're doing a project, you're up scaling a self and data science, building a portfolio yourself and you're learning something, about something you're passionate about.
Mike Taveirne: Yeah, it really worked out, all pretty well. All that data is public on their site, although it's not as usually, easy to see. I actually chat with a couple guys, on LinkedIn, we have interesting conversations around that too, they were pretty interested in this stuff, kind of where they stood, among the whales, and one of the suggestions I have for Keyva, is trying gameaify the system a little more, make some public leader boards, you can do it for, who lends the most in the Philippines, or to women, or for agriculture, you know. Whatever kind of leader boards you want, and these guys will actually probably compete with each other and lend more as a result.
Kirill Eremenko: I really hope Keyva is listening. And if they are, then Mike is your next data scientist. They definitely should hire you, you're so passionate about this stuff.
Mike Taveirne: Yeah, it's really great. I got lucky that was the data set, I got lucky with the timing, and what I'm working on, that this all worked out, in an awesome way.
Kirill Eremenko: Yeah, that's so cool. And has that changed your perspective of Keyva, are you going to keep lending there?
Mike Taveirne: Yeah I actually took a break. I haven't lent on there for a little while. I lent a little more, just to get up to my round number of 25,000 there. But I've got some capital in there, that's from repaid loans, I'll probably add some more in and keep going and go around the world with it.
Kirill Eremenko: That's really cool. Is that Kaggle competition over?
Mike Taveirne: May 15th, is the deadline for the submissions.
Kirill Eremenko: Okay, and so what's your plan after that?
Mike Taveirne: After that, I'm hoping the crypto exchange interview, I've got next week works out, business for a data engineer, seems like a really hot place to be. When I think of data engineer, I don't just think of the traditional, data warehouse. An ETL developer background, that's primarily my back round, but something that is exploring, more unstructured data, using more Python to model and transform that data, and get it ready for, probably additional science kind of efforts after that. And it just really seems like a hot interesting place to be. So I'm hoping that one works out right now.
Kirill Eremenko: And is that what it said in the job description, that it does that aspect? That you're after?
Mike Taveirne: It's data engineering. They mentioned a few different scripting languages in there. Or coding languages actually, too. Java, C++, python was in there, but speaking to the recruiter, it also sounded like this deck is pretty traditional now, with some kind of SQL database, and so, if it's something where they're adding more data, or we're there maturing into more data scientist space, I think that would be great for me. 'Cause that would work out well with my existing skill sets, and the stuff I'd like to learn.
Kirill Eremenko: Is a big company?
Mike Taveirne: Yeah, they're pretty big. Right now, they're the 12th largest crypto exchange. Yeah so they're doing well.
Kirill Eremenko: Okay, cool, well good luck. Hopefully that goes well.
Mike Taveirne: Thanks.
Kirill Eremenko: All right so, and in terms of skills and education, so you're doing this Python course, how long is it and how much have you done of it already?
Mike Taveirne: It's fairly long. I think I've done around maybe a third so far. I've got to get back to it now. I've been focusing on the Keyva thing, so I haven't been doing 'em at the same thing, I think everything will be a lot easier, once I get back to it now. Now I'm eager to get on, and do my first models, and things in it, so that'll be exciting.
Kirill Eremenko: How long? How many hours?
Mike Taveirne: You know, I don't know at the top of my head. I'll guess it's ... probably about 40 hours.
Kirill Eremenko: 40 hours?
Mike Taveirne: Yeah.
Kirill Eremenko: Okay, fair enough. All right and so once you smash through that course, what tool or skill is next on your list?
Mike Taveirne: You know what, I don't know. I'll have to see what's interesting out there. I played a little bit with this thing called Dark Flow.
Kirill Eremenko: I don't know.
Mike Taveirne: I think it's Dark Net, I believe it's written in C++, and it's some kind of neural network, for recognizing images, and it uses ... as academics named it I think, YOLO.
Kirill Eremenko: Oh YOLO?
Mike Taveirne: Yeah, You Only Look Once, right? I actually have a video, walking around Dojo of it, going on stuff. And Dark Flow, is somebody, brought it over into, Tensor Flow, on Python. So I just had the environment, I had this thing I could download, still had to go through. I did the get [inaudible 00:24:36], still had five or six errors to Google, but everybody had already 'em, so got through that, and then was able to run it. I thought I'd have to train in on some things first, but you can just download the existing weights and it's good to go. So I was gonna try and train it to recognize cats, and then I've a video where I walk around, Istanbul, for about 20 minutes, and ... this, one of my imitations on the video, is try to count how many cats are on it, 'cause there's cats everywhere, on Istanbul.
Kirill Eremenko: Really?
Mike Taveirne: Yeah it's a cool place, I like it a lot. But if you go out for coffee, you're gonna see like five cats on your way there. So it's an interesting video, [crosstalk 00:25:12]. Yeah even more though, cats are a big thing, in Istanbul. And it's my favorite city to walk around, so I highly recommend it, if you haven't visited it yet. It's a great place to go.
Kirill Eremenko: Yeah, wow, okay. I haven't been, I think I should go some time. And so you have this video, and recognize all the cats?
Mike Taveirne: Yeah, it's a recognize a couple of cats around Dojo, the dog, chairs, it's grabbing everything, right? So I didn't know how it worked, before I started playing with it. I thought I'd have to train it a little bit, and then I could run it. Instead, I was just able to download everything. It's good to go. And it's interesting, to take the video and just walk around, see what it gets. You can adjust the threshold of when it actually recognizes something. You could just attach it your webcam, so you can hold things up real time, pick up your phone, it's pretty good at recognizing cell phone, sometimes it thinks it's a remote. There's a funky pillow on there, and I held up the pillow next to me, and it circled me, and it says "person." But the pillow thought it was griffe wearing a tie, it was a two version of YOLO, too. The new one I believe is much better.
But it's not ported over to the Tensor Flow, with that, so it's not something I've played with yet. So I was considering trying to play around with the C++ version, at, when in reality, after this course, we'll see where things stand with the new job, but I'll probably look for something else, interesting to do, along those lines, actually learning how to, model something, seems to be the real hot solid skill, and the biggest challenge, and it would be really cool to learn that stuff.
Kirill Eremenko: I like your approach, that ... even though you don't consider that you're studying ... computer vision or deep learning, but you're playing around with it, and through that, you're probably learning, if not, more, at least, the same amount as you would, if you were delicately studying, what it is all about. So and just because, when you play around with stuff, time flies and you're like "Now you know how to use YOLO." Right, and you don't even consider that were actually studying it, it's really cool, it shows that you're really into the field, right? That you want to get to the success that you're aiming for. And that's inspiring.
Mike Taveirne: If there's a problem, I know there's "Oh, these algorithms work great for this or that, but you still gotta try these and play with the weights and all that stuff, I don't necessarily want to learn the math or all the academics behind it, I want to know how to use it. And maybe my lift as a result, is only 25 percent. And somebody with a PHD, can get to 35. But I want to be able to get my hands in there, make something, get that 25, and then "Hey, maybe I can hand it off, to the PHD." That's kind of my goal. I don't need to be the PHD super ninja, but I wanna have the skills to make something, and be at least a little bit dangerous.
Kirill Eremenko: Yeah and my favorite analogy there, I've mentioned a couple times in the podcast already, is driving a car, right? By [inaudible 00:28:19] cars and so Mike, by the way does, drag racing, or is it-
Mike Taveirne: High performance drivers education, HBD events, is what they call 'em.
Kirill Eremenko: All right, okay. So you're into cars, right?
Mike Taveirne: Yeah.
Kirill Eremenko: You probably, in that case, this rule wouldn't apply to you, 'cause you probably know how they work inside, and things like that. But for me, I get into a car, it's about knowing how to drive it, from A to B. I don't really care the difference between a crankshaft and a camshaft. I don't care how it works inside or I don't know, I don't need to know. I need to know how to use it. Same thing with these algorithms. You don't need to know, mathematics behind, you can, if you want to push the boundaries of ... research. And come up with new stuff. Or really dig into the things. But in honor ... the level of applying, of applied level, you just need to know how to write the code, and get the results. You don't need to know the mathematics behind it, you don't need to know all the details, you need know the the intuition, where the petrol goes in the car and which of the pedals is brake, which one's gas.
But there's a limited of number of things you need to grasp, in order to apply it efficiently and get the job done. And so that's really cool approach, a lot of people get frightened by the fact and "Whoa, deep learning is so difficult." Or "Artificial intelligence, I would have to do a PHD in this." You don't. How long did it take you to apply YOLO, from the point where you didn't know it, to the point where you had it working on your computer?
Mike Taveirne: It went way faster than I expected. Really, it was just a couple of hours.
Kirill Eremenko: Couple of hours. Not even of couple of days, let alone couple of months, right? PHD takes five years. It could you a couple of hours to apply YOLO, and again, even in your case, how long were you expecting?
Mike Taveirne: At least a weekend.
Kirill Eremenko: At least a weekend, and so, that's just stands to show, how ... easy things have gotten, this day in age, right? With the power we have, with these computers and all these tools. Tensor flow, and Karis, and stuff like that, things are just getting simpler and the fear factor keeps a lot of data scientists out, away, which shouldn't. What is the ... we see, that was a good example of you're fearless. What is a fear that you have?
Mike Taveirne: I have a comment on some of this simplicity stuff, I don't think about my fears too often.
Kirill Eremenko: Yeah, that's good.
Mike Taveirne: I have some friends over at Data Robot, and that's kind of my whole understanding of their philosophy, is just helping regular people, like business annalists, get more dangerous with this stuff. Don't be afraid of it, and the way their software works, is you feed it the data, so you can feet it the titan survival data from Kaggle, and just stay this is more target, whether somebody survived or dies, and ... hit go. And it'll try all these algorithms, it'll try different weights. It might create ensembles, and at the end, it's got something that, pretty quickly you made and you can use, and then you tweak it some more, if you've got ideas, how to change things, but ... it makes you pretty dangerous, right away. I don't know if that's a software I can try out on cloud or anything, I've tried out some databases recently on the cloud just to play with them and learn them.
But that's another piece of technology I'd like to toy around with, just see what I can do. After I do make my first model, or try playing with something on [inaudible 00:31:49] there's a credit card fraud data set now, I'll take this course, go through that, and maybe this will be my good next steps, I'll finish my course and then I'll say "Okay, I'll go to the credit card fraud model, see what I can do with that." And then see okay, well, what happens if I put it through Data Robot and just take an initial first stab. Does it beat me right away? Or does it take some work? Or does how do these things all turn over. And how quick is that gonna be? Productivity wise. Versus me trying to hammer it out myself as well.
Kirill Eremenko: Gotcha. It would be interesting, comparison, can you beat the robot?
Mike Taveirne: Yeah.
Kirill Eremenko: That's so cool. I think a lot of ... the main advantage would be the domain knowledge, or your understanding. With QI, you have knowledge way beyond what is even supplied in the data set, or in the descriptive files. Because you worked with them before.
Mike Taveirne: Yeah.
Kirill Eremenko: And you would know intrinsic things that nobody would ever put into Data Robot, just because they wouldn't even think about it. So I think that's a advantage that humans have for now, over companies like Data Robot. But I think Data Robot's doing pretty great job, I heard that they're flat out, really busy.
Mike Taveirne: Yeah, it looks to be pretty amazing product. And it seems that domain knowledge would help you come in and do some of the feature engineering, or other steps. It certainly helped me during the Keyva competition on Kaggle as well, 'cause there's people who aren't really too sure how it works, and it really shows in some of that work. And I tried to come and help people out, and street 'em into the right direction, for how the system actually works. Even knowing that it's crowd funded loans, people didn't necessarily get, when they were building things or kind of predicting things that wouldn't necessarily be worth while. If you understood how the system worked, you wouldn't be going down those paths. So does that seem to be the strong human elements, still gonna be needed there for how the world really works.
Kirill Eremenko: Okay, that's a good point. I wanted to shift gears a little bit and ask you this question. So there's many ways to identify the life cycle of a data science project and I've seen a couple online. The way I identify it is, it has five stages. Stage one is asking the question. Understanding what you're actually after. On Kaggle, it's done for you. They tell you what you're after. But when you are a data scientist, you need to go up to your stakeholders and find out, okay, let's identify the questions, put it in writing, so we're all on the same page, there's no scope-creep, and so on. Big step. Step two, is the data preparation. Step three is ... data modeling. And getting the algorithm up and running. Step four, visualizing your results.
And step five is presenting. The step five is the one people often forget, that after you've prepared the data, modeled, visualized, you still have to convey the insights. And data preparation takes up about 70 or 80 percent of the time. And data presentation takes up another 80 percent, on top of everything that you've done. So my question to you would be, in this framework, in the understanding that's, my understanding also, a lot of our students follow this concept of the ... five stages of the data science process. Sounds like you're very passionate and excited about the data modeling and surprisingly, the data preparation. Which you did a lot, in the [inaudible 00:35:18] project. What would you say your current stance is, on the other three stages? The identifying the problem. The visualization, and the presentation. And how and if you are thinking of developing skills in those areas.
Mike Taveirne: Yeah so I like the visualization, and presentation parts. I've read some Steven Few books, I really like those. I've made some dashboards in my BI background, there's been a lot of visualization tools, [inaudible 00:35:47] and such out there. So I generally like that, unless I'm having a challenge for, and I could see this come up in data science a little more often, if something's kind of multiple detentions, in a real complicated thing to present, having to elevate my visualization game, to be able to convey that story. But I still like that challenge. It's fun and interesting, right? You're working with visual cues then, so I've always found that's pretty interesting. I was a little frustrated with Python, initially, because I had made staked bar charts incorrectly, with I think Seaborne for, I don't know, probably two days before I noticed that I had to actually draw the bar, do some math, to draw the second bar, on top of the first one.
And I thought I saw a tweet, where the guy just didn't like bar charts, so it wasn't part of that, I don't know, something like that, that or maybe I just need to get some better Python stacked bar chart skills. Nonetheless-
Kirill Eremenko: Or just use a different library. Seaborne's pretty good though, maybe there's something that can do better?
Mike Taveirne: Yeah and I started playing Plotly a little, and it's cool, you can hover on things, and see things there. But I really like that are, I'm pretty comfortable there, the presentation, if I feel good about what I found and how I drew it, I got no problem going up and saying these are things I found, I was a sales engineer previously, so pretty comfortable, even though I'm still a core super geek, I'm pretty comfortable going through, if I'm confident about the work, no problem delivering it. The initial part I think is really hard and it sounds like an area where some companies or teams don't even ... narrowly define the scope of what question we're gonna do and kinda just end up fishing around, and not producing a lot, and maybe a fair amount of unsuccessful projects, come as a result.
So depending on where I am, and what kind of domain knowledge I have, that's still probably gonna be the toughest part for me. Here's a bunch of data, well what can we do with it, or what might we be able to do. Ideally, it would be something where, I could go learn what other people have done it and try to recreate or increment on that, for something better. But to me that would probably be the biggest challenge.
Kirill Eremenko: Yeah that's a good point and I heard a data scientist, who was this ... I forgot at which company, one of the head data scientists one of the online companies we often use, he said that, the worst situation, is when somebody comes to him and says "Here's some data, give me some advice."
Mike Taveirne: Give me something cool, yeah.
Kirill Eremenko: He's like "Come on, tell us what's the problem. You gotta identity what is the business problem? What is the challenge? What is the opportunity for disruption, opportunity for improvement, efficiency and things like that, to look into it." And you're right, it's one of the biggest challenges people do like a lot of Kaggle competitions. Or even for me, I was working in consulting and [inaudible 00:38:49]. Everything is already done by, in Kaggle, the people who upload the project. Or in consulting the partners of the business, or the project managers, or the people who sell the job, and so all you get is a file, which says what needs to be done, what data you have, and so the first step is very often, especially also in these courses, when you take a course, you're already told what you need to do. What the homework is, right?
And so a lot of the time, people skip this very fir step, and through their training, there just not used to it and then they get into the real world, and then all of a sudden, you gotta identify this. You gotta put it in writing. You gotta tell people that this is going to take this much time, and then say no when they want extra work, and things like that. A lot people aren't used to it. But yeah, okay, [inaudible 00:39:43] about that. So you still haven't told me a fear. What's your biggest fear right now, going into data science?
Mike Taveirne: I mean I don't really have a lot of fear about-
Kirill Eremenko: Just confidence?
Mike Taveirne: ... yeah, I'm not gonna be skydiving out of a helicopter, or anything, but I'll skydive in a data science anything. I don't think it's a big deal for me to go back into ... a whole lotta traditional jobs. Looking for another unicorn I can ride, where I can work remotely, from potentially anywhere, so that would a huge plus. And then I'd want to do interesting and challenging work, and then get paid pretty well for it. All three of those together, is a challenge, but just going back to Chicago, find a local job, consulting, that would be no problem. Doing Natisa DVA stuff, not a big deal either, I just haven't resolved to wanting to, do something more traditional like that, foot on the back round.
Kirill Eremenko: Fair enough, and have you looked at freelance work?
Mike Taveirne: No, not so much. It seems pretty tough to sell my skillset, freelance. I don't know how it is for maybe a straight data scientist, but doing DVA work, especially business intelligence, they seem pretty adamant to wanting to do there, in the office, in front of 'em, so ... I have skills. I'm more of a niche database with Natisa, so I could definitely find contracting work, but it's ... always been on sight, anything I've ever done.
Kirill Eremenko: But I mean like data science, have you heard of upwork.com?
Mike Taveirne: I've heard of Upwork. I kind of figure ... I might not be competitive against guys on there. I figure if I go to Upwork right now, they'll be a ... guy in India, with a PHD who will be much more than productive than me, and probably a cheaper rate than I had searched for.
Kirill Eremenko: All right. We haven't identified any fears, but we identified a false belief. That's a false belief, that's limiting you. What if somebody in India ... just go and put up your profile and see what happens. The reason is, even if there is somebody in India, or anywhere else, this thing is, it's also about, not just about supplies, but demand. And on Upwork, demand for data science is growing really fast. And people ... need a lot of different areas of data science, you can position yourself as a data scientist, with a lot of expertise in databases and data preparation now, after that project. Or you could put up a profile of a ... computer vision YOLO expert, if you go broad, yes, there will always be an expert that's better than you. And mostly likely they'll get the job.
But if you niche, if you're like "YOLO expert, with data science expertise." Maybe somebody's looking for that.
Mike Taveirne: That's a really good point, 'cause yeah, maybe that guy is very busy and there is a huge demand, and there's not enough people. Definitely does seem like something I should explore.
Kirill Eremenko: For instance, at Super Day of Science, who would have thought, that going on ... let's say some person from England, right? Going on Upwork, like Ben, he worked for us [inaudible 00:43:06]. Going on Upwork, that he would get a job, not in Tabloh, as he posted, but in supporting students of a Tabloh course. Life happens super randomly sometimes. Yet, that's what happened. We were looking for a person, with Tabloh expertise, but not to do a project, but support our students. And we're like "Oh, let's go on Upwork checking." All right, here we go, Ben. And he ended up supporting our course for sometime. And yeah so, you post a job, on I don't know, Computer Vision, and you'll end up getting a job, I don't know, maybe teaching someone, or doing a master workshop in Bali or something like that. So you never know.
One of the reasons I like Upwork, because there's so many different people, it's a huge marketplace and ... yeah, I think you could stand out there.
Mike Taveirne: Yeah, that's a good idea. I think I got profile, but I haven't logged in even in forever, I'll go refresh it pretty soon, yeah.
Kirill Eremenko: Yeah, that's cool. All right so that's freelance work. Great, interesting. How are you enjoying Bali so far?
Mike Taveirne: It's pretty good. I mean it's way nicer than Chicago. My friends are shoveling snow. I'll just send 'em more pictures of the beach and everything so it's pretty good. I've also been on the road now for about four months. I got car stuff going on, the week I get back so, I'm headed back this Friday. I'm eager to get back home and back to my friends and back to racing season, too. Or track season, I'll say.
Kirill Eremenko: That's cool, and Mike mentioned before, that he travels with his helmet. Where did you get do some track days?
Mike Taveirne: So the Philippines is the easiest place to get on a track. So there, they have a race series called Vios Cup. It's an Asian market only car, for a while, Toyota was subsidizing intro classes. They're trying to sell you a turnkey race car. So we've got a couple of classes and they go to the call to action to sell you the race car. The first class, $100 dollars US, and you get to go out there, at [inaudible 00:45:14] grade three track, pretty real deal racetrack, and you do some drills. You're in a gutted car. You've got a racing suit on, if you didn't bring your helmet, you give you a helmet. These are the same cars that Pilipino celebrities race, when actually do the racing series.
So they got plenty of regular folks doing it, and they've got five male, five female celebrities there racing as well. So you're racing their cars, and you do these drills. There's handling drills, braking drills, acceleration drills, and then eventually do some laps on the circuit as well, and it's a [inaudible 00:45:47], just try and keep up with the instructor. As long as you're doing well, they keep pushing it. Some friends that are work there, so it was a real fun time. There's another racetrack there as well, in the south we went to. So that first one, we were at Clark International Speedway, there's one in the south called Batonga's Raceway. And this one, my buddies there, I drive a Subaru BRZ at home, so it's the BRZFRS, 86, Philippines club that I'm hanging out, and when those guys want to schedule track dates, it's pretty easy there. It's a lot less formal than in the US. It's a pretty big deal for us to do it.
Pretty casual for them to go out and rent a parking lot or rent a racetrack for a day. So we went out that one, couple different guys let me drive their cars, super grateful for, right? That take a lot of trust. It's an older track, the walls are closer, they're all concrete, they're not tires. But yeah, I had a great time there. That all went smoothly. Much easier to get on as cart tracks, so I've been on a couple cart tracks in the Philippines. I cart on every cart track in Thailand, in 23 days, that was my, 13 tracks in 23 days, so that was my challenge. And also cart on every track in [inaudible 00:47:00], and then anywhere else I go, I'll try and figure out a way to get on a race track, or at least a cart track.
Kirill Eremenko: Wow, it sounds like you're living the life man. Just traveling, do what you're passionate about, working on what you're passionate about, and hobbies, the racing, that's so cool.
Mike Taveirne: Yeah, it's been pretty fun. Hope to keep it up so far. You can do the whole, credit card, mileage, gaming thing, if you're an American citizens, the banks are fighting over your business. So with the many banks and the many credit cards, I've been flying pretty cheap, on nice birds, for a long time now. So that's definitely anybody in the US, so look into game. Friday when I'm going home, I usually don't pay with first class with the points, but it was Café Pacific, they had the ability, so I have a first class suite.
Kirill Eremenko: No way.
Mike Taveirne: If you just look it up on Google flights, it's a $15,000 cash purchase, which I would never in a million years pay, but it was just a few more American Airline miles and I have a ... I've been doing this for a few years now, well five years I've been flying around on just airline miles, and it was just a few more, they had the spot, so I'm like "Yep, I'm gonna take it and try it out."
Kirill Eremenko: So much cash did you pay for it?
Mike Taveirne: 80 dollars.
Kirill Eremenko: 80 dollars?
Mike Taveirne: That includes the flight out to the Philippines, which is business class, on A and A.
Kirill Eremenko: Oh my god. That's so cool, I've heard of that before but I didn't know it was that cool, it's first class.
Mike Taveirne: Yeah, it's not even a big deal, with one credit card, you can go to Europe and back in economy. If you did two, I have friends, worry stuff will harm their credit rating, all this stuff. I don't get one or two credit cards at a time, I'll apply for ten, I'll get approved for eight, now I'll jump through the hoops to get all the miles. I do it an extreme way, but doing it an extreme way, has meant that, for five years, I've flown around the world, all in business class or better, and that's about what I've paid, $80 dollars in taxes, for a round trip ticket.
Kirill Eremenko: That's crazy.
Mike Taveirne: So it really works well and I'm shocked, more people don't actually do it.
Kirill Eremenko: So you get a credit card, and they have these special promotions, if get our credit card, we'll give you this many miles, if you do this and this.
Mike Taveirne: Yeah and uniquely in the US you can get a lot more, so if you're in Germany and you get the Lufthansa card, they'll probably give you a 10,000 miles. If I'm in the US and I get the Chase Lufthansa card, on a normal day, they'll give me 50,000 on a promo. I think that one only goes up to 60,000. Not my favorite miles, but just a good example of showing, even for other counties, kind of major airline, in the US, you can easily get a lot more points and it's pretty much no brainer, very little effort just to get even a roundtrip ticket to Europe.
Kirill Eremenko: That's crazy and then you close that credit card?
Mike Taveirne: Yeah I'll keep it up for a year, there's a couple that'll be my go to ones, that I keep using, but most of 'em, year comes up, they have an annual fee, I don't plan to keep 'em, so I just close it before the annual fee fits. And they still give you new ones.
Kirill Eremenko: If this was an interview, hypothetically, if I was an employer, or a recruiter, even that alone, for me, would indicate that you have that type of mind, that you find ways to get things done. Or shortcuts, efficiency. That's efficiency, right?
Mike Taveirne: I'm a huge optimization guy. So the way I would accrue miles on there, it would seem a little bit obsessive compulsive to some people I think. I climbed up, in about a year, I got a million chase miles, chase points, which you can transfer to United miles. And then that's just how I am, if I get something, and it's interesting, and there's a good reward in it, I go at it hard. So yeah, I'm all about optimization and efficiency, for the things I really get into and really like.
Kirill Eremenko: Yeah that's pretty cool. Oh I wanted to ask you with the racing, did you ever think of using your racing data to do some analytics on it?
Mike Taveirne: It's not anything I'm going to throw on Python or anything, but I do have some data analysis. I've got a few different blogs. The ones that my car blog, is called Point Me By. Pointmebuy.com and that's based on our passing rule. So any kind of competition my friends and I have will be time and a tech thing, just with lap times. But at the HBD's, you're not allowed to just pass someone. It's permission passing, in safe zones. So if someone's coming up behind me, and I see 'em, I'm gonna let 'em pass on the straight, I'll point out to the left of the car, or over the car to the right, saying pass me over here. I'm gonna stay where I am, you're safe to go there.
So that's the name of the site and I've got somethings on there, where I do have data from myself and my friends, and I'm able to analyze it. So we get 10 hertz, GPS data, that gives us position. Most of the data is gonna be based off the position and velocity, 'cause we can graph that on, just a line chart and see where I break, and where my buddy breaks. And maybe his is steeper, he's decelerating faster. Maybe that's because of our brake set ups, maybe he's just pushing the pedal a little harder. Same thing with acceleration, we can see if somebody's on 93 octane fuel or 85 ethnol, typically you'll see that car will accelerate a little bit better. We can also see what are the top speeds we're getting on the straight, how are taking these turns? Maybe the turns the toughest part.
That takes the most skill, that's the scariest part. Maybe I'm going through same tires as a buddy, similar suspension, so in theory, we could both take the turn at say 70 miles an hour, but I'm doing it at 65 and he's doing it at 70, so I can use that data to say "Well I'm leaving some time on the table there." So I got a couple of write ups there, with some of the devices we use. And one of 'em has four of us driving and just different color, and turn by turn analysis, here's where we were, and here's what we were doing, and hey, it looks like my buddy already is a much better driver than the rest of us.
Kirill Eremenko: That's very interesting and so those are just devices you put in the car and there's a device that stays in the ground, that?
Mike Taveirne: So there's two kind of devices. One is a GPS only one, and that's going to give you accelerometer data and speeds. There's another one that's GPS and OBD-2, so that one will actually give you lat long. So the first one they could, I don't know why they don't give you that. But the second one does. So with the second one, you can actually draw your position, on a Google Map. But also the OBD-2 part, goes into the car's computer, and then you can get a whole ton of information. So you can get the steering angle, you can get the speed of each tire, you can get the oil temperature, so lots of interesting stuff in there. So that's when I first, found out "Oh, my oils getting really pretty hot here. I should run an oil cooler on the car." So there's a lot of good stuff in there, which most of the time I'll use for drawing on my video, just 'cause it makes it look a little cooler, so my YouTube video, I'll have a dashboard on there, and I'll have RPM's on it, along with speed and position.
And my best lap times, current lap times, it all comes out pretty cool.
Kirill Eremenko: That's so cool and is the GPS accurate?
Mike Taveirne: Yeah, that's one ten hertz GPS. Pretty accurate, if you do draw it on a map, it's pretty solid, it's right there. I do have some carting video with similar stuff, but using older GPS solutions either a phone or a one hertz action cam, and at one hertz, it's pretty choppy, especially if it's carting, 'cause the course is small, so you're in different positions very quickly, so it's real jagged looking, if you're on a longer, track, I've been on the Auz Marina, in Abu Dubai, a couple of times. They have some really cool car rental programs, you can do there. Where you can drive crazy cars. Like the formula 3000, and the thing there, is nobody knew how fast they were going. They say the cars go to 150, I don't think anybody's going to 150.
But if you look on trip advisor, that's where everybody says they're doing. But I had the camera there, until I brought that and was able to draw everything, but then also give my real data from it. And I didn't go over 120. And I was the lead behind the instructor. But the newer cameras, you can actually get much better data from, so I think the newest Go Pro is ten hertz or more, it might even be up to 20 hertz. Well I don't know if it's as accurate with its sensor, but you're gonna get better stuff than I have on my old stuff.
Kirill Eremenko: Cool, very interesting hobby. Looks like you're really into it.
Mike Taveirne: Yeah, it's a lot of fun.
Kirill Eremenko: So we're coming to the end of the podcast. Before we finish up, what's the best way for people to find you? Maybe there's inspiring data scientists that would love to learn from you, follow your career, or maybe there's people that are interested to maybe get some advice from you on their projects, or Kaggle competitions and things like that?
Mike Taveirne: Sure, the blog I made most recently is called "Do you even data.com?" If you look for me on Kaggle you'll find it at "Do you even data." And on my blog, I'll have a ... contact there. Otherwise my email address works, which is
[email protected] and I can give you the spelling of that.
Kirill Eremenko: Yeah, we'll put it in the show notes. And what about LinkedIn? Are you on LinkedIn?
Mike Taveirne: Yeah it's Mikedove10, on LinkedIn.
Kirill Eremenko: Mikedove10. All right, so we'll put the links in the show notes as well for that. Probably final question for you, what's your one tip of advice that you could give, people who are starting out into data science, or transitioning into data science and don't know where to start, and have fears, or have worries or overwhelmed and things like that. What would you say to them?
Mike Taveirne: I'd really say just find a course you like and then sit down and start taking it. It will probably go a lot smoother and quicker, then you think. So just jump into, start doing it. It'd be really beneficial if like me, you happened to find something that's about a topic you're passionate about and then you can jump into that, and it just makes doing everything so much more easy. The initial start would be the biggest hump to overcome and really, there's no reason not to start doing it. It's an interesting field and there's a lot of room for growth there and not enough people.
Kirill Eremenko: Love it. So basically, just take the first step. Take that leap to get into it. Thanks man.
Mike Taveirne: Thanks.
Kirill Eremenko: All right, see ya later.
Mike Taveirne: Thanks for having me.
Kirill Eremenko: So there you have it, that was Mike Taveirne. An inspiring data scientist, who is looking to get into the space of data science. I would actually even go beyond just saying that he's looking to get into the space, I think he's already in the space. And that's the beauty of data science that ... as long as you're passionate about it, as long as you actually enjoy what you're doing, and you're excited about it, you might think that you're still getting into it, but you're already in there. You heard him talk about computer vision and how Mike think that's he's just playing around with computer Vision. I think running a YOLO algorithm, on some video that you shot at a co working space, through the tech chairs and cats and pillows, I think that's already pretty cool. And I think you can really apply that, in different projects and create a portfolio of ... if not, real world project, then at least Kaggle projects, or test projects. Or showcase that for yourself.
And that's exactly what he's doing. So there's an example of somebody who is, as we found out, fearless and is just diving straight into it. And taking the first step. And I really appreciated his advice at the end, to simply take the first step and get on with it, and not be afraid, it's actually much easier than it seems. So make sure to connect with Mike, you can get all the show notes for this episode, at superdatascience.com/159. Where you'll find the transcript for the episode, along with all of the blogs that Mike mentioned and his LinkedIn URL, where you can connect with him and stay in touch, or ask him any questions you might have about his career or any other thing that he mentioned. Especially how he got into Kaggle, I think that was a very interesting story. As well, if you are looking for maybe a freelance or consultant, Mike might be somebody that could fit your position.
And also, I wanted to say that, if you enjoy this podcast, and you know somebody who's been wanting to get into the space of data science, but is kind of afraid, or has some doubts or some limited beliefs, then forward it onto them. You might help that person, take the first step, and as you saw, taking that first step, is the most crucial part. It is the most important part and everything else, you sort it out, or they'll sort it out along the way. And I hope you enjoyed today's episode, I look forward seeing you back here next time. And until then, happy analysing.