Kirill Eremenko: This is episode number 209 with aspiring data scientist, Rio Branham.
Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. Each week, we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today. Now, let’s make the complex simple.
Kirill Eremenko: Welcome back to the Super Data Science podcast, ladies and gentlemen. I’m very excited to have you on this show. Today, we have one of those ultra inspiring episodes. Rio Branham, he is an aspiring data scientist who just got his first full-time job as a data scientist literally a few weeks ago. In this podcast, you will hear lots and lots of passion. First of all, Rio has listened to all of the episodes of the SuperDataScience Podcast. Now, he’s finally on the show himself. He’s moved from not being in the space of data science one-and-a-half years ago to actually having a job on the space and giving back to the community, inspiring others as well.
Kirill Eremenko: You’ll find out quite a lot of interesting things, for instance what the difference between data science and econometrics is, what kind of tools and techniques Rio uses, his aspirations. We’ll also recap on some of the talks that he attended at DataScienceGo because that’s where we met with Rio and he told me his story. Then, once he got his job, I was very excited to invite him to the show.
Kirill Eremenko: Why this podcast is very inspirational? Because of the mindset, because of Rio’s mindset and how he approaches things, approaches his learnings. You’ll hear about how in a weekend, he’ll learn about productization of data science and how he actually set up a little experiment for himself. You will get some ideas on how you can do that, too.
Kirill Eremenko: You will hear how Rio actually declined a job offer in data science because it was of a company that he wasn’t passionate about their mission. That, I think, in itself is a very inspiring story.
Kirill Eremenko: You will also hear Rio make a reckless commitment, just like Rico Meinl and Ben Taylor suggest, Rio made a reckless commitment right on this podcast. Make sure to listen to it so you can hold him accountable. He’s going to do something incredible in 2019 and made that commitment publicly here on the show.
Kirill Eremenko: Lots of exciting things coming up in this episode. Can’t wait for you to check it out. Without further ado, I bring to you aspiring data scientist Rio Branham.
Kirill Eremenko: Welcome to the Super Data Science Podcast, ladies and gentlemen. Today, we’ve got a very special guest on the show, Rio Branham calling in from Utah. Rio, how are you going today?
Rio Branham: I am doing really well, Kirill. How are you?
Kirill Eremenko: Very, very well indeed, as well. Rio, it was really cool meeting you at DataScienceGo. That’s the place where we caught up and thank you for coming up and saying, “Hi.” A huge pleasure. Then, I found out more about the things that you do. You just got your first job in data science. Congratulations on that. We’d love to talk more about that on this podcast. How are you feeling in general after this event, the DataScienceGo 2018?
Rio Branham: I’m feeling really good. A lot of things have happened in my career since then and things are just really moving in the right direction. Getting this job and things like that weren’t a direct result of that conference, but I feel like the motivation that I got from the conference and notes that I took really have helped me stay motivated and really working towards some of my goals. It’s been great since the conference.
Kirill Eremenko: Fantastic, fantastic, man. Thanks, and great to hear that. Also, it was really cool just now, just before the podcast, we were chatting and you mentioned that you’ve listen to every single episode of the SuperDataScience podcast. Hats off to you. That is so cool. That’s inspiring for me to hear. How does it feel to now be on the podcast, having listened to all of them, all 200 plus episodes, how does it feel to be on the episode now yourself?
Rio Branham: It’s real exciting. Honestly, if you went back and told me when I first heard about your podcast that I would be on your podcast, I wouldn’t believe you, because, really, your podcast has been with me the whole way as I’ve been learning about data science. I think I barely even knew the term data science when I first found your podcast.
Rio Branham: I’ve really been listening to it for a long time. It’s helped me connect with a lot of people. Obviously, DataScienceGo, I would not have been in touch with that if there wasn’t a podcast and learning about new technologies, new tools and things like that. It’s really been helpful in moving me along in my career. It’s pretty exciting to be able to share how it’s been helpful to me and to the other listeners of the podcast.
Kirill Eremenko: Wonderful. How long ago was that when you said that you haven’t been in that space for that long and now you already have your first job? Just so that our listeners can engage the timeline, how long ago was it when you started that into data science?
Rio Branham: Sure. Probably a little less than two years ago.
Kirill Eremenko: Mm-hmm (affirmative). That’s about how long the podcast has been around, just …
Rio Branham: Yeah. I think you probably had maybe, I don’t know, a few podcast. Maybe it was a year and a half ago, but it was pretty early on.
Kirill Eremenko: Mm-hmm (affirmative). Okay. Got you, got you. What would you say has been the biggest value that you’ve gotten from the podcast? You mentioned a couple of things. What would you say is the one thing that’s helped you most in your starting out into data science as a career?
Rio Branham: I would say just getting involved with the community. It’s really the gateway for me because I love listening to podcast in general, but just the gateway into people or technologies or terminologies, really just bringing me … It was a way for me to enter into the data science community. That’s been great and that’s really gotten me where I am today.
Kirill Eremenko: Got you, got you. Okay.
Kirill Eremenko: On that note, let’s get started. Tell us a bit about who Rio Branham is. If somebody on the street were to just stop you and say like, “Who are you and what do you do?” What would you say?
Rio Branham: First, I’d say I’m a data scientist for OODA Health, which is a health care technology startup.
Kirill Eremenko: That’s your first job, right? That’s your brand new …
Rio Branham: That’s my first full-time data scientist position. I did just finish up with a data science internship for a few months before that at a company called Instructure. It was great stepping stone as well, but I actually also just recently graduated in April with my undergraduate degree in economics.
Kirill Eremenko: Congratulations. Congratulations. Lots of things have happened for you this year.
Rio Branham: Yes. It’s been a big year.
Kirill Eremenko: That’s awesome. That’s awesome, man.
Kirill Eremenko: Sorry I interrupted you there. You first would tell them that you’re a data scientist, you’re a proud data scientist at OODA Health, which is a health startup in South Lake City. What else would you say?
Rio Branham: I’m a big rock climber. I like to get outside. I like to hike and rock climb. I also like to snow board. Lots of fun, outdoorsy things to do here in Utah. Yeah. I like to participate in all of that.
Kirill Eremenko: Man, that’s so cool. I was just in the Yosemite with Paulo from DataScienceGo. We were there. Man, it’s so much fun hiking. I never realized it. It’s like you can spend two hours, you can spend 11 hours on a hike and have a lot of fun.
Rio Branham: Yeah. I heard that you were out there in Yosemite. I listened to your podcast. I was jealous I hadn’t made it out there yet.
Kirill Eremenko: Yeah, man. It’s so close to you. You should definitely go. It’s so much fun.
Kirill Eremenko: All right. Okay. Yeah. That’s a quick intro to who you are. Tell us a bit about, like you did this internship, but how did you get there in the first place? What made you one-and-a-half years ago consider a career in data science? Why did you get attracted to this space?
Rio Branham: Sure. Like I said, I just graduated with my degree in economics, which is really what I was interested in, really from my first semester at college. I knew that I wanted to study economics. I liked mathematics. I figured it would be helpful getting me into business, but I didn’t quite know what I wanted to do with it. I just figured it would be a versatile degree to get.
Rio Branham: As I went along, I figured, maybe I want to do a PhD in economics. I was really interested in doing research in developing countries. I wanted to do some sort of humanitarian work as a career, but maybe use research as a way to get involved with that. I was a research assistant for an economics professor for about a year at my university and started to, a big part of economics is econometrics, which is statistical side of economics, how to do statistical research, linear aggression, logistic aggression. That’s a natural overlap with the field of data science, although I didn’t know it at the time.
Rio Branham: But as a research assistant, I was getting familiar with a program called Stata, which is proprietary statistical software package. It’s not a programming language, but it got me into working with data. I was doing a lot of cleaning data sets and merging data sets and running some of the regressions for some of the research projects that we were working on. I realized that I loved working with the data and that probably doing the actual research and going for the PhD program isn’t what was best for me.
Rio Branham: Then, I started looking for other career fields where I could use those data skills. That’s when I shifted that mindset and decided that I didn’t want to pursue an academic career, so then I quit that job and I found a job in industry.
Kirill Eremenko: Stumbled upon data science.
Rio Branham: Yup, I really did. Very lucky that I had a complimentary skill set already from economics.
Kirill Eremenko: Yeah, yeah. Yeah, that’s definitely an advantage, but how did you encounter it? Did you meet somebody? Did you read a book? What is due to the podcast? What was the first encounter where you were like, “Wow! Data science. That sounds pretty cool. I want to be in the sexiest profession of the 21st century.”
Rio Branham: Yeah. I definitely didn’t come across that until later, but good question.
Rio Branham: In my program, they had an alumni mentorship program so you could sign up for it. Then, they would match you up with alumni from the economics program at our university in different career fields.
Rio Branham: I did that a few times. That was someone who worked for a research institution out in California. I was paired up with someone who did economics consulting, which I considered doing for a while. I just wanted to get exposure to a lot of different people and a lot of different fields.
Rio Branham: Then, I got matched up with a guy named Randall Lewis. I got to look up his specific title, but I think he’s an economist at Netflix, but basically he’s doing data science. He’s using econometrics and data science at Netflix so started talking to him. That’s where I think really when I started to develop this idea that I could leverage the skills that I had and get into that field of data science. I guess that’s really where it started. Then, I started doing research on my own. That’s probably when I came across the podcast and started doing some of my research on my own in the actual field of data science.
Kirill Eremenko: Okay. Got you. Wow! What a random coincidence. You get matched up with somebody who’s already in the space. Then, you realize that, wow, this is something that you can do. Life is full of these random moments. What do you think you’d be doing if you hadn’t been matched up with Randall Lewis from Netflix? Do you think you’d eventually get into data science anyway?
Rio Branham: I think so. I think it was probably inevitable because I think I was looking for data science without knowing that data science existed. I wanted to work with data and the research side of econometrics that I was doing, using data to make predictions and inference and solve problems. That’s just the definition of data science. I think I would have stumbled across it at some point.
Kirill Eremenko: Got you. Got you. How would you say data science is different to econometrics and what you studied in your degree?
Rio Branham: Sure. Econometrics focuses mainly on inference. Rather than prediction, trying to get the most accurate prediction, we’re really trying to find strong correlations between things. That’s why they use mainly logistic and linear regressions because those are very interpretable. They have coefficients where you can actually see what effect does this certain variable have on the outcome that we’re looking at.
Rio Branham: For example, income. How much does education affect your income? They’re not concerned about predicting your income. They want to know what factors make up income. That’s the research that they do. It just happens that those are similar techniques used in data science, but we’re generally a little more interested in the prediction side rather than the inference that we can gain from those same methods.
Kirill Eremenko: Very cool. I never heard that described that way, but I think it sums it well. Econometrics is predominantly focused on inference whereas data science takes it a step further and talks about prediction. You find that you enjoy the prediction part, not just the inference?
Rio Branham: Yeah. It’s just a different field. It provides different value. I think both are useful. Yeah.
Kirill Eremenko: Yeah, for sure. Definitely, both are useful. Okay. That’s very cool. You’re into data science. How is this first position? You’ve been there not that long at the health startup, only a month or so. How are you feeling about that? Is data science what you imagined it to be? I know you did the internship, but now being a full-time data scientist, how does it feel?
Rio Branham: Yeah, it’s great. It’s going to be different, every company and every industry, but so far, it’s been real exciting because, since it’s a startup, we don’t have a ton of people. I’m really on the ground floor which is real exciting. I’m able to really be involved with really important decisions in building out our product. Yeah, just important decisions for the business. That’s real exciting to me, so that was one of the things I was looking for was somewhere like I could feel like I was having an impact on the company, where the work I was doing as a data scientist would actually be valued. OODA Health is great. They’re really focused on using data science to make sure that they’re ahead of the game and they’re making the best product possible. It’s really exciting. I love that.
Kirill Eremenko: Mm-hmm (affirmative). That’s really cool. What’s the product?
Rio Branham: What they’re trying to do is improve the payment system in the health care industry. It’s really complicated right now between, there’s the hospital and then there’s the insurance companies and then there’s the patients. Often times, the patient doesn’t know how much they’re going to be paying for any sort of health care. They get a bill. Sometimes it takes a few weeks to get a bill. They’re not sure if they need to pay the insurance company or the hospital or the doctor’s office. It’s just not a very clean process.
Kirill Eremenko: Yeah, man. I’ve been there.
Kirill Eremenko: Sorry to interrupt. I was traveling in the US a few years ago. I was scuba diving. Then, I had a small accident. I had to go to hospital for it. There’s a $5,000 bill that my insurance was meant to cover. They said they would. Then, I went back home. It’s still has been two years. They still are talking to the hospital about paying it. The hospital’s messaging me. The insurance company is messaging me. I’m messaging them. It’s such a nightmare. Yeah. Very, very feel the pain. Feel the pain on what’s going on.
Rio Branham: Yeah. That is exactly what we’re trying to get rid of. We’re trying to make it more like a retail experience. You know exactly how much you’re going to pay and everybody gets paid immediately, the long-term goal is to really fix that industry. There’s a lot of research saying that there’s a lot of waste in the health care administration in this whole process of going back and forth between the different entities. You just trying to figure that all out. It’s just wasteful of people’s time and a lot of resources and so we want to try and streamline that.
Kirill Eremenko: That is a really exciting implication. I’m very pumped that you’re working at the startup. Really great place to start your career.
Kirill Eremenko: Do you feel like you actually can chance a whole industry, like you’re a data scientist at a business and the work you’re doing can impact people and organizations and make everything much better. How do you feel about that?
Rio Branham: Yeah. That’s one of the reasons I decided to work here. I had another job offer somewhere else. I decided to turn it down because I was not passionate about what their mission was as a company.
Rio Branham: With this company, I really felt it was something that was necessary and was really going to help consumers in the United States. I feel like that’s real important for me is to work for a company where I feel like we can really make an impact.
Rio Branham: I was really impressed with the founding team of this company. A lot of them worked together at another health care startup. They’re actually based in San Francisco, but they’re also building out their engineering team here in Salt Lake City. It seems like they’re very streamlined in the way that they’re approaching things. They have a lot of great talent in the health care industry and the technology industry.
Rio Branham: Usama Fayyad is our chief technical officer. In my mind, he’s a bit of a legend. He’s published a lot in the field of data mining. He helped start the KDD Conferences, the Knowledge Discovery and Data Mining Conferences. It really feels like they’re really embracing data science as company. No individual person can make the whole effect of the industry, but I think as a team, we really have a strong team and we’re going to be able to make some real impact.
Kirill Eremenko: Interesting. I totally agree with that.
Kirill Eremenko: I’m going to ask you a very provocative question. It’s very exciting to hear that you had multiple job offers and you were able to pick the one that you’re truly passionate about, but let’s rewind time a little bit and let’s imagine you don’t have the second job offer, that you just have the one job offer from the company where you’re not passionate about their mission. Would you take it?
Rio Branham: That’s a great question. At DataScienceGo, actually, I was talking to Sinan Ozdemir. I was telling him about this struggle I was having because, at the time, I had the job offer from the other company that I was not passionate about. I did not have the job offer from this company. I had started the interview process with OODA Health, but I had not gotten an offer from them yet. I was struggling with that exact question because it’s hard to say, “No,” to a job offer when I’m looking for a job. I was talking him and I was like, “I really think I’m going to have to turn it down because I don’t think that I’ll be happy working there and I don’t think I’d be a good foot for their team.”
Rio Branham: He supported me. He’s like, “I really admire that. That’s really inspiring.” I think I was going to turn it down anyway, but just having that support from him and a few other people that I talked to really helped me make the decision that I don’t want to waste anybody’s time. I don’t want to waste my time. I don’t want to waste the company’s time. Even though it was scary and I didn’t know when the next job offer would come around, I did decide to turn it down before I had another job offer.
Kirill Eremenko: Man! I really admire you for that. That is one of the most inspiring things I’ve heard in this podcast. I hope listeners are paying attention to this that you had a job offer in your hands and you didn’t know if you were going to get another one from the other company, but you weren’t passionate about it and you turned it down. Huge respect for that. I think already that decision paid off. You’re much happier now with this position that you have.
Rio Branham: Absolutely.
Kirill Eremenko: Yeah. Good on Sinan for supporting you on that. That’s a part of community, my friend. That’s the power of having somebody to turn to as a mentor or for some advice and getting their opinion and getting verification from somebody more experienced that you are on the right track, that you avoided making a huge mistake that might sit you back for a couple of years or months in your career, right?
Rio Branham: Yeah.
Kirill Eremenko: Yeah, man. Okay. That’s really cool. You’re in this path, in this career now and all the best. These guys are super lucky to have you. If anybody from OODA Health is listening, then Rio is a very, very passionate person. Somebody who’s listened to 200 plus episodes of the podcast, man! You got to have it in you to sit through all of that. For all of my blabbering on this show, you got to have it in you.
Kirill Eremenko: Tell us, in addition to all of that, you’re also actively building your brand online. You’re building or you’re giving back to the community, which are synonymous in this field. It’s about you build a brand through giving back, through sharing your experiences. You wrote this article, My Path Into Data Science Thus Far, and you mentioned a couple of people that have helped you. You mentioned me as well and the SuperDataScience podcast, thank you. Thank you for that. I really appreciate it.
Kirill Eremenko: Tell me what pushes you to do that? You could have been happy that you have the job and plow away and build a career in data science. Why are you doing this? Why are you writing articles? Why did you agree to come on the podcast? What is powering you in that sense?
Rio Branham: Yeah. I listened to your podcast with Randy Lao, who I also was able to talk to at DataScienceGo. His story’s pretty amazing. We’re just sharing his story on LinkedIn. He’s got a ton of followers now. He’s really a great influencer in that space. I had that on the back of my mind. I was thinking how that’d be cool if I could start sharing some things, but I didn’t really know if I had anything to share really. I was trying to just engage with people, comment on things.
Rio Branham: Then, I had someone reach out to me and say that they had been watching my career path. I didn’t have this job yet. I was still in my internship. He just asked me for some tips on how to break into the data science field. I was taken aback. I didn’t know that I had anything really exciting to share with him, but I ended up writing a pretty long message back. Since I had just recently listened to your podcast with Randy, I was like, “You know what? I might as well just make this into something I post on LinkedIn,” just basically a list of all of the things that I’ve done so far to help me get into data science. I just decided to do that because I had that inspiration. I knew that I wanted to be more involved with the community.
Rio Branham: I took those things that I wrote to that person and put them into an article. Basically just spend a few hours and threw it all together. I probably didn’t take as much time as I should have in editing it and all that, but I just was real excited to put something out there and see how well it was received.
Rio Branham: It’s gotten more activity recently. A few people like yourself have commented on it. I think that’s probably got a little bit more people to see it, but at first nobody saw it. It got maybe one or two likes. I was really bummed. It was like, “Oh, man!” I was really tempted to take it down, but it seems like there’s a few people that have reached out to me since who have appreciated it. I don’t remember who, but I think there’s been a few people at podcast who’ve said then, “You know, even if you help one person, that’s really worth it.”
Rio Branham: To me, it’s real exciting to know that just that one simple thing that I did because it felt really simple to me might help a few other people that’s really exciting. I really like that. I wouldn’t be where I’m at if it wasn’t for other people who were willing to share and be part of the community so I want to do the same regardless of how [inaudible 00:26:31] in the industry.
Kirill Eremenko: Mmm (affirmative). That need to contribute, right?
Rio Branham: Yeah.
Kirill Eremenko: It’s very interesting because I was chatting to my brother today, actually. Went to the beach here in Gold Coast. We were talking about what’s … An interesting question, like what makes a person plant an orchard or some … Yeah, for instance, like orchard of certain plants or fruit or something where they’re going to get results, but they won’t see that in their lifetime. What makes somebody go and plant an orchard that they will never see grow to the level, to its full potential in their lifetime? It only during his lifetime.
Kirill Eremenko: I think one of the things is that when you see others do that, like when your parents did it for you or the generation before you planted an orchard that you were getting benefits from makes you want to contribute and makes you want to give back to the community as well, even though you won’t be able to reap the benefits of that yourself. In this case, you’ve got some value from other people in the community so inevitably, you have this desire to create value for others even though it’s nothing to do with your own career. It’s nothing to do with yourself. It’s very selfless thing to do. It’s taking your time. It’s taking your effort, but at the same time, it’s helping others. I think that’s where it comes from. What would you say?
Rio Branham: I definitely agree. I mentioned earlier that I was interested in doing research in developing countries. I actually was able to sit down with Tarry Singh during the DataScienceGo and talk to him a little bit about his AI philanthropy and how he tries to help startups in other countries, start to implement data science and things like that. I had a good chat with him because, I don’t know, I think Pablos made a really good point, Pablos Holman, at the conference and said that, “You kind of won the lottery being in a developed country, having a good education. Like, that’s really, that makes you way more advanced and have a lot more opportunity than a lot of other people in the world.” I guess I’ve just always had that mindset where I don’t feel like I deserve a lot of the things that I have. Everything’s just happened by winning the lottery, being born in the United States and having a good education, having parents to help me grow and learn. I just feel like I’ve already had that helpful mindset where I don’t feel like I can really be fulfilled unless I’m doing something to help other people because I feel like I don’t deserve to have all the success and things that I have, not that I’m super successful, but just being in the place that I am, I didn’t deserve that.
Rio Branham: So, other people also don’t deserve when they’re not given the same opportunities. I want to do whatever I can whether that’s through helping people professionally, get into data science or whether that’s eventually doing work in helping bring data science to developing countries to help them progress. Whatever it is, I think that’s just something that I need to do to feel fulfilled.
Kirill Eremenko: Wow! Fantastic. Very admirable, man. That’s very inspiring thing to hear. I’m sure a lot of people will agree with you. A lot of people also think that way.
Kirill Eremenko: You mentioned a couple of times where, at DSGO, you mentioned you met Randy Lau, you listened to Pablos Holman’s note speech, you talked to Tarry Singh and a couple of other people. Let’s rewind the conference a bit. It’s been almost a month. Let’s start with what are your favorite talks? What is some of the favorite talks that you attended there?
Rio Branham: Pablos Holman was my favorite …
Kirill Eremenko: Very inspiring, right?
Rio Branham: Yeah. It really aligned with my values, talking about how we can use data science to really solve some really big problems. That was really inspiring.
Kirill Eremenko: Who was his video of the shooting down mosquitoes with lasers?
Rio Branham: Yeah. That was really something.
Kirill Eremenko: That was funny. Okay. Pablos Holman. Who else?
Rio Branham: That was great. Rico Meinl was great. His challenge to make a commitment and commit yourself to something, even if you don’t quite know how you’re going to do it. That was really great. I made the commitment to recommit to being involved with humanitarian organizations and really trying to revisit that passion that I have. After I made that commitment, I was able to talk to Tarry about it. We had a great conversation about that. Then, I met someone just networking, one of the nights who has a really similar interest and he’s actually been doing some work with some non-profit organizations. That just felt really cool that I was like, “Awesome! I made this commitment. I’m going to do it.”
Rio Branham: Then, automatically, two different opportunities came up for me to talk to people about that and build my network and starting to approach that. Rico was really inspiring as well.
Kirill Eremenko: Wonderful. Sounds like you were about to follow his foot paths because in 2017 in DataScienceGo, he was an attendee. Then he, a year later, now he’s a speaker because he made the commitment. He’s like, “Kirill, a year from now, I’ll be standing on that stage and inspiring other people.” Looks like he’s passing on the baton to you and it’s your turn, man, to, yeah, to radically … What is his term?
Rio Branham: Reckless.
Kirill Eremenko: Reckless commitment. Reckless commitment. What is your reckless commitment? I understand in terms of with helping developing countries and contributing there, but in terms of reckless commitment, do you have to make the whole ocean is to make a commitment that is public and that you cannot go back on, that you have to … You say, “I’m going to do this,” and then other people hold you accountable to it just because you already told others. You burn the bridge or you burn the boats. What is your radical commitment in that sense?
Rio Branham: I guess I haven’t really made one yet where I made a public commitment.
Kirill Eremenko: How about you make one right now?
Rio Branham: Yeah. I definitely need to do that.
Kirill Eremenko: I mean, like right now in the podcast?
Rio Branham: Okay. Yeah. That sounds good.
Kirill Eremenko: You want to help third-world countries do humanitarian effort somehow there?
Rio Branham: Yeah.
Kirill Eremenko: Yeah. Do you want to travel there? Is that your plan or you want to do some remote work for some organization, something like that? How are you envisioning it?
Rio Branham: I’d love to do both, but ultimately, I think I would really enjoy being able to travel and visit places and be with people and do that.
Kirill Eremenko: Okay. All right. This is my suggestion. No pressure, but how about you make a public commitment right now on the podcast that you will travel to a developing country in 2019 and whether it’s through data science or not, but you will help at least one person there.
Rio Branham: All right. That’s a great commitment. I love it.
Kirill Eremenko: I’m going to hold you accountable to it. I’ll ask you next year. All right?
Rio Branham: All right. Sounds …
Kirill Eremenko: All right.
Rio Branham: In 2019, here I come.
Kirill Eremenko: Okay. 10,000 in the next two weeks are going to hear this and 10,000 people are going to hold you accountable. You, my friend, Rio, you’re going to a third-world country, a developing world in 2019 to help at least one person. Commitment made. Congratulations.
Rio Branham: Thank you. Thank you for the support.
Kirill Eremenko: Rico’s going to be proud of you, my friend.
Kirill Eremenko: All right. Okay. Rico’s talks or Pablos Holman, Rico, you’re probably like nervous right now, sweating. You just made this public commitment [inaudible 00:34:21] of the podcast, but come back to us. What was another talk that you like?
Rio Branham: Gabriela de Queiroz was really inspiring as well.
Kirill Eremenko: With the deep learning hacks, yeah?
Rio Branham: Yeah.
Kirill Eremenko: How quickly you can apply deep learning.
Rio Branham: Yeah. That was really great. I haven’t really applied a lot of deep learning yet. That was another big takeaway, something that I really want to start looking into more because, I don’t know, I kind of have had this complex in my head where I feel like I have to do everything from scratch and I think that’s really valuable, but I also think I need to realize that sometimes efficiency is more important. If you can utilize other tools that are out there, then that’s something that can really be beneficial. I have not yet, but I’m planning on looking into the resources that she mentioned and trying to apply those because that was pretty impressive.
Kirill Eremenko: Mm-hmm, mm-hmm (affirmative). Got you. Okay. Yeah, I heard quite a few comments from Gabriela. She was actually also running a panel. Which panel did you attend? Did you attend the future of technology or the women in data science?
Rio Branham: The future of technology.
Kirill Eremenko: Okay, okay. Got you. What did you think of that panel?
Rio Branham: It was really great. Yeah. it was fun to see all you guys up there talking on about answering questions and talking about the future of data science. It was insightful.
Kirill Eremenko: Okay. Got you. Thanks. Pablos, Rico, Gabriela, chat a bit about the panel. All right. Those are some of [inaudible 00:35:48] talks. That’s really cool. What would you say was your, like a month from now, you got your first position in data science. Amazing. How are you applying the things that you learn? This goes out to all our listeners who are also at DataScienceGo. One of the things we said was, “All right. You learn a lot of things, but now make a commitment to apply at least one thing throughout your career and get the benefits of being here, of all the things that you learn.” How would you say you are applying some of the things that you learn and how are you planning on continuing doing that in the months to come?
Rio Branham: Sure. I took a lot of great notes and I’ve been reviewing them and figuring out different action items that I needed to complete. One of the other things that was great was Ben Taylor, who talked about how you really needed to have passion to be successful. He talked about having a passion project, something that you’re just passionate about and do on the side, that really can show other people that you’re spending your free time on it.
Rio Branham: That’s something that I’ve started on. I’ve started to think of some and I’ve started to poke around, playing with some data sets online that are interesting to me to see what kind of personal projects I can put together because that’s really motivating for me as well because work is great and I love my job, but sometimes you want to work on something that’s just for fun. I’ve really taken that to heart and I started …
Rio Branham: The point is to keep myself motivated, work on something fun, but also, if I want, I’ll practice some new techniques or some new frameworks or something, then I can apply that to a personal project on the side so I can be gaining some experience as well as keeping myself occupied and doing something fun.
Kirill Eremenko: Mm-hmm (affirmative). Okay. That’s really cool. What’s a fun project that you’re considering at the moment?
Rio Branham: I don’t have a well-defined project, , but I mentioned, I like rock climbing, I like hiking. There are a lot of websites where you can look up different hiking routes and different rock climbing routes. I’m thinking about trying to utilize some of that data on that from those websites. They have a lot of data about people who have gone on those hikes or done those rock climbs and see if I can come up with some cool visualizations. I haven’t totally defined what the project would be yet, but I’ve identified some of these sources where I might be able to get some fun data to play with.
Kirill Eremenko: That’s so cool. That’s so cool. I was just thinking as you’re talking that in Yosemite when we were there, one of the main pain points was like three different maps. It’s like, I don’t know how many hikes, maybe 50 different hikes you can do. There’s three different maps that I had to carry around.
Kirill Eremenko: The internet reception is almost nonexistent there so I had to carry around three different maps like paper maps. Then, look at one, look at another one, look at a third one to find the route. It’d be really cool if somebody created an interactive tool that you could maybe even download on your computer because the reception’s not great there or maybe they could have it as an iPad at Yosemite or something like that. They do have Wi-Fi so you could probably use it if you went online through their Wi-Fi.
Kirill Eremenko: But anyway, that you could have these routes, there’s a map and then you could just put your mouse over one and highlights. Then, you’re like, “Oh, you can go this way. You can go this way. During winter, this path is closed. During summer, this lake is, you know, dried out, whatever. You don’t want to do that.” Give some valuable tips. It would have been really helpful for me in terms of hiking. Just as an idea for you there. Something along those lines could be cool.
Rio Branham: Yeah. It’s a great idea.
Kirill Eremenko: Mm-hmm (affirmative). Okay. That’s a really interesting takeaway. I agree, Ben Taylor has some of the best talks in the house and this time he’s talking about passion. It’s good that you got that take away from him there.
Kirill Eremenko: On that note, I would like to shift gears a bit and talk about your tools and techniques, like you got your job as a data scientist. You’ve been listening to our podcast for a long time. Even though you don’t have 10 years or five years experience in the field, you already know what’s going on and I’m assuming you know what you want to focus on, at least in which direction you want to explore your career. What would you say are some of the top tools that you use in your work as a data scientist right now?
Rio Branham: Currently, Python is the biggest one. That’s where I do all my work. I actually started out learning R, but then transitioned to Python because some of my other co-workers were using it, but Python’s what I use right now to access data from our database and then to do all my modeling and visualization and exploration of the data and understanding what’s going on with the data. I would say, really, probably everything that I’m doing right now is in Python.
Kirill Eremenko: Mm-hmm (affirmative). Okay. Got you. Python’s a powerful tool. What kind of techniques do you use? For instance, models, is it logistic regression and decision trees, random forest, deep learning. What would you say are some of the most powerful tools on one hand that you have in your tool kit? On the other hand, that you’ve had a chance to use not necessarily at your current job because you just started, but maybe through your internship and what do you think employers find useful?
Rio Branham: Sure. Random Forest has been a really good one for me. It’s pretty powerful and pretty fast. It also provides some level of interpretability. We can get some importance measures for each of the different features that we’re included. That’s been a good one, but honestly, one of the things that I’ve realized is sometimes simplicity is better, especially for a company, sometimes, they need to understand what’s going on in your model so just a simple decision tree or logistic regression. It’s a little bit easier to explain what’s going on.
Rio Branham: Oftentimes, what I’ve noticed is sometimes, I’ll come in with an idea and be like, “Oh, maybe we could apply deep learning here.” Deep learning definitely has its place, but I think that sometimes, it’s not worth the computational effort on a problem that doesn’t need it. That’s what I come across so far through the internship mainly is where I’ve experienced that is I did a little bit of natural English processing projects there that was really fun. Used some basically in Naive Bayes is the really simplest way to apply that. That was really us good results and so we didn’t feel the need to move on to try and do some really complex deep learning models on our natural English processing. We also didn’t have massive amounts of data, which is when deep learning would be a little more useful. Yeah, decision trees, a random forest, Naive Bayes for natural English processing. Yeah.
Kirill Eremenko: Okay. Okay. Got you. Okay, mate. Thanks for the overview. I’m sure a lot of people find those interesting, how you went about natural language processing and the whole concept of doing what’s enough. I think that’s a valuable piece of advice. You’re right, sometimes, data scientist get carried away and just jump straight into deep learning or AI or something like that, shooting at pigeons with a cannon. That’s what we say in Russian metaphors. Yeah. It’s the 82 year old. You got to do … Sufficient is sufficient. Why do more when it requires more computational power, more time, and more effort when maybe a basic, but sometimes even a logistic regression is sufficient in comparison to a deep learning algorithm.
Kirill Eremenko: On that note, let’s do a rapid fire list of questions to get to know you a bit better. Are you ready for this?
Rio Branham: Yeah.
Kirill Eremenko: Okay. Cool. Who or what has had the most influence on you to become a data scientist that you are today?
Rio Branham: There’s been a lot. Your podcast has been really helpful. I think I mentioned that already, getting me in touch with the community, different people to follow along with. Some of the mentors that I’ve had through my undergraduate economics program who helped point me in the right direction, helped me realize that data science is what I wanted to do.
Rio Branham: I think that parents have been really influential as well in the part that they have encouraged me to pursue a passion. I think that’s where I’ve really gotten that nature of wanting to help and whether that’s professionally or in my personal life.
Rio Branham: The data scientist that I want to be is someone who can help out, who can really have an impact with a company, who’s doing some important work. I think my parents have had a big influence on that, being so it’s important for me. Yeah, just as I built a stronger network over LinkedIn, all the different influencers, people that you’ve connected me with or other people that I’ve come across on my own, just seeing them pursue their passions has really helped me realize what path I want to take and how to get there.
Kirill Eremenko: Wonderful. You mentioned community. We mentioned community a couple of times. What did you think, in my open key note at DataScienceGo, I said that there’s three keys to succeeding in this changing world of data science and that you got to focus on your skills, you got to focus on your career, and you got to focus on the community. What did you think of that?
Rio Branham: I think that was spot on.
Kirill Eremenko: Why is that?
Rio Branham: I think it’s really important. Like I said, it really provides a lot of motivation, at least for me, being in touch with people, seeing other people have success in the industry, really helped me continue pushing forward because a lot of the influencers on LinkedIn will be really encouraging and motivating and allow a lot of people that I talked to at DataScienceGo.
Rio Branham: To see other people have believe in your and tell you that you should keep going and keep working on things is important because data science is a technical field. It can be really daunting sometimes to feel like you’re never going to know everything that you need to. You’re never going to be able to get a job, because it’s just too competitive, but that community is really important in helping you realize that there’s a place for you here and people are willing to help. Yeah. I think that it’s a …
Rio Branham: Plus, I think another point of community that maybe isn’t mentioned as much is that collaboration is really important as well. I think rarely is a successful data science project done with one person. It really often takes a team from data engineers to data scientist to other business leaders, but even having multiple data scientists working on one project together, just having another set of eyes and another mind working on something can really enhance on your productivity. I think collaboration is really important. That comes from building a community as well.
Kirill Eremenko: Yeah. Totally agree with that. That’s very, very valuable point. Also, we all go through ups and downs in our lives and careers. Light times and dark times. Community what keeps you going in those dark moments when maybe you are unsure about the next step in career or you’re having a rough patch and you have somebody to turn to that you’re not completely alone, like a safe place where you know that, as you said, people make you feel that there’s a place for you here. That’s very important, that support component of community. That’s valuable as well.
Kirill Eremenko: All right. Next question. We’ve already talked about tools a little bit. What tools do you use on a daily basis. You mentioned Python. Are there any other tools that use on a daily or even weekly basis?
Rio Branham: Let’s see. Using SQL daily to extract data from our databases.
Kirill Eremenko: SQL and Python. Anything for visualization? What do you use for visualization?
Rio Branham: I know I mainly use Python for visualization honestly.
Kirill Eremenko: Seaborn?
Rio Branham: Yeah. Matplotlib and Seaborn is really what I’ve used mostly. I actually got really comfortable with ggplot2 in our and building some shiny apps. That was something that I think that I would like to start incorporating a little bit more just because I really enjoyed it and never had a lot of success building visualizations with that, but currently I just … I’m in Python so it’s pretty simple to just throw together a visualization in Python.
Kirill Eremenko: Got you. Okay. What techniques do you use most commonly? Tell us a bit more about them. You mentioned some of the algorithms that you use, which was probably the answer to this question. Let’s rephrase the question. What are some of the libraries that you use most commonly in Python?
Rio Branham: Hmm. Definitely Pandas for exploring data sets. SciKit-Learn is really where I do most of my modeling from in Python. NumPy is useful there as well for manipulating data with matrices. Matplotlib and Seaborn, we mentioned. Really just the standard data science libraries in Python is what I’m able to get most of my work done with.
Kirill Eremenko: Cool. Is there anything that you’re very excited to learn? What’s the next big thing that you’re going to be learning in terms of libraries or Python algorithms?
Rio Branham: I am excited to get a little bit more into deep learning. I started taking your course on Super Data Science, so I’m excited about that.
Kirill Eremenko: Awesome!
Rio Branham: One area that I think that I’m really excited about is learning a little bit more about production and putting algorithms into production so learning a little bit more about cloud technologies and Docker and some web frameworks like Flask and Django in Python. It may or may not be directly relevant to my job, but I think that it’s still a useful skill to be able to import your machine learning models into some sort of framework that can actually be used by a product, by some software, by some API. That’s something that I’ve really just am interested in and want to build up that skill.
Kirill Eremenko: Got you. That’s very cool comments and you gave me an idea for a course. We don’t normally see courses or people like place where people can learn about productization of data science projects and output so that’s a very valuable skill to have, as been mentioned on the podcast a couple times, that data science doesn’t stop when you bring the insights, like most of the time, you actually need to deploy that so that it’s part of the business that it’s integrated, that it’s now product that can bring value on a consistent basis and you can always, you need to maintain it and you need to take care of it. Yeah, so that’s a whole thing in its own right, a whole different skill set so really cool that you’re looking into that.
Kirill Eremenko: All right. Next question. What is the biggest challenge you’ve ever had as a data scientist?
Rio Branham: I think the biggest challenge has honestly been overcoming self-doubt.
Kirill Eremenko: Wow! It’s like that time when you through you should take down the article that you wrote because nobody’s looking at it.
Rio Branham: Yeah. Yeah, definitely and things like that. Maybe that’s just personal to me, but I think everyone can relate a little bit, like …
Kirill Eremenko: Of course.
Rio Branham: … a little bit of the imposter syndrome or feeling like your work’s not quite good enough, but a pattern that I’ve started to see recently is that I build up in my head this idea that something that I want to learn is just too hard and it’s just too complicated. I’m never going to be able to get it, but before I even try. That can get me down sometimes, but I’ve notices that with a number of things, once I start learning it, once I really start to put in the effort and move past that doubt and that fear of it’s going to be too hard, it actually comes and it’s not as difficult as they make it out to be. It’s not easy, but I realize that once I put in the effort, I can learn them.
Rio Branham: I felt that way about programming when I first started, I thought programming was just way too complicated. I definitely don’t know everything about programming, but it started to come once I really put effort into it. Same thing with machine learning and different learning about natural language processing, learning about GitHub or the thought of getting a job. All these things before I learned about them, before I achieved them, I had that kind of doubt that it was possible. Being able to recognize that pattern has been useful because now I can say, “Okay. What things am I think about right now? Okay. Maybe I’m thinking that about deep learning because I haven’t really approached it yet,” but if I acknowledge that, then I can get rid of the doubt and I can just go into it and start learning and not be too worried about it. I think that’s been the biggest thing for me to overcome is just realize that I have a lot more potential than I give myself credit for.
Kirill Eremenko: Wonderful. That’s very inspiring to hear. I think we all should learn from that. Everybody has those moments. I have those moments. Everybody I know has those moments where you’re like, “Oh, can I do this? Can I not do this?” You got to power through. You got to believe in yourself. We all, as human beings, we have so much power inside us.
Kirill Eremenko: There’s a motivational video. I don’t remember who it was by, but it’s one of the famous quotes which is mentioned there is that it is our light, not the darkness that most frightens us. A lot of the time, we’re actually scared that the self-doubt comes from what if I can actually do it? You think that it’s self-doubt that you’re thinking, “What if I can’t do it, if I’m not good enough,” but in reality, we’re just unafraid of the uncertainty of what happens when we go to this next level once we do do it and once we do accomplish what we are after. We’ve never been there. It’s a comfort thing. We’re comfortable in our current zone, like it’s called the comfort zone for a reason.
Kirill Eremenko: We know what we know, but once we do accomplish, you do learn programming or you do master that machine learning algorithm, it’s a whole new world that you have to adapt to and learn how to live in and thrive in because now you have more power in your hands and you’re not used to it. It’s much easier to just hold back and be like, “Um, I’m afraid that I’m not good enough.” In reality, a lot of times, we’re just afraid that we are good enough and it’s not something we’ve experienced before.
Kirill Eremenko: Very exciting to hear that. It’s a big thing, like I admire you for admitting that. It’s a big thing to look your fears in the eyes and especially voice it on a podcast. I’m sure a lot of people will be able to relate to that. Thanks so much for mentioning that, my friend.
Rio Branham: Yeah.
Kirill Eremenko: All right. Next one, what is a recent win you can share with us that you’ve had in your role, something that you’re proud of? Of course, getting a job as a data scientist is a massive win, but what’s runner-up after that?
Rio Branham: Sure. I think what I mentioned, I’m interested in learning a little bit about putting machine learning models in production. This is actually a couple months ago, my internship, but I got that bug and I was like, “I want to start learning about that.”
Rio Branham: I spent a weekend researching it, learning about AWS and Docker and Flask and all these other tools that I haven’t really played around with yet. In the process of a weekend, I’ve built really simple machine learning model on a public data set and was able to build out a really simple API that I could access and call my model and get predictions from. That was something that was real exciting for me, just reconfirmed what we were just talking about, which is, “Oh, wow. I can do hard things. This is really simple and really basic and I have a ton more to learn,” but I went into it with the thought process of, “I just need to build the most simple thing possible.”
Rio Branham: Then, once I see that it works, that’s just going to be a big motivation booster. Then, I know that I can move further. That was really fun thing, little side project that I did just over the weekend to prove to myself that I can do something.
Kirill Eremenko: Wow! That was done over a weekend. Man, that is really impressive. That’s very cool. It’s not that hard, at the end of the day. What did you say? Docker, Amazon and Flask, right?
Rio Branham: Yeah.
Kirill Eremenko: That’s so cool. It took you a weekend to do that. Tell us, so what does the end result of that project look like? You put a model into a system where it can be continuously deployed and can bring value? Is that correct?
Rio Branham: Yeah. I ended up just taking it down because it was just practice, but the idea is you would have some URL end point that where you could call from anywhere. Since it was live, it was hosted on Amazon Web Services and you have this machine learning model that you have built in the background and then you’re able to call this through the API interface call the URL. You can send some new data to this model. It’ll send you back the prediction from this model that you’ve already trained.
Rio Branham: That can be useful in tons of different situations where you have a model and you want to put it out into production where you want to be able to get new predictions on new data. It’s once you’ve built the model, then it’s accessible for you to interactively and continuously get new predictions on new data that comes through.
Kirill Eremenko: Wonderful. Thanks. I think that would be very valuable to other people. Is that code on GitHub that people can have a look at it?
Rio Branham: I don’t think it is yet. I should definitely put it up there, though. I will.
Kirill Eremenko: Please do put it up. We’ll share the link in the show notes when you’re ready. I think a lot of people can get value from learning how you did that. Awesome.
Kirill Eremenko: Okay. Next one is getting to the end. What is your one most favorite thing about being a data scientist?
Rio Branham: I love a lot of things about it. I think one of the really fun things for me is being able to identify where data science is in my everyday life. It seems to pop up everywhere, whether you see on a lot of these apps, Messenger and Snapchat, they have these facial filters, where you can see different, you can put dog ears on your face and things like that and seeing people who go like, “Oh, like, that’s artificial intelligence,” or just different apps that I use on my phone and then being able to identify how data science is really in a lot of different places and just the diversity of data science and how it can be applied in almost every field you can possibly think of. I love that. You’re not constrained to a certain industry or a certain type of project. You can really apply it to anywhere you want. That’s really inspiring to me.
Kirill Eremenko: That’s so cool. You’re actually reading my mind because when we’re recording this podcast, the episode that’s coming out tomorrow, it’s a five-minute Friday episode exactly about that. It’s exactly about where they’re finding data science in your life. I give a couple example of mobile apps that really blow my mind. There’s value in that. It’s important as data scientist that we actually do that, that we not just use them and that’s it, but think about, “Oh, how did they do this? Do these deep learning or natural language processing or follow deep learning or did they use some machine learning algorithm? Is this an artificial intelligence? What kind of AI? Is it reinforcement learning? How is it learning,” and things like that because that helps inspire creativity. That helps inspire innovation in us when we see all these applications. We don’t just use them like we would normally use them, but we actually use them with this critical thinking in mind of how would I do it and how could this maybe benefit my career. Very inspiring to hear that. Man, we’re on the same wavelength in that sense.
Kirill Eremenko: Okay. Awesome. My third question and especially I’d love to hear your opinion because you’re starting out, you’ve got plenty of knowledge in the field and you’re starting out in your career. Where do you think the field of data science is going and what are you preparing in anticipation of the future and what should our listeners prepare for?
Rio Branham: I really see the democratization of data science being a big part of it where more and more people are going to be able to get into the space. I think that the roles within data science are going to become more distinct and more defined. We talk a lot about job descriptions and how sometimes, they’re looking for a unicorn who does everything. Really, sometimes, there’s three or four different jobs that are rolled up into one job description to be separated out. I think that will become more defined over time. What a data engineer is, someone who specializes in data visualization and all the different areas of data scientist not just a data scientist.
Rio Branham: I think people will be able to specialize better in that way. I think that rules in data science will become available to a broader set of people with a broader set of skill sets, not just machine learning, deep learning, and AI so I think that will be an important.
Rio Branham: Something that I picked up from the conference from Sinan’s talk. He talked a lot about security and the different dangers of artificial intelligence and machine learning. I think that’s something that’s going to need to be talked about a lot is about what to do about potential security threats from different algorithms or bad actors who might be using machine learning or just data privacy, things like that. I think that’s going to be talked about much, much more.
Rio Branham: Me, personally? Like I said, I have some general skills that I want to work on, like deep learning and things like that, but I mostly try to stick to what are the problems that I have right now at my job or that I’m trying to solve personally in like a personal project. I try to just learn the skills in the moment that are going to help me with that project.
Rio Branham: I feel like, one, that helps me learn that tool or that technique better because I’m actually applying it right away, but two, I only have a limited amount of time. If I spend a bunch of time trying to study things that I’m not applying in the moment, it’s a little bit more efficient for me, I guess, to try to learn as I go. Maybe that’s not the best way to approach it, but what do I need to accomplish this task? I guess just be aware of other techniques that are up there even if I don’t know how to use them yet. Being aware of them is helpful because then I can say, “Okay. Oh, for this task, I’m actually going to need to know a little bit more about computer vision so I’m going to go research that, because it’s applicable right now.”
Rio Branham: I think a lot of times people will jump into it. I think when you’re first getting into it, yeah, you definitely need to get exposed to a lot of different parts or a lot of different areas of data science. I think Ben Taylor mentioned this, too. Just trying to pick up a little bit here and a little bit there and not really dive deep into anything leaves you without a lot of actionable skills that you can really provide value. I think it’s important to make sure that what you’re learning is something that you can actually apply or that you actually will be able to apply either at your current job or at a future job or in some project you’re working on right now. Maybe that’s not as specific as you were looking for, but that’s my take on it.
Kirill Eremenko: No, no. Totally love it. That’s a great overview. I think so many people are going to get some great ideas and valuable pushes for their next steps from that.
Kirill Eremenko: Thanks so much, Rio. It’s been a huge pleasure having you on this show. I think this podcast has actually gone overtime, but I’ve been enjoying it thoroughly and gaining a lot of value myself.
Kirill Eremenko: Before I let you go, though, what’s a best way that people can get in touch with you and contact you, follow your career, maybe collaborate with you. As you mentioned, one of the key benefits of community in data science is collaboration. What’s some of the best ways to get in touch?
Rio Branham: LinkedIn’s probably the best way to get in touch with me. I’m trying to be much more involved there. I more than happy to respond to anyone who reaches out and connect. Yeah, so LinkedIn, just Rio Branham and the spelling of my last name, we can put that in the show notes. It’s a little bit complicated.
Kirill Eremenko: For sure. Is it Rio Branham and we’ll put that in the show notes as well. Make sure to post when you do go overseas next year to a developing country to help out. Make sure to post an article about that. We’ll all be looking out for it.
Rio Branham: Definitely. I will absolutely do that.
Kirill Eremenko: Okay. One more question for you today. What’s a book that you can recommend to your fellow data scientists to help empower their careers?
Rio Branham: Sure. A book that really helped me a lot was Introduction to Machine Learning with Python. It’s by Andrea C. Müller and Sarah Guido. It just does a really comprehensive overview of a lot of the different techniques that you need to get starting with machine learning. That was really helpful for me. I’d recommend that.
Kirill Eremenko: Thank you. Thank you. That’s Introduction to Machine Learning with Python.
Kirill Eremenko: Okay. On that note, thanks so much, Rio for coming on this show. Great episode. Really enjoyed it. Finally, finally after listening to 200 plus episodes, you are on the podcast. I think we did a fantastic job and a lot of people are going to get a lot of great valuable insights from this. Thanks again, Rio.
Rio Branham: Thank you, Kirill.
Kirill Eremenko: There you have it, ladies and gentlemen. Hope you enjoyed today’s episode and got a lot of valuable takeaways. I know it went a bit overtime, but that’s because Rio had so many interesting and inspiring things to share. I’m sure we all got at least a little bit of inspiration from this episode. Make sure to hold Rio accountable to his reckless commitment that he made for 2019. Think of what reckless commitment could you make for your career or for your life in the coming years since we’re coming up to end of the year very soon.
Kirill Eremenko: Also, I would like to say that probably my favorite part of this episode was when Rio told us about how he declined a job offer in a data science company even before he had the job offer that he truly wanted with a company that he was passionate about, simply because he was not passionate about the mission of that other company, the first one. That’s something that we should all keep in mind that it is our lives that we are investing into our careers. We want to be working on something that we are indeed and truly passionate about.
Kirill Eremenko: On that note, make sure to connect with Rio, you can get the show notes at www.www.superdatascience.com/209. We’ll get all the links there and plus a transcript for this episode and URL to Rio’s LinkedIn. Connect with him and follow his career.
Kirill Eremenko: Also, on that note, we talked a lot about DataScienceGo. We’ve got a special final promotion running for the DataScienceGo recording so if you haven’t jumped onto that yet, this is the last day, last opportunity to jump on that. You can find the DataScienceGo recordings from 2018 available at www.datasciencego.com/recordings. Make sure to check that out if you haven’t yet.
Kirill Eremenko: Thanks so much for being here today. I look forward to seeing you back here next time. Until then, happy analyzing.
