SDS 321: The Life of One Advanced Data Scientist

Kirill Eremenko: Welcome to the SuperDataScience 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.

Kirill Eremenko: This episode is brought to you by DataScienceGO 2020, our very own data science conference. We’ve already done three events in the past three years and we’re moving into our fourth year in 2020. And to give you a feel for what to expect, here are some stats from DSGO 2019. We had 620 attendees fly in from 25 different countries. 38 speakers gave talks, 150 plus business decision makers attended the sessions as well and get this, 2400 cups of coffee were drank during the networking sessions.

Kirill Eremenko: So DataScienceGO is not just a place where you will get all the top data science skills that you need for your career. That’s definitely a huge component of the conference, but also it’s a great place where the community comes together to network. At DataScienceGO, you will meet data scientists and professionals from companies like Accenture, AIG, Wells Fargo, MasterCard, Facebook, Google, IBM, Microsoft, Salesforce, Teradata, Amazon, eBay, Shopify, and many, many more.

Kirill Eremenko: So this is a great opportunity to meet and network with your colleagues, to meet and start catching up with your mentor or maybe to even meet the manager at the next company that you’ll be working for. At DataScienceGO 2020, we’ve been almost doubling every single year. So we’re expecting about a thousand attendees at this next event.

Kirill Eremenko: DataScienceGo is happening on the weekend of the 6th, 7th and 8th of November, 2020 and you can already secure your tickets today at datasciencego.com. And one more thing is that we actually have different tracks. So we found that this is a very important component for attendees and we have tracks tailored to your experience. So if you’re a beginner, there’s a beginner track which will help you get the skills to break into data science. If you’re an intermediate practitioner, there’s an intermediate track for you to progress to advanced. And if you’re already advanced, there’s an exclusive advanced track just for you.

Kirill Eremenko: So whatever your level, you can find the right track, the right talks, the right workshops, the right sessions and case studies and panels at DataScienceGO. So on that note, this is the best conference for you to attend to skyrocket your data science career. So make sure to secure your ticket at datasciencego.com today. And I can’t wait to meet you in person in California in November, 2020.

Kirill Eremenko: Welcome back to the SuperDataScience podcast ladies and gentlemen, super pumped to have you back here on the show. Our today’s guest Morgan Mendis is one of the most advanced data scientists I have ever had the privilege of meeting in person. Morgan and I met at DataScienceGO 2019 a couple of months ago. And since then his life has taken on so many interesting twists and turns. You will be so excited to hear what’s been going on in his life. In this podcast you will learn the story of how we met and why we got to chatting at DataScienceGO in the first place. You’ll also hear about a VP of data science, a vice president of data science role that Morgan is helping fill in Washington DC. So if you’re an advanced data scientist somewhere in that area or if looking to relocate in that area, this is going to be super exciting for you. Roles like that don’t just lie around on the ground. They’re quite hard to come by and this is an opportunity of a lifetime.

Kirill Eremenko: So listen up is going to be really, really exciting. It’s at the very start of this episode, you’ll hear about that role. And even if you’re not looking to get into a VP of data science position, maybe if it’s a bit too early for you, maybe is that something you’re aspiring towards in the future? It will be very interesting to hear what kind of requirements are for in a role like that and what is the goal of a role of a vice president of data science. You will also hear about why Morgan decided to turn down a very exciting opportunity in his career and in order to follow his dreams, pursue his dreams and passions and move to Haiti and what he’s doing there, the very noble and admirable cause that he’s helping with his data science skills called Ayiti Analytics. You’ll hear all about that and how you can get involved if you’re also excited about helping others learn data science. So a very, very wonderful thing that Morgan and the team at Ayiti Analytics are doing and I was very Inspired to share this story.

Kirill Eremenko: And of course we also went through Morgan’s background and all of the great takeaways that he’s learned along his way to becoming an advanced data scientist. So you’ll learn about Excel and how for some applications he still uses Excel and why it’s important to know which tool to use where, how you can automate Excel with R and you’ll get some very valuable tips there, especially on how you can save time to apply to do more exciting things in data science. You learn about how Morgan mastered Python and why and when he uses R, when he uses Python, when he uses both. How he combined his data science skills with his econometric skills and what that led to. You’ll also learn a lot about the ETL process in data science, how to maintain models, why it’s important. And also Morgan went into quite a lot of depth on the Airflow tool, a very cool tool for extract, transform load procedures which you can already use in your career.

Kirill Eremenko: So if you’ve never heard of Airflow before, this is a great opportunity for you to get up to speed with this tool and see if it’s right for you. Those are just some examples of what you will hear on this podcast. I’m very, very pumped about the conversation that we just had and so let’s not put it off. Without any further ado, I bring to you advanced data scientist Morgan Mendis.

Kirill Eremenko: Welcome back to the SuperDataScience podcast ladies and gentlemen, super excited to have you back here on the show because today we have a very interesting guest joining us. Calling in from Haiti, Morgan Mendis. How are we going, Morgan? How’s everything going for you there? 

Morgan Mendis: Good. How’s everything with you Kirill?

Kirill Eremenko: Amazing. Everything is good and so cool that you’re calling in from Haiti like how do you pronounce it again? You just told me I already forgot. How do you pronounce the name of the Island?

Morgan Mendis: Oh, so it’s Haiti in English, but people might know it also in Creole as Haiti.

Kirill Eremenko: Mm-hmm (affirmative). Haiti.

Morgan Mendis: So it means land of many mountains.

Kirill Eremenko: The land of many what?

Morgan Mendis: Mountains.

Kirill Eremenko: Mountains. Okay. Well I’ve never been to Haiti. I would love to go one day. And you told me your origin… Your mother is from Haiti, right?

Morgan Mendis: Yes, my mother is Haitian and my father is from Malaysia.

Kirill Eremenko: Okay, fantastic. Very cool. And it’s very interesting. You’ve only gone back there like, would you say 12 days ago?

Morgan Mendis: Yeah, so I just moved here about like 10 days ago to start a new job down here.

Kirill Eremenko: That’s crazy. Like so much has been going on in your life. So let’s rewind back a bit. So we met at DataScienceGO 2019 in San Diego. That was what, one and a half months ago. Right? And since then so much has happened. So first things first, how did you get to DataScienceGO? What were you doing there? Because I thought you were actually, you live in California.

Morgan Mendis: No. So what originally happened was about in 2018 I saw on LinkedIn a bunch of people in my network in data science, I posted about DataScienceGO being a really awesome conference and that they were actually able to connect with other data science practitioners rather than with like industry and more corporate sponsors. So I remember during that week I went ahead and got the early bird special and was like, “Yeah, I want to go to this conference in California.” And coincidentally through my work previously, I also got accepted to be a presenter at another conference in the San Francisco area. So I went out there and then I told my employers at the time, I was like, “Hey, I’m already going to be out here in California. Would you guys mind supporting me going down to San Diego while I’m out there to go check out this data science conference I’ve already paid for?” And they said, “Yeah, sure, go down there, check it out. And also if you can make a plug for our new opportunity for head of data science at the team.” So that’s how I ended up at DataScienceGO.

Kirill Eremenko: Oh yeah. Yeah, okay. And then that’s when we talked and you mentioned that you’re hiring for a VP of data science. Did you manage to find anybody at DataScienceGO?

Morgan Mendis: No. That was actually the reason I remember I came up to you at the last day of the conference because I was like, “Man, I’ve met a lot of interesting people, a lot of students.” And a lot of them had questions for me and, I was really excited to give back, but I was also a little frustrated because I was looking for somebody at a higher level who could help support the team, especially because I was interested in taking this opportunity in Haiti.

Kirill Eremenko: Yeah.

Morgan Mendis: So that’s when you invited me on the show. So I could hopefully let it be known that they’re doing really cool and exciting things at Inspire and to meet somebody that would be awesome.

Kirill Eremenko: Yeah, absolutely. And I wanted to say that I really appreciated your feedback there and we actually very actively took that on board and consider it. And I was like, “Okay, why couldn’t Morgan find a VP of data science at the event? How can we, fix whatever that is indicating? And we’ve actually worked a lot on the event and at the next events at DataScienceGO starting from like the next immediate one, we’re actually going to have a separate track for very advanced practitioners like yourself. Like we already had some advanced talks this time and I think you mentioned you enjoyed like the Salesforce talk by the head of data science at Salesforce, is that right?

Morgan Mendis: Yes, yes. She was talking about model comparison and kind of the infrastructure and it was really great hearing her perspective and the future of Salesforce and data science there.

Kirill Eremenko: Yeah, so we already have a few talks like that, but next year we’re actually going to have a separate dedicated track for advanced practitioners only. It will be very exclusive limited seating to get as many talented advanced practitioners like yourself in the conference so that you can make network with each other and also that you can give back to the rest of the data science community. So definitely to give feedback on boards. Thank you so much for providing that and coming up to me and indeed for our listeners out there, one of the reasons why I’m excited to have Morgan on the podcast today is because this is so rare where a company is hiring for a VP of data science.

Kirill Eremenko: So if you’re an advanced practitioner or you’ve been in data science for three, four, five, six years or so or more and you really believe you can lead not just several data scientists into their professional career growth, but actually lead an organization in the space of data science. Morgan’s got a great opportunity for you. This is actually… So as I understand, you were in this position yourself and then you decided to leave to go to Haiti to pursue your passion and dream in this other company and now this position has freed up and you’re looking to help your previous company fill this role. Is that right?

Morgan Mendis: Not, not exactly. So the opportunity arose early, right about the same time I went to DataScienceGO, my previous manager had left and they were interested in me stepping up and taking on more responsibility. But at the time I was like, I’m really passionate about pursuing my dreams and that means I have to go to Haiti. So I wanted to support them as much as possible in finding this new person to fill in the role. And I thought that it was really important that the person have data science experience from the get go. Not potentially somebody ladder who’s making a horizontal move from software engineering or like a director of business analytics, but more focused specifically on data science that has a vision, a strategy for the tools, but also how can we incorporate, design into, and human elements into like healthcare data science.

Kirill Eremenko: Well, on that note, why don’t you tell us a bit about your company that you’ve just left and where this opportunity exists and if anybody’s interested, how they can get in touch about this role?

Morgan Mendis: Sure. So Inspire is one of the largest online networks for patients and caregivers to share information about medical conditions. So Inspire is connecting people so they can share information to help them really understand the condition, but also find emotional support through others. So through the position of data science, we’re trying to find different ways that we can surface relevant content and information to people so that they can find others who are like themselves and who are going through these really complex medical condition and potentially aren’t able to get that information directly from the doctors. So a huge aspect of the work I was doing was trying to synthesize information and all of this health data that they might be getting in terms of medical jargon and translate them to information that they can use to better plan and understand their condition to really honestly choose to live their lives in the best way that they can based on their own values. So not the values that potentially academic is prescribing them, but based on giving them access to information so that they can make their own decisions.

Kirill Eremenko: Okay. Wow, that’s very noble. Would you be able to provide an example of how you’re using the data to help patients who fight on given a specific condition, something like just to put it into like a tangible output somebody would get?

Morgan Mendis: Okay, sure. So we have a product at Inspire called… So we’re developing, it’s called Health Profiles.

Kirill Eremenko: Mm-hmm (affirmative).

Morgan Mendis: Which actually allows you to, you answer a few questions about yourself and then you’re able to see relatively in the community for example, what drugs are you taking for your lung cancer. You will answer these questions and then you could see other people in the community who are taking similar drugs and based on the privacy settings, because health information is, security and privacy are really important to us at Inspire. I’m saying Inspire as we, but they’re really important. So you want to protect the patient and give them full control over their information. But we still want them to be able to find other people who they might be able to relate with and connect with on a personal level.

Morgan Mendis: So we want to use the data to help them connect. So they’re able to answer these questions and they’re able to find other people who’ve answered similarly and then reach out to those people if they’ve said, “Hey, you can share my information with others in the community.” So that they can then ask them follow up questions about, “Hey, I’ve been getting this side effect from taking this drug. Have you experienced this as well?” Right. Or they might say, “Hey, I’ve had a really tough time adhering to my medication treatment schedule, what are some tips that you do in order to stay on top of your medication schedule?”

Kirill Eremenko: Okay. Okay. Got you. That’s a very, I would imagine helpful service to people out there who are going through these tough challenges. So hats off to you and to Inspire for doing this. This is a very cool undertaking. And tell us a bit about the role. So this VP of data science, how big would the team be?

Morgan Mendis: So the team is expected to grow. When I left there was about five members on the team and from the indication of the CEO, they really expect that data science is going to become the bread and butter of the company. So I wouldn’t be surprised if the data science team were to grow to being something a 15 plus. Again, I can’t speak too much about it because I’m not currently at the organization. However, they see the strategic importance of data science and they really want to find new ways of leveraging the data that they currently have and potentially looking to use existing open data to augment the data that they have as well. So for example, there’s a blue button that CMS or the Centers of Medicare and Medicaid Services as well as the veteran affairs have opened up via fire protocols.

Morgan Mendis: So this is also the same protocol that Apple HealthKit is using. So currently if you’re, let’s say you’re a veteran, you might be able to go in and get all of your medical information from veteran affairs, right? But this data’s coming out to you on a text blob. How do you visualize? How do you use that information? That’s a challenge currently for data scientists in the healthcare space, is that, we need to take this information and we need to be able to analyze and present it back to people in a way that then they can make their own decisions about their health and how they pursue medical services. So it’s a huge political debate in the U.S. right now about how our medical system and how we pay for medical services. But one thing that’s not debated by anybody is that patients should be in control of their medical information and they should be in control of their medical care.

Kirill Eremenko: Mm-hmm (affirmative). Yeah, totally. Totally got you. And what kind of a person is the company looking for this position? For VP of data science. What kind of experience?

Morgan Mendis: So they’re definitely looking for somebody who’s got experience getting their hands dirty, but they’re also looking for somebody who has experience strategically envisioning how to lead a team and how to take a product from prototype all the way to production and deployment. So they definitely want somebody who has the engineering experience and the analytical experience a little bit, kind of that business acumen, but is also willing to get a little bit deeper into the weeds of the technical depth of evaluating models, evaluating technologies. So of course, right? There’s the one thing about the idea of it being unicorn, but I think a key thing that we also want to push in this role would be the idea of understanding the importance of the patient being at the center of it.

Morgan Mendis: So definitely have to have a little bit of engineering and the product mindset, but I think that a really big important thing to Inspire is the culture. So making sure that somebody is able to understand the importance of patient centricity and autonomy. We don’t want somebody who’s pushing like, “Hey, this data science, it’s going to solve everything. They just need to give us their data and then we’ll manage everything.” No, it’s got to be a give and take relationship. So if they give us data, we need to be able to give them back something so that they have an incentive in order to share their information. Because Inspire is, it’s mission and goal is actually to promote medical science. So it’s trying to work together. It has relationships with the FDA and other research bodies trying to understand how can they better understand these conditions from the patient perspective. And so at Inspire, they call that the patient voice. So having someone in a role who understands the technologies, but also understands the human element, I think that’s really important for Inspire.

Kirill Eremenko: Mm-hmm (affirmative). Mm-hmm (affirmative). And where is Inspire located? Where would this role be based?

Morgan Mendis: So this would be based on the D.C. area, Inspire’s located at Arlington, Virginia.

Kirill Eremenko: Okay.

Morgan Mendis: Right. It’s going to be right next to new Amazon headquarters.

Kirill Eremenko: Nice. D.C you mean Washington D.C. right?

Morgan Mendis: Washington, D.C. Sorry.

Kirill Eremenko: Okay. Got you. Awesome. Well, and finally, if somebody is interested, how do they get in touch? I’m assuming you would make the referral. How do they get in touch with you?

Morgan Mendis: So they can reach me obviously on LinkedIn. So Morgan Andrew Mendis, M-E-N-D-I-S. That’s my last name. But yeah, please feel free to send me a message on LinkedIn. I’m happy to share more information.

Kirill Eremenko: Got you.

Morgan Mendis: But if you also want to look up the Inspire website, it’s www.inspire.com.

Kirill Eremenko: Nice. Very cool. And we will of course show those things in the show notes. So if anybody out there is interested in a VP of data science position, which we don’t talk that often about on the podcast, there’s your opportunity. And also I think this was very cool to even, if you’re not interested, I think it shows an example of what people are looking for in a VP of data science role and what those roles are like. So yes, thanks a lot Morgan for sharing that. Hopefully you’ll get some really cool applicants for this role. And it sounds like the company is doing some very good things for the community. 

Kirill Eremenko: And so tell us a bit about your dream now, congratulations first of all, you’re pursuing your dreams. You’re in Haiti, you’ve completely changed the course of your career now and your life, sounds really exciting. Tell us like, how did all transpire?

Morgan Mendis: I was just a little kid, back in Maryland and I was dreaming of, how can I eventually grow up to give back to the world? And I was telling you earlier that it all stemmed from the idea that I want there to be more economic opportunity in the world, economic development. And it kind of stems from, in order to have economic opportunity, you need to have some kind of education. You need to have some kind of skill. But before you could pursue education, you need to be able to pursue, have good health. So that’s why I really got into healthcare.

Morgan Mendis: My goal was to get into global health. However, the positions are extremely competitive. And that’s why I was board on an insurance company and I was working part-time during my final semester of college. So I started learning R and that’s kind of how I got into data science. And that kind of led me down the road to eventually finding myself in a position where here in Haiti they needed somebody who was able to do data analysis and data visualization and really string together all of their information systems. And that’s how I ended up here in Haiti.

Kirill Eremenko: And you’re now the principal data scientist at, what’s the company called?

Morgan Mendis: So yeah, I’m actually doing two things down here in Haiti. One is that I’m working with a nonprofit organization called the Caris Foundation. And there I took the title as health informatics consultant. However, I’m also working to start the first data science lab here in Haiti called Ayiti Analytics. And there we’re trying to train the first generation of data scientists in Haiti. So we’re really excited to hopefully push forward the opportunities of Haitians to pursue analytics and to use data science to improve the state of the nation.

Kirill Eremenko: Wow. That’s another very noble cause. And yeah, what kind of challenges, you only started this like a few weeks ago. What kind of challenges are you expecting along the way?

Morgan Mendis: So I was mentioning to you earlier that there’s a lot of current political instability in Haiti. So it’s tough to get people to come into the office every day. Internet access might be intermittent here. Sometimes people lose power. So it’s really difficult to have some kind of cadence in terms of scheduling. So we understand that for people sometimes to get to a location where we’re having the onsite trading, that could be a challenge, getting around the city, there’s often roadblocks. So it’s common here for you to be in many WhatsApp groups in order to get information on which streets are available. So that’s one challenge, is transportation and for people coming in.

Morgan Mendis: Another challenge obviously is that for people to take the time off, to spend, to learn data science, to invest that time, they need to potentially not be working or they might need to be working twice as hard as other professionals in other countries where they might be able to have a safe location where they can have internet access, to learn the code. A lot of data science learning requires online content and if you can’t get people physically in your proximity to help mentor you, it’s difficult. You need to be able to reach them via the internet and that’s a challenge here. So we are experiencing that challenge, another challenge is that there’s not a lot of resources in Haitian Creole and in French for data science. So I was very excited that you are telling me that SuperDataScience has some of their courses in French because that’s definitely something we’re going to be interested in.

Kirill Eremenko: For sure. And as I mentioned, I would be more than happy to support a mission like that and provide free SDS accounts to your community to make sure that they’re learning and like we can do as much. And I always love when people are doing things like this and this was like what was surprising me when you were speaking just now that you effectively turned down a vice president of data science opportunity in a up and growing company which is making a massive impact in the world. It’s something that people would just love to have, a lot of people are aspiring to have and build their career towards the VP of data science position. You evicted turned that down to move to a country where you’re facing lots and lots of challenges and at the same time like by your voice, I’m sure our listeners will agree, I can tell you are happy, you’re like excited, you’re living your dream.

Kirill Eremenko: How does that add up? Like you turned down a massive progression in your career in order to follow your dreams in a completely different environment, which seems like much less secure, much less safe, and yet you’re very happy. Tell us, how do you feel about all this?

Morgan Mendis: So one thing I have to say is that Haiti is one of the most beautiful countries in the world. And I know Kirill that you were talking earlier about that we both are avid travelers. I’m just blown away being here. I love the culture, I love the food, I love the music. It’s such a beautiful place. And I think that when you talk about the opportunities of like people in their career progressions, some people, they want to be in a management position, they want to be in a position where they’re being able to bake big, high level strategic decisions. I have always been on more of the community organizing side of things where I want to be down with the people. I love learning languages because I like talking to people and I like learning about their problems. And generally what I’ve noticed is that providing simple solutions that aren’t complicated, that can change somebody’s life to me, is much more rewarding than building these really complicated tools and models that kind of sit behind, sit in a server somewhere that nobody ever sees and potentially doesn’t change their life.

Morgan Mendis: It might make somebody, a few more percentage points of ROI on their investment vehicle, or it might save a couple dollars for a supply chain. However, for me it’s about changing somebody’s life. It’s about talking to people, it’s about hearing about how can I make it so they can live a more rewarding life. Because, data science has given me the opportunity to live a rewarding life. Our education as a society, our development as a civilization has all been towards, pushing the whole race forward. It’s not about us individually or us and tribes, it’s us about, us coming together. So I’m really excited to be here and yeah, I think that’s about different interests, different passions. You have to choose what do you find valuable in life?

Kirill Eremenko: Mm-hmm (affirmative). Mm-hmm (affirmative). Yeah, no, that’s absolutely true. Your fulfillment, happiness, they don’t really come from accomplishment or going up the career ladder. And sometimes it’s necessary, sometimes you might find exciting but sometimes you just want something else in life and that’s totally normal. It’s important to be able to let go of things and move on to, as you said, you’re following your dreams. It’s like no better place to be. So very excited for you. Very pumped up. Can’t wait to hear some of the great things you’ll do. Maybe like in a year, a year and a half we can do another podcast where you’ll tell us about all those things you’ve created in Haiti and how many people you’ve gotten up to speed of data science. I think that’ll be very cool.

Morgan Mendis: Yeah. Well I have to push you is that, I would argue for me success would be that you are inviting somebody from Ayiti Analytics who we’ve trained and who we’ve got off the ground to be on the show to talk about what they’ve done in maybe the last nine months.

Kirill Eremenko: Nice.

Morgan Mendis: That would be to me success. That would make me happy.

Kirill Eremenko: That was awesome. All right. Okay. Let’s blend that in. Sounds like a good idea. Okay. So let’s take this opportunity that we’re on the podcast and what I would love to do is you are by far one of the most advanced data scientists I’ve encountered in my, speaking with data scientists, meeting people, traveling around the world. And I want to… That’s one of the other reasons why I invited you on the show. I really want to share your experience of advanced data science with our audience and be like what kind of takeaways they can get. So the things that you’re teaching in Ayiti and the people that you get up to speed, I think they’re going to be very lucky to have such a great mentor as you leading them. So I would love to see what kind of insights you can also share with our audience here. How does that sound? Do you mind going through a couple of your case studies or use cases of data science that you’ve done in the past?

Morgan Mendis: Sure, I’ll have to preface it with the fact that I don’t consider myself even within the top 10% of data scientists. So I appreciate the compliment, but I think that what I do is that I take models and I take whatever tools best fits the situation and hey, sometimes that’s Excel. I make that argument all the time is that you don’t need advanced tools. Sometimes you just have to use the tools to the full capability or full extent. But yeah, I’m happy to go through some of the case studies that we’ve discussed earlier.

Kirill Eremenko: Mm-hmm (affirmative). Sounds good. All right, well let’s get started. So are you going to be fine if we go through your experiences like post-graduation, like one by one like ChenMed, Aledade and so on.

Morgan Mendis: Sure.

Kirill Eremenko: Is that better?

Morgan Mendis: Yeah, yeah. No, that’s fine.

Kirill Eremenko: Awesome. So in that case, let’s get started, and perhaps let’s just go through your experiences after graduation one by one. You mentioned that, in your email that the first role that you had post collage graduation was at ChenMed. So like what did you do there and what kind of tools did you use?

Morgan Mendis: Sure. So I first, when I got out of college, I started as a business analyst at ChenMed. So they are a medical provider. They run several different facilities across South Florida and they’ve opened up across the Southeast. But my position really was using Excel to do a lot of business analytics. So that was writing reports, copying charts, and putting them into PowerPoint and all of this. I was a little frustrated. I was like, we can be automating a lot of this before. And so what I started doing was that I started automating a lot of the work that I was doing in R, and I would actually show up really early in the morning before everybody else, seven o’clock. So I could start automating the reports and my manager had no idea that I was secretly automating the reports. But the reason why I would stay late is because I was then using the data to then explore different tools and different libraries in R.

Morgan Mendis: So one of the things was, we had a challenge of understanding how to move different… One of the patient offices was closing and we wanted to reallocate people to different offices within the geographic region. Very simple. Took the data, converted into longitude, latitude, place it with Gigi Maps onto a map. And then I was able to calculate the distances to the local offices and say, “Hey, we can just put all of the patients to this office and they won’t reach capacity. We can put the other patients this office and they won’t reach capacity.” And based on proximity, they’ll still be able to do it within their regular commute. And it won’t be that much of a transition for the patient populations. So very, very simple things like that. Taking existing tools, right? But my key thing was, “Hey, I already know these tasks exist out there. Let me just try and automate them and that way I can get more access to the data to explore what else we can do.”

Kirill Eremenko: Mm-hmm (affirmative). What’s an example of the automation that you were performing in Excel with R?

Morgan Mendis: So there was a lot of opening up different spreadsheets, setting up linkages, lots of different, what we would consider typically to be unions or concatenations of the datasets. And so that was a simple thing is that I would have to wait potentially for new data set to come in or new Excel file to come in before I could potentially do my full reporter, run my aggregations, like the pivot tables and to me it was like, “Pivot tables are cool, but there’s nothing special about them. We were familiar with them in Group By and SQL. So I started just saying writing up scripts. I was like, okay, once the data comes in, boom, run the script and the data’s going to be pumped down to a new Excel spreadsheet. I just have to then move a table into a PowerPoint and focus more of my time on analyzing the data for the key insights that we’d be providing back to the office professionals rather than spending my time trying to make sure that my indexes and my VLOOKUPs match up.

Kirill Eremenko: Mm-hmm (affirmative). Mm-hmm (affirmative). Okay, got you.

Morgan Mendis: So it’s about taking the time so that you can focus on analysis and understanding the data rather than focusing on, “Hey, am I looking at the right file? Hey, is there any kind of data validation errors going on?” That’s another example is that, if you need to validate the data or if you necessarily had issues with potentially the formatting in Excel, with R, with Python, you can automate most of that duel, do unit testing in order to validate the data and so that you don’t necessarily need to spend your valuable time as an analyst checking these small little boxes. You can spend more of your time understanding the data, understanding what was the process of which the data got to you by and potentially how can you make it more valuable when you send it off to the next person.

Kirill Eremenko: Mm-hmm (affirmative). I love it. So you’re freeing up your time from checking small things or concatenating data, doing these recurring tasks in order to have more time for exploration. That’s a really, really cool thing. And it sounds a lot like a robotic process automation, this type of automation, were they, the scripts that you wrote, did you need to like run them yourself or were they running in the background like every night or something like that?

Morgan Mendis: So again, this is part of me actually, I had to write the scripts and there was no automation. I couldn’t set up a cron job at the time. So they, at this organization at ChenMed, they were pretty tight about what tools you could use and what access you could get to the internal systems. So there was no way that I could just, okay, I’m going to go hack into their system and set up this cron job or set up this automation process. I wrote the scripts, so that’s why I would come in early and I didn’t mind coming in early because then I said, “Hey, I get to spend the rest of the day exploring and innovating with the data. So I don’t mind coming in early to just run the script. I can do it while I’m having my cafecito.” This was while I was in Miami.

Kirill Eremenko: Nice.

Morgan Mendis: So I love the Cuban coffee.

Kirill Eremenko: Got you. Okay, cool. Very cool. I think it’s a very valuable insight or career advice for people. Like if you find yourself doing recurring things where stuff can be automated. And I love your dedication. Like already you can tell you’re loving what you do. You come in early, you stay late, you’re having fun along the way. Fantastic. What was next, where’d you go after ChenMed?

Morgan Mendis: So after that, because I wanted to get more access to actually deploying more advanced data science and actually using more tools, I took a job at Aledade as a data analyst. And so the first day when they hired me, they were like, yeah, you can use R. That’s great. And once I started the engineers looked at me and said, “No, this is a Python shop. You got to learn Python.”

Kirill Eremenko: So they didn’t know what they use at the interview?

Morgan Mendis: No. So originally the rest of the analytics team at the time was using SAS and I had studied econometrics of strata in my undergrad, so I was kind of familiar with the idea of SAS, but I was like, “Hey, it’s not open source. It’s not my go to tool.” But when they suggested I learn Python, I was like, I’ve been looking for an excuse to really ramp myself up on that. And so luckily I had a really good mentor at the company, a gentleman by the name of Jim Fulton, who really provided a lot of guidance to me in learning Python and learning some very good standards for software development. And I don’t consider myself a software developer by any means, but he definitely helped to guide me along and help me learn about a lot of the tools, even though he wasn’t working in data science about how you could use Python for data science. He’d been using Python for so long, he’s like, “No, of course, this makes sense. It’s a great tool for data analytics.”

Morgan Mendis: So, at that point I started exploring Python and PostgreSQL and really got excited about it because I was like, “Oh man, this opens up a whole new set of tools and opportunities for me to either automate or explore new modeling techniques and connecting different tools.” So again, for me it’s all about finding the right set of tools to improve the process and then, as you improve the process, you’re going to get a lot of gains across the organization. So I was really enjoying at that point, but a lot of my most enjoyable experiences there was actually more leveraging my training and economics to do econometrics.

Morgan Mendis: So at Aledade they are working for what’s called accountable care organizations. And they were, in a similar vein to ChenMed. They were trying to push patients centricity in a new movement that was called value based care. And so we started exploring how can we analyze the data to make, make the system more effective to providing optimal care to patients while also reducing the cost of care. Right. So that’s a huge issue is that, healthcare is very expensive. So is there potential, not necessarily to reduce the medical services but instead to say which kinds of medical services are going to have the greatest gains for patients.

Kirill Eremenko: Okay, very interesting. Okay. And so you were able to actually leverage econometrics and combine that with data science. What are your takeaways from that? Not often do those two get combined by data scientists.

Morgan Mendis: Yeah. So I think the really interesting aspect was that, and this is what I really started really understanding the enjoyment of doing data science, was that we are using statistics to do these evaluations and to better understand, what we call medical interventions or different treatments for different patient populations. And the really interesting thing is that, I was able to do like some complex analysis and make some strategic decisions which changed our program delivery in terms of how we suggested our health system work. And I remember years later after doing this analysis, I was reading, and health affairs, which is a really popular journal for health data and analysis. And a group of researchers had published some findings that I had already uncovered earlier at this company. The difference though was that after I did my analysis, I had found some discrepancies in what our original hypothesis was.

Morgan Mendis: And so I went down and did deep dives into the data. Then I went up and I called actual people who are working in the sites to come up with a patient narrative like so to better understand the data and then we were able to use that narrative to explain the model and explain the results to other people. Because a lot of people don’t want to see, your regression outputs, right?

Kirill Eremenko: Mm-hmm (affirmative).

Morgan Mendis: That’s not the thing that’s going to change their mind. What’s going to change their mind is if you give them a story, give them a story that they can remember so they can better understand, in the future be like, “Oh, this kind of sounds like what Morgan was telling me about the story.” And so I thought it was really interesting that I remember reading through the article and at the end they identified this area but they didn’t provide any narrative or any explanation. And that was the benefit to me was that, having this opportunity to actually within a data science capacity, to actually be able to use this advanced methods and get to the same conclusions as academics, but being able to work with actual practitioners to come up with a narrative that’s going to change the system. That’s the exciting thing.

Kirill Eremenko: Is that why you used Tableau in this role to explain these things in a more visual way?

Morgan Mendis: Yes, yes and no. So Tableau was one of the tools that we use to obviously help tell the narratives. We also use Tableau as a business intelligence tool and it just allowed us to rapidly take all of the massive amounts of data we had and quickly turn it out. So when I think about Tableau and I think about its value, I think about how quickly you can transform data into insights rather than, you still need to, it’s not going to replace the opportunity to spend time with people talking to people. And I think that that’s the one thing I want to emphasize here is that, Tableau is great for being able to present information, but it’s not going to replace the storyteller. It’s just an aid or tool to, help you as a storyteller.

Kirill Eremenko: Mm-hmm (affirmative). Okay. Absolutely.

Morgan Mendis: You got to make sure you have a story, story tell and there’s a quote that I’m reminded of is that “stories happen to those who knew how to tell them”.

Kirill Eremenko: Wow. Nice. Very beautiful. Very beautiful quote. Okay, so you manage to combine these two fields, econometrics and data science. What else did you experience in this role? Because there are some other, tools you use not just Python, also is R. You also use both Python and R in this role. Is that right? 

Morgan Mendis: So I really preferred actually the statistical output from R, so there are some regression models at the time that I couldn’t find in Python and I just felt a little bit more comfortable with the robustness of all the output that R was giving me. And as your listeners may well know is that, R was designed really by statisticians. It was born out of S. So there’s a lot of model development historically that’s been done in R, and there’s a lot of really interesting modeling that’s or innovative modeling that’s been done in R previously before it got into Python or before data science blew up to what it is today. So at the time, I really liked using Python because it helped me connect my different solutions, but I like to use R when I was actually doing the evaluations. But if I was building a new data product for example, or connecting some SQL to build a web application, then I was going to go to Python and I might then just have Python execute an R script if needed.

Kirill Eremenko: Okay. Yeah. Makes sense. I’ve heard of that done before. But since then, have you moved completely to Python or are you still using a combination of the two?

Morgan Mendis: So I definitely go to Python. Like, I just got, at my new position, I haven’t installed R, R studio yet, but for example, I was working on the side doing a research project with a former colleague and he asked me, he was like, “Would you mind doing this in R so that I can follow along with your code?” And I said, “Of course.” That’s 100% of valid reason to use a language is that you can collaborate with others. If your team isn’t using Python, don’t force Python on them. Use the tool that’s going to best enable you guys to work together. Because to me, collaboration is way more important than your personal speed in a language.

Kirill Eremenko: Mm-hmm (affirmative). Yeah, no, totally. Totally agree. So as I understand you’re mostly using Python now, but do you still use both from time to time?

Morgan Mendis: Yeah.

Kirill Eremenko: Oh, okay. Got you. Anything else? Did you use any advanced, I don’t know, maybe like deep learning in that role?

Morgan Mendis: Oh no, no. I have not actually had the opportunity to explore deep learning in production. I’ve only been able to play around with it in my like personal side projects, but nothing in production yet.

Kirill Eremenko: Okay, got you. And so when you would deploy models into production, would you then later maintain them, and make sure that, like check up on them that they’re working well?

Morgan Mendis: Yeah, I think that’s a key thing of course is that every, depending on the data interval of your data. So for example, we might be reevaluating models on a monthly basis because for example at Aledade they were getting data from the government at a monthly basis. And because of the lack in terms of getting complete records and claims data, you might only be getting a portion of a given month. So you’re going to have to wait almost three or four months before you have a full picture of all of the events that transpired in a given month. So you have to keep updating your models regularly to incorporate the new data or potentially corrections to the data that might be coming. So, especially if you’re dependent on data coming from a third party, right? They might change, they might change their methodology for how the data is being sent over to you. Right?

Kirill Eremenko: Mm-hmm (affirmative)

Morgan Mendis: Or some other kinds of procedures. So you need to be able to quickly, you need to be regularly, I’m sorry, not quickly. You need to be able to regularly go back and reevaluate.

Kirill Eremenko: Mm-hmm (affirmative). Okay. Got you. Awesome. Well thanks for sharing this role at Aledade. It sounds like a very important step in your career where you’ve got to learn Python and also apply econometrics in combination with data science. Yeah. What was the next step after that?

Morgan Mendis: So I joined Inspire the position we were just talking about, which they’re hiring for the VP of data science and I was actually the first data science hire at the company.

Kirill Eremenko: Oh, okay.

Morgan Mendis: So, yeah. So it was a really, I was kind of a green field opportunity and I actually got to learn a lot about all the different aspects of data science in terms of building up first key analytics and then moving to challenges such as buy versus build. So, I think I ran the gamut at Inspire the different things I did. So I had some fun, building out like time series regressions to forecast member growth, obviously that’s fun modeling. The other thing was, working with AWS and setting up different systems to build a data lake. So just to start there is that because it was a green field opportunity and I was the first data science hire, I had a different perception of the way systems need to work in order to produce high quality analytics. And so the company had existing reports that they needed. But for me I wanted to get into the exciting work of “Hey, let’s start building exciting data science products.” However, I need to have the data in a format and in an environment that’s accessible and is going to allow for development.

Morgan Mendis: So the first thing I had to do was start designing a data warehouse and building out an ETL process. So I want to say that probably half of my job was actually data engineering and then maybe a quarter was actually doing exciting data modeling in data science. And then another quarter was actually doing much more of like the analytics management of saying these are the tools that we need to put in the place, these are the kinds of resources we need to do and this is how we need to prioritize all of these different objectives and opportunities.

Kirill Eremenko: Mm-hmm (affirmative). Okay. Interesting so that you separate them like that. So data engineering would involve putting the right datasets together and making sure that all data is flowing properly. Is that about right or is there other elements to that role?

Morgan Mendis: No, so I would say that it really encapsulates much more than just building the right datasets. It’s about identifying where the data’s coming from and then mapping out the best processes to putting them into an environment that’s accessible for data scientists or data analysts. So I say that more, it’s more than just creating data sets because they have, I think a good data engineers, is like critical to any team. Again, I wouldn’t consider myself a good data engineer. It was more of, you have to get this work done in order to do the fun stuff. So it’s actually more much more related to software engineering, I think in the sense that you need to be able to put these systems together in a sustainable way so that the data analysts, the data scientists don’t need to worry so much about cleaning and validating the data. They can spend more of their time analyzing it and talking with stakeholders to building products.

Morgan Mendis: So in that role I was, I first took the data, I explored AWS data pipelines, that didn’t work. I thought about just writing a bunch of my own scripts and setting it up with cron jobs. And I was like, that’s not sustainable. So eventually I settled on Airflow. And this is Apache project that’s, I think it was originally developed by Airbnb and it’s pretty amazing. It just allows you to set up a lot of jobs that happen in parallel so you can move data from one system to another, process it multiple times, and you’re actually able to create a directed acyclic graphs. So you can see in a network how your data’s flowing and potentially where there’s bottlenecks or potentially where there’s errors that are going to break down your ETL process.

Kirill Eremenko: Okay. Wow. And so you settled down on Airflow and that solution is still running now?

Morgan Mendis: Yeah.

Kirill Eremenko: Wow.

Morgan Mendis: And yeah, I’m hoping to set up a lot of other jobs of Airflow in the future. I think it’s a great tool. I hope that AWS catches up in their data pipeline, but I always keep my fingers crossed. I think AWS, they’re always going to produce something amazing. But right now I think Airflow is doing a great job. And especially if you’re looking for something to quickly get started with to build your own processing. Highly suggested.

Kirill Eremenko: Okay. So tell me a bit more, how does it work? So you have lots of data sources, you have an ETL process. How does Airflow facilitate that?

Morgan Mendis: Okay. Yeah. So Airflow uses what’s called operators. So you might be writing scripts and writing them as functions. It’s callable. So in order to move data, transform data, or to produce reports, right? So you can use Airflow to send messages, you can use Airflow to, like to send emails, like automated reports to end users. But the key thing is that you’re writing functional scripts in Python or you could actually use other languages too. I know it basically allows you to use whatever language is best suited for you. So you could write bash scripts if you wanted to. And they use what’s called these operators to then call these functions to do these transformations.

Morgan Mendis: So Airflow also has, I believe integrations with Redshift and Postgres and many other like popular data tools that you can then say, “Hey, I’ve got this data in my SQL database, I want to federate it across, let’s say put it into elastic search index, right? And I’m going to set this index up. So I would say, all right, Airflow, copy this data down, put it into CSVs for MySQL, then store those flat files into S3 then from S3 I’m going to load them into elastic search and or separately into Redshift.

Kirill Eremenko: Mm-hmm (affirmative).

Morgan Mendis: Right? And each one of those tasks, moving the data from MySQL to CSV, from the CSV to S3, from S3 to whatever data system or storage other system you want for analytical queering, each one of those tasks could be a Python function and you would then have different notes set up. And so then you can create dependencies, right? So that one task will only execute after successful completion of a previous task, right? So as you imagine, there can be splits, right? Or there could be different tasks happening in parallel and all of that can then be represented via a directed acyclic graph. So if you’re a big fan of, network theory and optimization as I am, you are really excited because then you have a bunch of graphs and nodes and you can watch them all move and execute and you just get a little giddy about watching it all.

Kirill Eremenko: That’s so cool. It sounds like a very advanced version of SSIS that Microsoft provides.

Morgan Mendis: Yeah, I can’t say I’ve worked at SSIS but it, yeah, it’s open source. So I think you can probably-

Kirill Eremenko: It’s just like a really cool advanced ETL tool.

Morgan Mendis: Yeah.

Kirill Eremenko: Mm-hmm (affirmative).

Morgan Mendis: Yeah, exactly. Like so I said before, it’s like Amazon has their data pipeline process and it’s very similar is that you can write scripts and you can even leverage it with Amazon Lambda functions in order to execute different things in the same way like Lambda is just, you’re hitting different functions and you have inputs and outputs. Similar, I just thought that it was a lot easier to work with Airflow. It’s all in one system. Rather than going to AWS where you have to work in their ecosystem, you have to configure everything via JSON. The nice thing is that Airflow gives you, it’ll spin up like a flask instance so you have a web app that you can interact with so you can turn on and off different operation jobs. And I was actually, I’m really impressed. One of my friends showed me that his company wrote their own custom operators in Airflow in which they were then able to use what they call juper centric development.

Morgan Mendis: And this is a really interesting idea is that to make data scientists, able to quickly iterate and prototype and put things into production, they would just make it so that if you write the Jupiter notebook so that executes and builds a model or processes the data, whichever way the data analysts and the data scientists want, all they need to do is to have a notebook that’s clean enough that can run from start to finish successfully and boom, you just take that notebook, you send it to your data engineer, they put into Airflow and you have readable code. Right?

Kirill Eremenko: Wow.

Morgan Mendis: Right. It’s a beautiful thing is that you have readable code living and working in production. You don’t need to take your code from the Jupiter notebook copy to a PI script, wrap it in another function. No, you just have a Jupiter notebook. It’s all there.

Kirill Eremenko: Very cool. Very cool. That’s the way it should be, right? Like why should you have to go through all these hoops and finally create potential for additional errors where you can just, you already have the code, just run it.

Morgan Mendis: Yeah. And again, like I said, because it’s readable and you have all the formatting and the benefits, like for example, you might have it so that your Jupiter notebook is going to produce some kind of visualization of your metrics or evaluation of your models. I’m not sure if you guys have talked previously about using data visualization to evaluate models in development, but if you have that in your script, then you could quickly go into Airflow or go into your system, look up the notebook and say, “Oh Hey, that model that we’ve been, checking up on, we can see how well it’s performing with the new data via Airflow by just checking the notebook rather than having to go through another all these other hoops to evaluate the model.

Kirill Eremenko: Mm-hmm (affirmative). And to validating the model, you just mean like the lift curve and things like that.

Morgan Mendis: Yeah, you could have all of those visualizations right there.

Kirill Eremenko: Yeah, I said that they’re just updated within your data. You don’t have to rerun them specifically.

Morgan Mendis: Yeah.

Kirill Eremenko: Very cool. Very useful tool. Thanks. Thanks a lot for the debrief on how Airflow works. It makes me like wonder like Excel, R, Gigi Maps, SQL, Python, Tableau, Scikit-learn, AWS, Airflow, Plotly, Plus. All those you’ve already mentioned except for Plotly probably. You’ve already mentioned on this podcast once or twice or many more. Is there a limit to the number of tools an advanced data scientists should know? Are you just like, just keep picking up new ones all the time?

Morgan Mendis: No, I think, yeah, you’ve got to constantly be learning. like I didn’t even touch on like using spark or anything like that or trying, like right now I’m very interested in learning Scala just because I’m like, “Oh, this is going to be a great language.” Then maybe move on to Kotlin or something else. But I think that as you learn new tools, you think of problems in a different way and I think that’s the key thing is that, for example, I love, as I mentioned earlier, I love learning languages because I like talking to people. But I also know that what’s really interesting in learning languages is that you start thinking in a different way. You start thinking culturally or you start thinking as you approach people in a different way than you might have thought before.

Morgan Mendis: There’s a popular theory, I don’t know actually how popular it is, but it’s called Whorf Theory which from linguistics, which says that you are limited to what you can think based on the language that you know. Right? So for example, we might not be able to think of a certain solution because we don’t have the words to envision it or describe it. So again, I think I was actually listening one of your previous podcasts about, the future of AI and I thought this was good as you guys were mentioning, can you explain an ant language to a monkey, I mean, can you explain in ant language what a monkey is to an ant? Right? And I think that that’s a key thing is that as you learn new tools and languages, you’re going to start thinking about problems in a different way. Right?

Morgan Mendis: And I don’t think it’s about, like I said, I still go back to Excel to do certain tasks, not because I’m like, “Oh, Excel is the most advanced tool.” But instead I tell people, I was like, “Instead of learning necessarily all of these advanced tools, it’s like have you mastered Excel? Because Excel might be the key tool you need to be successful or for your organization to find value in the work you’re doing.”

Kirill Eremenko: Absolutely. Absolutely. And what languages do you speak out of curiosity?

Morgan Mendis: So English, Spanish and Portuguese. And I’m working now on my French and Haitian Creole.

Kirill Eremenko: Nice. Very nice. And so what I wanted to like, to your point, this is a great example that sometimes you cannot think of a solution, just because we are limited by the language we speak. So I was born in Russia and I speak Russian. And like recently I’ve been interested in Eastern European languages. Now I’m learning Spanish as well and I’ve found this very peculiar phenomenon that for example, in Russian, we don’t have a separate word for hands and arms, we just have one word, ruka. And that means hand and arms. So we’ll would be saying like, instead of shaking your hands in English, which would translate to shake your arms. We don’t have a separate word for feet versus legs. We just have one word called nega. Like put your shoes on your Russian feet would sound in English, put your shoes on your legs. And things like that.

Kirill Eremenko: So, and that is the same in Czech language and Polish language in as far as I know in some other Eastern European languages there’s just no separate word to distinguish between arm versus hand, foot versus leg. And I think maybe like, I’m not sure you might comment on this like, is that the same in Spanish or not? But like it just shows that’s like there are certain limitations as you say. And that also as I can see how you’re making this a point that in data science as well, the more tools you know Python, R, Excel, Tableau, whatever it is, AWS, Scala, Spark, the more opportunities you have to think of different solutions.

Morgan Mendis: Yeah. I think that that’s the key thing is also culturally relevant. Right? I don’t know if in Russian it’s popular to be obviously I think you do shake hands obviously how popular it is called truly to think about these things. I was talking to one of my colleagues yesterday about the idea that in some languages they don’t have, like they don’t have left and right. They only think in cardinal directions.

Kirill Eremenko: Interesting.

Morgan Mendis: Right? So yeah, when they talk about something they use like the idea of East to West, right? To describe progression or as time booze. Right? Or in a story. And it’s like really interesting to think about that. And I think it also goes into also like what’s most relevant to a given culture, to a given group of people. Right? Like we know that in some cultures, some native American cultures in Alaska, they have multiple, they have over 10 words for snow, right? To describe all the different types of snow. Right? And for as English speakers, we’re just like “Hey, there’s snow.” You get snow. Right?

Kirill Eremenko: Yeah.

Morgan Mendis: I actually am wondering, I’m like, I wonder what the Creole word is for snow right now.

Kirill Eremenko: Yeah.

Morgan Mendis: Because I don’t know how often they get to see it.

Kirill Eremenko: Yeah. That’s right. Wow. Wow. Very cool. And in addition to like the words or use cases in the case of data science, so for different languages, the programming languages in this case, in addition to that variety, that enriches your problem solving abilities. It also enhances the neural pathways in your brain. If you keep speaking in Spanish and then in English you’re going to use different neural pathways or slightly different neural pathways. The greater the variance between the languages, the greater you’re going to have to engage your brain. Sometimes like learning a language, sometimes I’m sitting there, my head actually hurts because I feel that something is changing. I have to overcome these long neural pathways and new ones have to be formed for me to think faster in your language. Same thing with programming languages. Same thing with all these tools that we use. The more than we use, the more neural pathways I believe your brain going to develop in order for you to use them faster, and that’s going to help you, aid you in coming up with solutions faster as well.

Morgan Mendis: Yeah, no. Honestly, that’s I think some of the most exciting thing if you’re a forever learner is that you know when you’re struggling that, “Oh wait, things are changing in your head.” right? You’ve got to maybe throw out this, this previous limitation because there was no connection points though that neural pathway, right? There was no connection point, but now because you’ve added in this language or you’ve added in this concept, it allows you to rethink things and it’s like, “Oh wait, now I can send information down this way. I can send my elec. Right? I can send those neural electrodes down that way so I can think of it this way.”

Kirill Eremenko: Yeah.

Morgan Mendis: Oh, I think that that’s really, really exciting. And I mean that’s why I think learning languages is a crucial thing to help people conceive of new problems. And as you’re talking about like in Spanish, one thing that I’m always kind of reminded about when I’m speaking with a native Spanish speaker is sometimes my expressions of like, “Oh me encanta eso.” They’re like, wait, why is it that you don’t have such a strong, affection towards such an item or affinity towards something. And I don’t realize it because in English we say love to anything. “Oh I love this coffee.” But you wouldn’t say that in Spanish or in French because they’re like, that word has a lot of significance. It has a lot of meaning behind it and it makes you start thinking a little bit more about the word choices.

Morgan Mendis: And it’s actually sometimes interesting to hear people multilingual people speak because I think that they have a different perception of words potentially. And they’re more sensitive times than people who are, only speaking one language because they’re like, “This is the word that we know and it’s common understanding.” But people are like, “No, it’s not that common.” It’s all about context and place. And it’s the same way with data science tools. For example, me using Python might be advanced to somebody who’s only using Excel. Right? But me using Python might be, trivial to somebody who’s only working in Scala or who knows, only working in Julia down the road or…

Kirill Eremenko: Yeah. Yeah, I totally get what you mean. Yeah, it’s all very relative and the more you explore, the more variety, the more things you can see about your past experiences, your future experiences, how others are working on tools. I think it’s a very exciting, exciting thing to constantly be learning. And it’s great to hear from you, like, because obviously at the start of your career, it’s very exciting to be learning. But in your situation, I know you say you don’t think of yourself as a highly advanced data scientist. I really think of you as a very advanced data scientist. I think from what we discussed on this podcast, our listeners will agree on. It’s very inspiring to hear that coming from you that even once you’ve accomplished so much and that you’re now pursuing your dreams and passion projects and things like that, you’re still very excited about learning. So that never stops. And I truly admire all of that. So thank you a lot for that inspiration in just the way that you approach data science yourself.

Morgan Mendis: I appreciate the compliments, but I hope that I can live up to them.

Kirill Eremenko: For sure. And so unfortunately we’re running out of time. And just before we wrap up, I wanted to do one more shout out to this amazing undertaking that you’re doing in Haiti, Ayiti Analytics and how you’re changing or bring up this new generation of data scientists who are going to be changing a lots of things locally and in the world and bringing good to the world. How can people support this course? Is there anything that our listeners can do in order to participate or just spread the word about what you’re doing in Haiti?

Morgan Mendis: Yeah, definitely take a look at ayitianalytics.org, which I’m sure Kirill is going to have posted on this podcast description, but we’re definitely looking for collaborators in the U.S., in Europe, in Africa especially as well. We’re really trying to advance data science in the country and we want to find other practitioners, other advanced data scientists who are willing to give back in terms of their time to help others grow, especially in giving opportunities for people who are potentially going to interact with exciting data to help develop their own country in a way. And so we’re definitely looking for collaborators, mentors, especially if you speak Haitian, Creole and French. We’re also looking for people to help us in terms of translating some of the content that is online into the local language.

Morgan Mendis: But yeah, if you just want to get involved and talk with some of the students and other practitioners in our group, that is also, very welcome. So we hope that people are interested and they want to collaborate and they want to give back. And I think that that’s a key thing is that we’re an organization that wants to use data science for the benefit of everybody and we are really looking for collaborators to come in and try to help us with that.

Kirill Eremenko: Fantastic. Thank you so much for sharing. And I will share the website link in the show notes, but just do mention it here. It’s Ayiti Analytics and Ayiti is spelled A-Y-I-T-I. As you mentioned it’s a play on words and how Haiti pronounced, right?

Morgan Mendis: Yeah. The phonetic pronunciation.

Kirill Eremenko: Of Haiti. Okay. Awesome. So ayitinalytics.org if you want to check out. Morgan, once again, thank you so much. How can our listeners get in touch with you and you said LinkedIn, right? Is the best way to get in touch with you?

Morgan Mendis: Yeah, and if they want to also send me an email directly, they can also hit me up at morgan.mendis@ayitianalytics.org.

Kirill Eremenko: Mm-hmm (affirmative). Awesome, awesome. So definitely people get in touch. Morgan, you’re doing some fantastic work. Before I let you go, one more final question for you. What’s a book that you can recommend to our listeners to help Inspire their careers?

Morgan Mendis: I’m going to hit you back with two books actually.

Kirill Eremenko: Nice.

Morgan Mendis: One book that I think transformed my life in data science early on was a book called Manga’s Guide to Databases.

Kirill Eremenko: Mm-hmm (affirmative).

Morgan Mendis: So go out, buy this book, give it to, you can give it to somebody who’s in middle school. They can learn databases, they can learn SQL. They can understand how it works from this book. This is how I learned SQL. I learned SQL on the fly at an internship via this book, 100% believe in it. It’s really, really easy to learn and right. It’s almost for any level of reader. The other book is, I really want to push some human centered design, so it’s The Design of Everyday Things. It’s another great book to give you an understanding into how you can use design principles in whatever you’re hoping to do. Whatever it is, you can always benefit from, interacting with your clients or interacting with your stakeholders and really understanding how you can afford them greater value. So The Design of Everyday Things.

Kirill Eremenko: Fantastic. Thank you so much. So Manga’s Guide to Databases and The Design of Everyday Things. Morgan, once again, thank you so much for coming on the show and sharing all your insights, inspiration with us and also just showing us what’s it like to live the life of an advanced data scientist. I think we can all aspire towards that and it’s really cool to see how you follow your dreams and hopefully those of us who can help, I insist we will get in touch and assist you in pursuing your passions and making a difference in other people’s lives in data science. Thank you so much for being here today.

Morgan Mendis: Thanks for having me Kirill.

Kirill Eremenko: So there you have it ladies and gentlemen, that was Morgan Mendis and thank you so much for being part of our conversation here today. I know we went a bit over but I hope you enjoyed it as much as I did and got as many useful takeaways from the conversation. For me, the biggest takeaway in terms of contributing to society was of course what Morgan is doing with Ayiti Analytics. So if you can help in any way, please get in touch. I’ve already spoken to Morgan. I’ve told and, as I mentioned on the podcast, we’re going to supply as many SuperDataScience courses as needed, absolutely free of charge to this cause in order to help get Morgan’s team and the people that they’re coaching up to speed with the concepts of data science, machine learning, artificial intelligence.

Kirill Eremenko: We just counted this out to the podcast. We have about four courses that we already have in French and we are going to be supplying these to Ayiti Analytics in order for them, as they need these courses, in order for them to help bring people up to speed with data science and make an impact in the world. So get involved if you can. The website is ayitinalytics.org and we’ll definitely share that on the show notes and in terms of technical aspects. My third takeaway was Airflow and the whole concept of this advanced ETL tool. I was very excited to hear about that. In fact, we spoke with Morgan after the podcast and I invited Morgan to come and present at DataScienceGO 2020 and he agreed.

Kirill Eremenko: So Morgan is going to be presenting at DSGO 2020. We ready to have the topic, it’s most likely going to be Airflow and visual diagnostics for machine learning is going to be an advanced workshop, a workshop for the advanced track of our advanced practitioners. So if you can make it to DataScienceGO, you will get to see Morgan there and perhaps even attend his workshop. The dates are 6th, 7th, 8th of November. So as usual you already by now you can get your tickets datasciencego.com.

Kirill Eremenko: So that is our episode for today. As usual, you can get all the materials that we mentioned, any links to Morgan’s LinkedIn to his email, to the projects that he’s working on. Also to the position that we talked about the VP of data science, you can get all of those things at the show notes at www.superdatscience.com/321. That’s www.superdatascience.com/321. Check it out and get what you are most interested in, what you are most curious about.

Kirill Eremenko: And of course hit Morgan up, connect with him on LinkedIn. It’s great to have advanced data scientists in your network, people who you can come to with questions, ask about their careers or just follow their careers and see where it takes them. It’s always inspiring to see what an advanced data scientist, how they choose to structure their career going forward.

Kirill Eremenko: On that note, once again, thank you so much for being here today. If you’d like to meet Morgan, check out datasciencego.com and I look forward to seeing you back here next time. Until then, happy analyzing.