Kirill: This is episode number 351 with Associate Data Scientist, Stratos Hadjioannou.
Kirill: 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, and now let’s make the complex simple.
Kirill: Welcome back to the SuperDataScience Podcast, everybody. Super pumped to have you on the show. Question, have you heard Rico Meinl’s episode? So most recently, Rico was on the podcast in episode 335, and before that he was on episode 123, in January 2018. So today we have a guest, Stratos, who underwent a really cool transformative journey.
Kirill: He actually heard Rico’s episode 123, so the first original Rico’s appearance, and heard Rico’s story of how Rico got on a plane in 2017 and flew to Germany to attend DataScienceGO, our conference there, and how that changed his life, and who Rico became after that. After listening to that episode, Stratos did the same thing. He’s in the UK. He got on a plane from the UK, flew all the way to San Diego to attend DataScienceGO, and that also transformed his life. Now, after pursuing the goal of getting a job in data science, listening to the podcast, attending DataScienceGO, doing courses, he’ll explain exactly the courses he’s doing, he finally got a job in data science. So congratulations to Stratos for persevering, for following his dream. Now he’s an associate data scientist at the National Grid in Warwick, United Kingdom. How cool is that? Very exciting.
Kirill: In this podcast, you’ll learn about how and why he combined online courses, and which ones, specifically. We will talk about how to create a data science ecosystem for yourself and put yourself in that ecosystem to continue growing and thriving, even if you’re not currently doing data science but you really want to be. We’ll talk about short, mid, and longterm goals and how to set those for yourself. And we’ll talk about the triad of successful job applications in data science, something that has worked for Stratos and surely can work for absolutely anybody applying for data science, three things to look out for. And as well, you’ll get some interview tips from Stratos.
Kirill: Very exciting episode, very pumped, and on that note, let’s jump into our amazing episode. Without further ado, I bring to you associate data scientist, Stratos Hadjioannou.
Kirill: Welcome back to the SuperDataScience Podcast everybody, super excited to have you on the show. Today’s guest is calling in from the UK. Stratos, how are you going today?
Stratos: Amazing, Kirill. How are you?
Kirill: I’m doing very well thank you. It’s raining today in Australia. Is it sunny in the UK?
Stratos: No, no. It’s never sunny in the UK. I don’t know. Let me just look outside. The sun is just coming up, but I think it will just be a cold one, but no rain.
Kirill: Yeah, we’ve got a big time difference, right? It’s like what? Almost 5:00 PM for me, and it’s 7:00 AM for you, right?
Stratos: Yeah. It’s just turning 7:00, yeah.
Kirill: That’s crazy. All right. What day is it today? Is it Monday, right?
Stratos: Monday, yes.
Kirill: Monday. Are heading off to work after this?
Stratos: No. I’m working from home today. I already told my manager that I have a podcast in the morning, but yeah, usually that’s the kind of time I’ll go to work. I tend to work early and finish early.
Kirill: Nice. That’s very cool that your job is flexible, with you being able to work from home.
Stratos: Yeah. I appreciate that for my company, but I think it’s a very common thing in the UK. I think it’s something that we have in the UK that, for my friends in other places in the world, I don’t seeing it being that common. But in the UK, they’re very flexible with hours and understanding. So, yeah, appreciate that for the country.
Kirill: Okay. Very cool, very cool. Wow. You have a very nice and exciting job as a data scientist, congratulations. That’s so exciting, my friend. Well done.
Stratos: Thank you so much. Thank you so much.
Kirill: That is awesome.
Stratos: Yeah. As I said before, the podcast is a huge part. It’s almost like I should congratulate you, as well.
Kirill: That’s fantastic. I love how this all unraveled. You contacted me on LinkedIn, what was this, like a year ago, in January 2019, right?
Stratos: Mm-hmm (affirmative).
Kirill: You just contacted me to say you’re excited, you’ve been learning a lot, you’ve got some data science opportunities coming up. Then in April, or somewhere around April, you finally got your first data science job. I think you started in July. It looks like you started in July with that job. So that was really cool. And you just messaged to say, “Hey, thanks a lot.” I really appreciated that. Just looking at it now, I really appreciate you saying hello and just thank you, without wanting or needing anything. Of course, that was a really cool opportunity to bring you on.
Kirill: This is a very exciting success story, of how you went from not knowing data science at all to now being a data scientist. So tell us, where did this all start? How long ago did you decide to start learning data science, and why?
Stratos: Yeah, so it’s interesting because when I started learning, I didn’t start learning data science. I just accidentally fell into data science.
Kirill: Yeah?
Stratos: I was on my last year at university. That was in 2018, about March time. So probably two years ago. Yeah, exactly two years ago. Then I was doing a chemical engineering degree, and I kind of started having kind of a passion for programming, or I should call it automation, or using programming to doing things better. I played around with a bit of VBA when I was doing my placement year in PepsiCo. Then I was reading a few things about Python, that’s the language you should go for. No particular reason. Again, no data science. So I bought a course on Udemy on Python, actually from one of your previous guests, Jose Portilla.
Kirill: Oh yeah.
Stratos: I started getting on with it, and I thought it would take me about a month or something. Then within a week, I was almost done by it. I found it so fascinating and how exciting, you could just manipulate things, data and all of them. You know how Udemy is with their recommendations and things like that. They started recommending some more courses, and some of the courses were actually your courses. They recommended to me the A to Z Machine Learning course. They also recommended to me another course from Jose as well, which was on data analysis.
Stratos: So I took the data analysis course first because machine learning stuff sounded a bit too scary for me at that time. Throughout the course, I started getting the hang of, “Oh, okay, so you can use pandas with Python and manipulate data, and you can access Excel spreadsheet. You no longer have Excel spreadsheets. You have this data frame structure, where you can do whatever you want,” and all of this. Then I started going into the space of visualization and using Matplotlib and seaborn.
Stratos: I don’t know how much detail you want me to go to, but what I decided then is I actually like this space. So I kind of took a step back, did a bit of research on what areas you need to learn. I then started doing something that I think worked quite well. I started combining courses, instead of just doing … I used the method that you use in university, how you would probably have lots of courses simultaneously. It’s not like you have one course and then next one.
Stratos: What I did, I took your course, the A to Z Machine Learning, because I decided I wanted to learn machine learning. I took the A to Z Machine Learning course, which was very practical, full of examples, amazing intuition, videos by yourself. Then, because I had that extra math knowledge from my background in engineering, I also wanted to dig a bit further. In combination, and you might not know that, your course maps very nicely with Andrew Ng’s course, from Coursera.
Kirill: Which one?
Stratos: The machine learning course, the very well known machine learning course. What I mean they map very nicely is, you guys start with linear regression, if I remember. He starts with linear regression. You switched to … So it’s almost like you go how I did it, but obviously it’s up to the listeners to do whatever they want. I would start with Andrew Ng’s, which is very technical, very mathematical, but maybe lacking that application. Then come to your course, listen to your intuition videos, kind of confirm that, yep, I understand it. Then bam, go into the practical. That kind of got the ball rolling.
Stratos: Within a month, I covered all of your course and Andrew Ng’s course. Yeah, and then continued on. I continued the same logic with deep learning because Andrew Ng had also a deep learning course and mapped it to your Deep Learning A to Z. So it worked quite nicely. I didn’t know if you knew that about your course, but your course is-
Kirill: No.
Stratos: My understanding is you wanted to create a course that could be intuition based, people will kind of get on hands on, but it also works for people are also interested in the mathematics. I feel like your intuition videos are very nice to confirm your knowledge, without needing to dig further and derive those equations again, if that makes sense.
Kirill: Mm-hmm (affirmative), yeah.
Stratos: Yeah, so I found that very fascinating.
Kirill: That’s very cool. Sounds like we need to partner up with Andrew Ng and create a course together. That would be cool.
Stratos: Yeah, there is also some nice books that I was reading. I think it was the Hands On Machine Learning. I can find exactly the title for it. Again, that book starts off with the basics of machine learning, linear regression, all of these, and then it proceeds into Tensorflow and deep learning. You can have that book on the side while … If you don’t understand something from the two courses, or if you want to learn a bit more, then you always have the book to reference.
Kirill: Gotcha.
Stratos: Yeah, that was quite a nice experience. I learned quite a lot from that.
Kirill: Interesting. Tell me this, because those courses are massive. For instance, the Machine Learning A to Z course is 40 hours long, right? It’s huge.
Stratos: Mm-hmm (affirmative).
Kirill: So what I’m wondering is, how do you keep up the motivation and also … I don’t know. How do you supplement that aspect that you don’t have a full-time data science job where you would apply these things? So you’re learning, that’s great, but then you go to your work and you’re an engineer. You’re doing something completely different. So how do you keep that ball rolling? How do you keep the momentum? Where do you get those hands on applications, to keep you excited, to show you how you can actually apply this knowledge in the real world?
Stratos: Yeah, that’s an excellent question. I have to make a disclaimer that I started learning before I actually started the job. It obviously continued while I was working as an engineer, but my main kind of biggest learning was during summer holidays. To your point-
Kirill: So you started before you even started your engineering job?
Stratos: Yeah. So I graduated in June. Then around July was about when I started doing all of this that I just described. I was starting a new job in September, so it kind of followed through all the way until I got a job in data science.
Kirill: Gotha.
Stratos: The first thing I did, which you might be thinking that I’m copying Rico here, but I think it’s your machine learning course that has … One of your courses has Rico’s podcast on one of the notes in Udemy. So I watched that. Actually, that’s how I found out about the podcast. I went back and re-watched the previous episodes, and he-
Kirill: How many episodes have you listened on the podcast to?
Stratos: Oh, I’m up to date now.
Kirill: All of them?
Stratos: Yeah, yeah. I think I haven’t listened to the 341, the one that just came out, basically.
Kirill: No way! That’s amazing, my friend. That is crazy.
Stratos: Yeah.
Kirill: You’ve listened to 340 episodes?
Stratos: Yeah. No, the podcast is … Actually, yeah, to your question, that’s one way to keep going, is the podcast. Having something, especially the podcast because it comes every week. It’s almost like you’ve got seven days. Yeah, you’re bound to lose motivation on some of them, but at the least, every seven days you’ve got that motivation to get you back. On the podcast, you hear people progressing, so you’re like, “I can’t stay where I am. I need to progress as well.”
Kirill: That’s true, yeah.
Stratos: So the podcast was a big one. But yeah, coming back to Rico’s podcast. I heard about the DataScienceGO. I just went on the website. I saw it. I was debating, should I go? Should I not? Is this for me? I’ll be honest, it looked like it wasn’t for me, in the sense that, who am I? I’m not a data scientist. I only just started learning a few things. I don’t even know what machine learning means. But then I decided, “You know what? If Rico did it …”
Stratos: I decided to just book a ticket and go to the US for two days and come to DataScienceGO, which looking back now, it was probably the best choice I could have done. I’m being honest here, it’s not that because I came to the conference I now have a job. It’s not that, but it’s the mentality. It’s almost like that milestone: okay, if in October I need to be at this conference, then until October I need to upscale myself. So I don’t have any time to lose. Then obviously after the podcast, with all that pumping that you leave the podcast, you’re kind of … Yeah. So I think that was a big thing that kept me going. What do they call it? What did Rico call it in the second phase?
Kirill: Radical commitment.
Stratos: Yeah, something like that. Basically putting something in the diary that you know you just can’t miss. You paid for the ticket. You paid for the airplane ticket. I mean, unless you’re somehow insanely rich and you don’t care about money, you might as well just go. So that was one.
Kirill: Let me clarify this. So what Rico did, and he’s been on the podcast. Actually, he was on the podcast recently, again.
Stratos: Yeah. I listened.
Kirill: When was that? He was again on episode 335, but the first time was a year before that. Or maybe more than a year. But basically what Rico did, a crazy thing, he, from Germany, booked a ticket to come to DataScienceGO in the US, in San Diego. I think this was the first one in 2017. Just for that. He just flew there, came to the event, and then flew back. That completely changed his life. So you’re saying you did the same thing but in 2018, right?
Stratos: Yeah, and from the UK, not Germany.
Kirill: From the UK. That is so cool. Did you get to meet Rico?
Stratos: I did. Well, not in person directly, but I did speak to him after the … Yeah, you can say yeah I did. I spoke to him briefly after he spoke, and I also asked a few questions. But no, sadly I-
Kirill: Was it cool? Was it cool to see that person that inspired you to fly across the Atlantic, to see him in person?
Stratos: Yeah. I think what was more inspiring was his talk during the podcast.
Kirill: Oh yeah.
Stratos: Sorry, during the conference.
Kirill: During the conference?
Stratos: Yeah, it’s the fact that he was there. It’s almost like, thinking through the timeline, that a year ago he was just this student that flew here, and then he was there, standing with all that confidence and spreading the word out there. Yeah, it was very fascinating, to see him out there. Congratulations to him for everything he’s doing.
Kirill: That’s awesome. Well, coming to the event or events is another way to supplement your learning. Listening to the podcast, coming to events, and all those things together keep you going. It’s very inspiring to hear that you were able to create this kind of ecosystem for yourself, you know? I don’t know how many people around you were studying data science as well, but you tapped into the SuperDataScience community, the DataScienceGO community, and by doing that, you kept yourself propelled and motivated to go forward. Is that about right? Is that how you see it?
Stratos: Yeah. I think, yeah. I’ve never heard of it this way, but yeah. Putting it into an ecosystem, yeah. That’s correct. It’s very important, I think, to keep having some sort of a short-term, medium, and longterm goals. You need to know why you’re learning what you’re learning. You can’t just learn for no reason. I mean, yeah okay, you can learn for your benefit. I get that. But you need to be aiming for something. “Oh, I want to learn this because I think this will help me reach that goal.” If that makes sense. That would be my advice to everyone, to always have a goal, always know why you’re doing something.
Kirill: Okay, so tell us about your goals. What were your short, medium, and longterm goals when you were learning data science?
Stratos: The short-term is I just knew that any kind of job that I will be doing, engineering or not engineering, I knew that I needed to know programming. I can’t just stay with Excel or whatever, with Word and all this. I need to be able to do things faster, do things better. I think that knowing programing is something that everyone should pursue. I’m not saying everyone should be an expert, but having the ability to program, doing things faster, automating, it just takes the boring aspect out of your job and makes everything more interesting. So that was kind of the short-term goal.
Stratos: The medium and the longterm, kind of it’s a bit of both together. It started off I just want to influence data science everywhere, but then after DataScienceGO, when I came back from the US the first time I went to DataScienceGO, I said, “Yeah, okay, now we’re shifting goals.” My medium/longterm, because I didn’t know how long it would take, I decided I want to be a data scientist. It was no longer a hobby for me. I’m spending a lot of time, of my own time, afternoons, nights, doing that. I might as well do it for a job, and I might as well be actually practicing it properly and in a place where my skills could be of maximum help. So that was kind of my goal.
Stratos: From there, as soon as I kind of put that goal into the books, that’s it. My learning became a lot more consistent, a lot more structured. I was looking on what is asked in the market, and I was learning it. That kind of got me going. From there, I had no kind of throwbacks. I wasn’t going back.
Kirill: Okay, gotcha. So short term goal, learn programming because you’re going to need it anyway. Midterm goal was you love it so much, you might as well get a job in a it. What was the longterm goal?
Stratos: Well, the longterm goal, and I don’t know how to define it, but whatever I’m doing, I want to be influencing data science. I like doing data science. I like producing nice charts, models, and things like that. But what I particularly get hyped about is when I show people who don’t know about data science, don’t know about programming, what it can do for them. I like the idea of going somewhere and kind of disrupting that organization or that team or whatever, from the concept of data science. “You know what? You no longer need those Excel spreadsheets. You can display them in tableau. You can run a model that gives you predictions. You can save so much time.” That’s my longterm goal. Whatever I will be doing in I don’t know how long, I want to be influencing data science. I want to be the person that will go in, and after I left, data science has exploded. I don’t know if I’m making it too generic, but that’s kind of my longterm goal.
Kirill: Okay. So you want to be spreading data science.
Stratos: Yes.
Kirill: Across different companies. Gotcha. Very exciting. That shows a lot of passion, that goal. That’s something very admirable, admirable to have that kind of goal. Very cool.
Kirill: Take us a bit back. You did all this learning. You went to DataScienceGO. How and when did you start applying for data science jobs?
Stratos: When I came back. Do you remember … I mean, I’m assuming you do remember. At the end of the DataScienceGO, you gave us these-
Kirill: Which one? 2018 or-
Stratos: 2018. You gave us this kind of talk, where you said-
Kirill: Yeah, yeah, yeah.
Stratos: Close your eyes and right something down, or whatever. Anyway, I remember I had this small notebook that you were handing out in the conference. When I was on the plane, 10 hour flight, which-
Kirill: For context, the exercise was we needed to get up, we needed to make the sound of victory or something like that, feel really empowered and passionate. Then imagine success, what you want to accomplish in the next 12 months. Then the objective was to sit down and write down your top three goals, right? Was it top three or top one?
Stratos: I mean, I wrote only one, but it might have been top three.
Kirill: No, top one.
Stratos: It might be top three.
Kirill: Okay, so it was top one action you’re going to take when you get home. That was the thing. So you’re on the plane. Sorry, let’s get back to your story. You were on the plane.
Stratos: Yeah, so from that moment, when you said imagine success, I might sound cheesy, but kind of I felt it there that my success, for me, at least within the next 12 months, was me standing somewhere, anywhere, and being a data scientist. Because I wasn’t at that point. I wasn’t applying. I was just a self-learner. So that was kind of locked in as a success.
Stratos: Then on the plane, I just started thinking of what can I do? Because I was clueless. I just knew I’m about to leave this very kind of prestigious engineering firm and start going into probably one of the most competitive fields, having an engineering degree, which let’s face it, is not … I mean, you hear mathematics degree, physics degrees, PhDs. It’s a good degree, but it’s not the best.
Stratos: So my first plan was, okay, let’s see how other people do it. It was evident to me that the way to get out there is to physically start shouting about yourself. The first thing I decided to do on the plane was I-
Kirill: Shout.
Stratos: Yeah, shout in the plane and see if anyone is hiring. No, it was just literally what’s the best way for me to show what I’m doing? As you can imagine, the first thing that came to mind was LinkedIn. I’m not a particularly social media person. I probably haven’t changed my profile picture for five years. I just have them, nothing more. But I decided let me get out of my comfort zone and start posting. That was one thing I decided to do.
Stratos: The other thing is, as I said before, I need to start from somewhere. I can’t just go nuts, start updating my CV. I need to get very specific because I don’t have a lot of knowledge to kind of brag about. So I went back and did a bit of … Sorry, again, I’m still on the plane. I said I will sit down, read what’s out there in the market. That’s another thing, I didn’t know what’s in the market. Because coming from DataScienceGO, all I would hear is Silicon Valley, Silicon Valley. I’m like, “Yeah, I’m very far from Silicon Valley.”
Stratos: So I need to see what’s in the UK market. That was the first thing. More specifically, see what they’re asking for, what skills are out there, and obviously how do I compare to those. From that, start developing the corresponding skills so I can at least put them down on the CV, so when that first scan goes through … Because I was feeling like if I get to the interview, if I kind of meet their technical requirements, I feel that story and that passion, I would be able to get out there and hopefully it would be enough.
Stratos: Then the final thing I said, because I think that came from Ben Taylor’s talk, I need to get myself some applications. I can’t just say, “Oh, I took Carol’s course,” or, “I took that course.” I need to get myself some toy applications, even if they seem toy examples and things that nobody cares about. They need to be out there, so I can demonstrate that I don’t just know how to follow a course; I actually know some applications. So that was the three main goals.
Kirill: Like projects you mean, right?
Stratos: Yeah, yeah.
Kirill: Like a portfolio project.
Stratos: Yeah, portfolio or just find … To be fair, what I took from Ben Taylor’s talk was it’s not just making a project. It’s just, okay, you know data science; find something that you’re curious about and just have a play with it. That was my approach.
Kirill: Yeah, that’s really cool. That’s very cool. An example of something like that, a recent one I heard, I was listening to this one video, just briefly, of I think the CEO of Kaggle. They were describing how they had this one competition with some data sets there, and it was about people doodling, like drawing things, drawing animals or whatever, and the algorithm had to detect what animal. Was it a lion? Was a hippo or an elephant? Whatever else they were drawing. So people would not only do that, but they would go an extra step and they would try to understand, depending on your cultural background, are you more likely to draw the animal clockwise or counterclockwise? How is that distributed by country? Crazy stuff, right?
Stratos: Yeah. That sounds-
Kirill: Like you say, whatever your passionate about, use data science and come up with some insights.
Stratos: I’m a big fan of Medium, and you hear some people just putting some random dataset and some random projects out there. You’re like, the only way you could have come up … The most recent one I’ve seen, and I was like, whoa, that’s fascinating … Are you familiar with the application Tinder?
Kirill: Yeah, of course.
Stratos: Okay, I’m not that familiar. I haven’t used it that much. But this guy had a tremendous amount of data from his Tinder account, of his friend’s Tinder account. So he asked-
Kirill: “His friend’s Tinder account,” in quotations marks of course.
Stratos: Yes, yes. Yeah, it is not him, yeah. And it was amazing. If I remember correctly how the application works. He did this diagram of how he started, with how many likes he did, how many super likes, how many of them ended up being liked back, how many ended up in conversations, how many of them replied, no reply. Basically, towards the end of the chain, how many actual successful dates and something like that. Some crazy metrics. It just shows the application of data science.
Kirill: Wow.
Stratos: As long as you have some data, you can just get some insane [crosstalk 00:30:22] out. Yeah.
Kirill: Interesting.
Stratos: I bet you, actually, if that guy was a publishing a dashboard or something, Tinder would have picked it up and put it on their application as a dashboard. Wouldn’t you want to see what’s going on, to automatically have your data?
Kirill: Yeah, yeah. Yeah, there you go. If he wants a job as a data scientist at Tinder, he’s got it, right?
Stratos: Yeah.
Kirill: He just needs to send them this link, and they will be like, “Oh my god, he loves our product. He loves data science. What else can we want? We got to hire him.”
Stratos: Yeah, that’s the thing. He doesn’t even need to send his CV.
Kirill: Exactly. It’s interesting, actually we are hiring for … What are we hiring for? For like a product coordinator at SuperDataScience. I was reviewing these three applicants recently, and three of them … This story, it’s not about data science applications of course, but it’s still relevant. So these three people … A lot of people applied. A lot. We got, I wouldn’t say millions, but we got quite a few applications. Then in the final round, there’s three people. I get these three CVs. Actually three profiles of people, like three emails, one about each person.
Kirill: So I read the first one, the second one, the third one. Then for the third one, I’m reviewing his profile, and I noticed … I’m reading his CV, and I’m like, “Hold on, I read this CV for the second person, for the third person, but I don’t remember seeing the CV for the first person.” So I went back to look at the first person, the email I got about that person.
Kirill: I realized that there is no CV. There is just I think there was their LinkedIn and a website that they put together, that describes them, like what they’re capable of, what kind of designs they’ve done, what products they’ve created, and things like that. So it’s kind of, like you said, a portfolio project. I never even got a CV. That person doesn’t even have a CV, In the end, that turned out to be the best applicant, and we ended up hiring them. You don’t need a CV these days. You just need to demonstrate that you can do things.
Stratos: Yeah, and do things differently, I think. That’s what I get out of that story, is if you are kind of innovative enough to show exactly the same thing but in a different way, that’s what will stick out to the recruiter.
Kirill: Exactly. That’s a really good point. So let’s recap, before we get too far away. Let’s recap on your I’m going to call this the triad of successful interviews, right?
Stratos: Yes.
Kirill: What was the first, second, and third items?
Stratos: The first one was … I forgot. The first one was-
Kirill: LinkedIn.
Stratos: Yeah, make yourself-
Kirill: Have a LinkedIn.
Stratos: If you want to generalize it more, make yourself visible, that you are doing what you are doing. The second one was I think … Was this the second one? Anyway, basically search the market. Make sure, know what jobs are out there, know what you want to get, and ultimately what they are asking for, so you can develop in those areas. The final one was don’t just limit your … Given that you’re just a self-learner, don’t limit yourself to just courses. Start putting some applications together.
Stratos: Let me just say that it’s not just about … Obviously what we just talked about, yeah, it’s beneficial for your [inaudible 00:33:46]. But it’s not just that. It’s also like a self-confirmation, that you actually like this space. For me, if you can spend your Saturday looking through some data from Tinder, let’s say, instead of doing something else.
Kirill: It doesn’t have to be Tinder. It could be Airbnb. It could be Uber rides. It could be your recent google searches. Whatever. There’s so many data-driven … Like your Netflix movies that you watched. Whatever comes to mind.
Stratos: Exactly, yeah. If you are willing to spend your nights, your afternoons, or whenever, whenever is a free time for you, to do that, just for fun, then it’s almost like a self-confirmation. Yeah, that’s where you need to be.
Kirill: Okay, gotcha. Great. So the triad is LinkedIn/make yourself visible, know what jobs are out there and what are the requirements, and number three, have a portfolio of projects that will talk for you.
Stratos: Mm-hmm (affirmative).
Kirill: Okay. All right. Very cool. So you made yourself visible. Eventually did you find the jobs you wanted to apply for? Did they find you? How did you go from there?
Stratos: Because I already had a job, I was a bit picky. I didn’t want to just go crazy and start shipping CVs all over the place. So I became very, very picky on the applications. Yeah, I did find a few applications that looked like … I wanted something that looked entry level, but also had not just entry level but also some development opportunity. Because I knew that if you were to put me in the data science spectrum, I need at least six months for me to understand how the industry works, to fill out those gaps that you develop as you go through the self-learning experience. So I did end up finding a few opportunities like that, mainly through LinkedIn.
Kirill: And you applied for them?
Stratos: Yes, yeah.
Kirill: Okay, and so how many did you apply for? How many did you hear back from?
Stratos: I think I applied to five. I know some of your listeners will be like, “Just five?”
Kirill: Just five?
Stratos: Who is this crazy person? Yeah. But I spent so much time in those applications that it almost felt like five each. So you can take that as 25. I think I heard back … The one I got immediately the job, but the other four I progressed to the interview stage.
Kirill: Wow.
Stratos: Yeah. Two of them I did the interview … No, let me start with the easiest one. Two of them I reached all the way and got offered. Which, one I took. The other two, I think I reached both of them, the interview. One, I got rejected at the interview. The other one, they were just not … I did the interview, no response. I already got the job here, where I am at the moment. I couldn’t be bothered, so I just … I don’t even know. Maybe they replied at some point, but yeah. For me, if someone tells me I’m going to come back at you in two weeks, okay, if it’s three weeks that’s fine, but if you tell me two or three weeks and then they don’t come back for like three months? It kind of puts me down. Why would I want to work for an organization that doesn’t even bother telling me, “You know what? We can’t come back to you at the moment. We need some more extension.” So yeah, I just left it.
Kirill: Okay. Very cool. So you applied for five. Four of them, you got interviews. That’s an 80% success rate to getting interviews.
Stratos: Yeah.
Kirill: Then two out of the five, you got offers. Meaning, that’s 40% success rate getting a job offer. That’s crazy, man. Congrats. That’s awesome.
Stratos: Thank you so much.
Kirill: Really exciting. To anybody who says, “You only applied to five? I know people who have applied to hundreds of jobs.” Well, the success ratio there is like 0.001. It’s much better to have a high success ratio and know that you’re applying for jobs that you really actually want yourself, that you’re passionate about. Yeah, it’s better to spend more time on one application and tailor it and really understand the company, understand their mission, understand how you can help, and have that laser-specific conversation with them. Rather than just sending this template email to hundreds of companies and hoping something … What’s it called in shooting? Spray and pray. You shoot all these applications, and you just hope and pray and wish that, “Oh well, hopefully somebody will reply, and then I’ll take that job.” You’ll end up in a job that you don’t love, anyway.
Stratos: Exactly, yeah. It pays off. It’s very tempting, when you are either desperate for a job or when you want a job, to just quickly update your CV so it looks generically okay and just spam it. Especially nowadays with LinkedIn, when some of them are just easy apply. You just click a button. Done. You applied. It could be your next job. So I think it’s very tempting to do that. I’m guilty myself. I’ve done it in the past, not for data science, when I was applying for engineering jobs. But it’s as you said, it comes down to learning the company, understanding whether it’s where you want to work for. Even if you do get the interview, if you didn’t spend that much time learning about the company, you’re very likely not to be successful because you will come off as just a random person who sent their CV, just because of the salary figure or because they just know the company because they’re a well-known company. I think it all pays off.
Kirill: No, totally agree. Okay. So cool. We probably won’t go too much into the interview process. Actually, yeah, let’s talk a bit about it. Was there a lot of technical questions on the interviews? Was it more behavioral? Is there anything you can share? Any tips you can share for people listening?
Stratos: I had, as I said, four interviews. All of them were very different. It goes to show how different [inaudible 00:39:54]. So I will just talk kind of generically how. One thing that is very common, you will think, “Oh, I need to know Python, or this.” Yeah, you need to know you are likely going to be asked to do some exercise. In one of the interviews, I was asked to do an SQL exercise, and that was on the spot. In another interview, I was sent some data three or four days beforehand, and I was asked to do some analysis, just go back and present my analysis to them. In another one, it was just like either do this exercise or just bring something to talk through. So whatever you’re applying for, expect some sort of an application.
Stratos: One tip I would say to that is just because you know Python and you put down Python, especially if they are asking for more than Python, let’s say they are asking for R, or they are asking for Java, whatever, it depends which company, but don’t be surprised if you go sit down, you’re amazing in Python, and they give you an introduction to R script to run through. It’s just for them probably to test whether you will be dealing okay with other languages.
Stratos: If you want a real-life scenario, I am known to know Python. All of my team are known to know Python, but because we have a historic model that was written in R, and now I’m working in that model, I now have to write in R. I’ve never touched R before. But now I have to learn it, and we have timelines. That’s just how life works. We can’t just change the model to Python because it’s what we know. So that’s one kind of thing. I know it sounds terrifying, but just be prepared for it.
Stratos: It happened to me, thankfully not with R. It happened with SQL. I knew a bit of SQL. When I sat down, I was expecting for them to ask me some very technical Python questions, and they just were like, “No, we just want you to connect to the database, query a few results, and do some group [inaudible 00:42:04].” Now that I know a lot of SQL, it’s like that’s nothing. But back then, whoa, okay. So it’s kind of a bit of a terrifying thing.
Stratos: More importantly, outside of the technical things, do expect to get some soft skill questions, specifically related to data science. That can be in a direct question, such as, “How would you deliver the results of a model to the execs?” Or, “If someone was going to give you an Excel spreadsheet to present something, what would be your best approach? Would it be a visual? A model? Which one would you use?” Some other questions you might get. Someone might describe their problem. Someone might come and say, “I have this kind of assets, and they’re all failing. What do I do?” That’s where they will ask you to kind of formulate the problem and kind of decide on how to approach it.
Stratos: Or, it could just be a presentation. That’s what was going on in one of my interviews. I was asked to present my code, and then I was given 10 minutes to present a presentation that was targeted to non-technical people, so someone who doesn’t know code, someone who just wants to know what’s going on. Those skills are very important. That’s what people are looking. At the end of the day, you can train anyone to become an expert in Python, but you need to have that ability to talk to stakeholders, pass on the message, formulate the problem. Those are the important things, at least for me.
Kirill: Wow, fantastic. I’m really glad that companies are testing that, now. Back in the day, when I was interviewing, it wasn’t a big consideration. But I think more and more companies are realizing that these soft skills are important in data science, at least as important as the technical. Because if you can crunch numbers and get the insights but you can’t communicate them, then what’s the point in that?
Stratos: Yeah. I think one thing that you will like, my manager says that a lot, but it’s the 80/20 rule. I think you had it in one of your podcasts, as well. That’s a very hard thing. What I mean by the 80/20 rule, for those who are not familiar. I mean, if you’ve got a piece of analysis, usually to reach to the first 80% of getting your message across, it would take you let’s say a day, or two days, if you do small time analysis. To get that extra 20% and make it amazing, you probably need two or three weeks. So getting the ratio right is incredibly hard, incredibly hard. If you want to do well in business, or in the industry of data science, you probably need to become an expert in that 80.
Stratos: The golden rule is get that 80, minimum viable product, as soon as you can. Get it to the customer, and if they want that extra 20, which most of the time they don’t, then put the time. Don’t put the time beforehand, because usually that 20 will be wrong, unless you ask your customer beforehand. I don’t know if that makes sense, but that’s a very, very key principle.
Kirill: Exactly.
Stratos: And I learned it the hard way.
Kirill: What do you mean? Like on the interview?
Stratos: No, when I started my job.
Kirill: Ah, okay.
Stratos: On my first problem, it was a very relatively easy problem, but I wanted to impress everyone. I wanted to do amazing. I found myself spending two weeks on a problem that should have taken me a day. That’s where I kind of was introduced to that rule. Since then, I kind of go by it.
Kirill: Fantastic. Wow. Speaking of starting, of first problems, can you share a bit about that? What was it like when you started your first job in data science? Was it how you expected it to be, or was it completely different?
Stratos: The first time, coming from self-learner, the first kind of month or second, it was just getting my head around that I write Python for a living. That was just a very nice feeling. Because you’re used to coming back from work, I have to do a bit of courses. It’s almost like you’re hiding away writing away your Python, and you’re feeling like you’re doing something illegal. Now you’re legal. You’re allowed to write Python and get paid for it. That was an amazing feeling.
Stratos: What was very good, and I really, really appreciate my manager for this, is when we started, the first thing we did, we sat down. And I encourage every manager, or everyone who is going to start coaching people to do that. We sat down and figured out, mainly based on my feedback from the interview, we sat down and discussed what are my weaknesses and what do I feel I need to develop at. Mainly technical, as in, “You need to develop your statistics. You need to develop your Python, your version control.”
Stratos: Once you do that, you do that first week or something, then you can tailor your projects around them. You can focus a bit more. You can ask for work that is more focused in those areas. What I’m trying to say is, when I started, I was a bit lost, but once we had that chat and I knew the areas that I had to develop, kind of that stress went away. I was like, “Okay, here’s what I need to know.” It was almost like I was coming back to my self-learning days, but now I was learning for a bigger purpose. I was learning it to apply to work.
Kirill: Wow, fantastic. Probably experiences in different companies are going to differ, but it’s still really great to hear how yours was. I think before the podcast you mentioned that the first three months or so were all learning for you, before you could actually start feeling that you’re fully working.
Stratos: Yes.
Kirill: Tell us a bit about that. Was that a frustrating experience? Or was that fun, that you were actually learning for work?
Stratos: It was a bit of both. It was fun when it was working. It wasn’t fun when it was not. I think what I would struggle in is me not being from a mathematics or a physics … Well, let’s stick to mathematics and statistics background. Sometimes I was getting frustrated with myself for getting the basics wrong. I’m talking very basic, like confidence intervals and even means and medians and things like that. I was just getting frustrated with myself for not getting it. So that was a bit annoying, but again, because you’ve gotten the job, you do the mistake once, you do the mistake twice, and you learn. The first thing I did, for example, is I went back and freshened up my statistics: how will you calculate confidence intervals, how you would you do hypothesis tests. So then I could kind of not make those mistakes again.
Stratos: Then in terms of the soft skills, it’s very hard going from staring at a laptop and hearing someone teaching you things and doing data science, to going out in the real world. I literally thought that … Okay, I’m not so delusional, I didn’t think that, but you want to think that you’ll go into the industry, you get a meeting invite, like, “Hey, I heard you were a data scientist. Could we have a catch up?” You sit down with the customer or the stakeholder … By stakeholder, sorry, that’s what we call them, I mean internal people. I’m part of the company. I’m not external at all. So someone will invite me, we’ll sit down, and I thought they’d be like, “I have this data. It looks amazing. It’s ready for you to split it and fit into the model. I just want the predictions.” Basically what you get in a course, where you get a nice data set. Maybe it’s a bit dirty, but you need to clean it.
Stratos: I didn’t really get the fact that to even reach the point to actually have data, it’s like a marathon. That was very frustrating in the beginning. Now, it’s very rewarding because, again, I’m coming back to the marathon, but if you are very good at formulating the problem and making sure that you know what your customer wants, that marathon gets shortened and shortened and shortened. You go from long and long discussions and confusions to, “Okay, I can hear what you’re saying. I know what your problem is, and I know how to solve it. Do you have this data set? Or are there any …”
Stratos: That art I will call it, that art, knowing how to formulate a problem, how to go from a problem to data to solution, it’s very frustrating in the beginning. I mentioned it before the call. It’s very hard to practice as a self-learner. Now, to be honest, probably very hard to practice even if you come top universities and data science courses. I think that’s the on the job training. It’s very hard to practice.
Kirill: Yeah, fantastic. No, I completely agree.
Stratos: Lots of frustration there, but lots of reward after you kind of … I’m not saying I mastered it, but now I feel like I understand it a little more.
Kirill: Yeah, and you learn to appreciate it, as well.
Stratos: Exactly, yeah.
Kirill: Fantastic. Well, that is awesome. Thank you for such a great description. We’re running out of time on the podcast, but we could keep talking for ages about all this stuff. You’ve had such a really cool journey, Stratos. I’m very inspired to hear from you and very excited that you came on the show to share with your colleagues and friends and fellow data scientists. I think this is going to be, like you said at the start, a push for others to keep going. Once they hear your story, they’ll be thinking to themselves, “Well, I can’t stop now. I got to keep going. I got to move forward.”
Stratos: Yeah.
Kirill: Very cool. Very cool. Well, before I let you go, please could you share with us where are the best places for people to follow you and your career?
Stratos: I think the best way is probably LinkedIn, because that’s the only place that I stay relatively active then. I do encourage, if someone has any questions on how to get started and where to go from where they are, or any suggestions, I’m more than happy to help people. It kind of ties very nicely with my longterm goal I said before, where if I can influence data science, I’m more than happy to do it. So yeah, I’m happy for people to get in touch. I can answer any questions. Or even if people are around from the UK area, and they want to catch up in person. I’m more than happy.
Kirill: What city do you live in, in the UK?
Stratos: In Warwick. I don’t know if you know Warwick.
Kirill: Warwick. We have a Warwick in Australia, as well.
Stratos: It’s probably a lot bigger than Warwick. Warwick is a town. It’s not even a town. It’s a village.
Kirill: Oh, gotcha.
Stratos: But yeah. It’s close to Birmingham, if you know where Birmingham is.
Kirill: No, I don’t, but I’m sure people [crosstalk 00:53:31].
Stratos: Yeah. If someone is from the UK, I’m sure they will know Warwick.
Kirill: Yeah. Stratos, I got to make a comment for you. I’m looking at your LinkedIn, and it looks like last time you posted was four and five months ago, and then before that it was a year ago.
Stratos: Yeah.
Kirill: Looks like somebody got a job in data science and stopped posting.
Stratos: No, that’s actually something I wanted to talk about. You know how I mentioned that I wanted to get out of my comfort zone and do … It’s fine to get out of your comfort zone, but unless it’s something that you actually enjoy yourself, then … The reason I stopped posting is not because I got a job. It’s because I found it very stressful, in the way that I didn’t feel like I was getting much of it. I very much prefer to have someone come to me and we talk and I help them personally, rather than me trying to post. It’s just not me. I mean, I don’t know how to describe it. It’s not myself. It feels like I’m portraying a person that’s not me, if that makes sense.
Kirill: Okay, well-
Stratos: I do post when I find something interesting, but yeah.
Kirill: Well, I have a piece of advice for you, then, if you don’t mind.
Stratos: Yeah.
Kirill: You shared a bit of advice. Can I give you some, as well?
Stratos: Yeah, of course.
Kirill: Coming and talking is amazing. That’s a fantastic thing. At the same time, being not yourself is terrible, right? You want to be yourself. You want to be not necessarily comfortable, but you want to feel like you’re doing something that you enjoy, or you can enjoy with time.
Stratos: Exactly.
Kirill: However, people are not going to come and talk with you, or won’t be able to find you, or even know that they can talk to you, unless you somehow get on their radar. So my advice would be, I can totally understand if posting is not your thing, find what is your thing. There’s so much medium out there online. You can post. You can write articles. You can comment. You can reply on Quora, answering questions. That’s not posting updates. It’s answering questions. Ben Taylor was the number one AI influencer on Quora because he answered a lot of questions. You can record videos for YouTube. You can record audios and share them on SoundCloud. You can become a mentor on one of these mentorship platforms.
Kirill: Basically, find something. Not even posting. For instance, you could do projects. I’m sure you not only do projects for work, which are sensitive, but probably you still do projects for your own portfolio, or maybe some here and there you’ll do a data science project where you can desensitize things, and you can post those. Or you can publish them on GitHub or on Kaggle or on Tableau Public. Even without writing anything, you can publish things. Like I have a profile on Tableau Public, which I haven’t updated in a long time. I used it while I was creating the courses in 2015. I looked at it, and it’s got thousands of followers because I shared useful dashboards with people that I didn’t even have to write anything. People look at them. They can click on them. They can download them. People like them.
Kirill: My point is, I completely appreciate that maybe a certain type of medium is not yours and you don’t feel yourself, but find what is yours and do that. Because if you want to have those amazing conversations and meet your mentors and meet people who you can influence and spread the word about data science, they have to be able to find you somehow. It’s really hard. This podcast is going to help for sure, but don’t stop. Find other ways that you can help. Maybe start a podcast of your own. You never know.
Stratos: Yeah, and I appreciate that. Actually, on that, it’s something that I’ve been thinking through. You are right, and I think what I want to get out there is, yeah, one of the things you said is doing projects. I do do quite a lot of things outside of work, more projects. That’s kind of my thing. I’m the kind of person which, if I see a data set online, I’m curious what that looks like in a graph. I will just plot it. I do quite a lot, and I think I need to think through what’s the best way. I’m happy for the listeners to suggest what would be the best for them, but if there is any way for me to kind of get that going.
Stratos: I think that’s my strength, is I can help people and advise people how can they get that step and go and get a job. I can probably not advise them what’s your best model to use. I’m not that technical. But I can get you from I don’t know if I like data science to I’m passionate about it. So that’s the kind of area I want to focus on, my community if you like. That’s the kind of community I want to target. But in what way and what medium, I’m still searching for that. That’s some very useful advice, and I will have a think of that. Hopefully I’ll get some nice suggestions, but also I’ll try and think through what’s the best way for me to get that message out.
Kirill: Fantastic. Well, we’ll leave it at that. Stratos, thank you so much for coming on the show and sharing your story with our listeners.
Stratos: Thank you so much.
Kirill: All right.
Stratos: Thank you so much, and, yeah, we’ll speak to you soon. Thank you so much for the invite again.
Kirill: So there you have it, everybody. I hope you enjoyed this episode as much as I did. For me, the most exciting and inspiring part was the dedication that Stratos has. It takes a lot of courage and a lot of commitment to buy a ticket and fly all the way from the UK to Los Angeles just for three days, to attend the event that is going to change your career. But as you can see, Stratos didn’t make a mistake. Stratos actually made the right choice, and that helped him follow in the path of the career of Rico Meinl. How exciting is that? I wish the same to you.
Kirill: To wrap up this episode, as usual, you can get the show notes for our conversation at www.superdatascience.com/351. There you can get the transcript for this episode, any materials that we mention, and of course the URL to Stratos’s LinkedIn and places to connect with him. Highly recommend connecting and staying in touch. He will surely be happy to answer any questions you might have about interviews, about creating a data science ecosystem, about courses, about conferences, about podcasts. Get in touch. Stratos sounds like an amazing guy who is going to be able to help you, whatever your questions are.
Kirill: On that note, thank you so much for being here today. I look forward to seeing you back here next time, in our next amazing episode. Until then, happy analyzing!
