Inna Saboshchuk switched gears from the world of psychology and academia to become a data scientist in Madrid. After completing her Ph.D., she knew she would need Python and data visualization skills, so she chose IE’s Exponential Learning Data Science Bootcamp for its employer connections and solid curriculum. See what she’s up to today, and Spoiler Alert: Inna’s new boss at Nextail Labs was in the audience at IE’s Demo Day and saw her Capstone Project presentation!
What were you up to before IE Data Science?
A lot of people describe themselves as either quantitative or humanities-focused, but I always felt like I fit into both of those categories. I majored in psychology, which is actually incredibly quantitative if you take the research track. You need to use statistics in order to analyze the data that you collect. As I was working on my Ph.D. I realized that I needed to learn more statistics in order to find the most interesting insights, so I got my master's degree in statistics while I was working on my Ph.D. During this time, I started to learn how to program in R. Once I graduated and finished my Ph.D., I went to a data science bootcamp.
It sounds like you already had a statistics background, so what made you choose to do a bootcamp?
I had some programming experience, but almost all of my professional experience was in academia. You can definitely argue that academia is quite different from the business world. When I graduated, I moved to Spain and started looking for jobs. A head hunter told me about the IE Data Science Bootcamp specifically. When I looked into it, I saw what they were teaching and I thought that it was actually a great fit for me. They care about preparing you technically and they absolutely do. IE teaches Python and SQL, which most companies use today.
The other thing that was special about the IE bootcamp is that they don't just focus on programming skills. They also focus on communication skills. It's a part of the IE business school so they tie together business concepts and data science concepts well.
Did you research other data science bootcamps as well?
If I had known about Course Report at the time, I definitely would have! There are other data science bootcamps here in Madrid and there are also a lot of online options. I looked into those a little bit and it seemed obvious to me pretty quickly that IE was going to be the best fit. It offered that mixture of statistics and humanities that I fit into. It's a reputable institution and I liked the professors. I also knew that I didn't want an online bootcamp.
What was the application and interview process at IE like for you?
Because it was through a business school, they asked me for my transcripts and they were pretty thorough. They give you the option of sending over a letter of recommendation, which I did. After an in-person interview, they evaluate your background to make sure that you can handle a fairly quantitative course. They also evaluate your communication skills.
It didn't take me very long but it was very thorough.
What was your cohort like?
My cohort was quite small – there were only 12 of us. The number one thing that I loved about IE was that they just did a fantastic job selecting students for the class.
There were people in my cohort that were being sponsored by the companies that they already worked for as well.
Did everyone in your cohort have a PhD or Masters degree?
Not everyone. We were a fairly diverse group in terms of education and professional background.
Once you got to IE, what was the learning experience like?
This was by far the most intensive educational experience I've ever had but also extraordinarily enjoyable because of the people that were there with me. It was very hard and I had a lot of fun doing it.
It's intense and rigorous. You're there Monday through Friday at least until 6pm but usually longer. Often you're there until 8:30pm because they plan extra things for you after your whole day of classes. Additionally, there’s class on Saturdays. They organized master classes where they have people from big data companies to give information sessions and other things like that. Then, you also have homework.
What was the teaching style like at IE?
It's very hands-on. There are lectures where the professors explain a little bit of the theory and then immediately after that, you start coding. From day one you are coding. There is a math and stats class if you don’t have a mathematical and statistical background.
Throughout the majority of the course, you’re also working on a capstone project. Real companies come in and give you a data set and then people from those companies mentor you in working with that data set. You start this Capstone Project four weeks into the bootcamp before you've learned all of the concepts in the curriculum but you're expected to jump into it and start analyzing. The professors are also there to help you and they're expected to be very available to us and they really were.
IE Bootcamp is in Spain – is the course taught in Spanish or English?
The bootcamp, presentations, and all of the after class events are all in English. All of the companies at the demo day were international companies where you will likely be working in English.
Can you tell us more about your capstone project? What did you build?
During my bootcamp we worked with Nielson, Iberia, Amadeus, and Indra. Since I graduated, I've been to a few Presentation Days and each time they've had new companies. My group was working with the Nielson data. Essentially, they gave us panel data from different bars in Spain. The only thing they told us we had to do was develop a Shiny app, which is a program within R to develop dashboards for businesses. Other than that, they were very open with whatever we wanted to do. We had to clean the data and then we had to decide which models we wanted to work with and what story we wanted to tell with the data. We ended up doing a Market Basket analysis because we wanted to see which items were most commonly bought together and if we could help bars predict which items could be bundled. It was very interesting. They wanted us to focus a lot on modeling and at the same time, they wanted it to be practical and have some kind of clear business application.
The whole bootcamp took 11 weeks and we started the capstone project at week four so we had nine weeks to finish the project.
What did IE do to prepare you for the job search?
At the end of the capstone project, we had a demo day where we presented our project. IE invites as many companies as they possibly can to the demo day. It's a cool opportunity because I got to present my project and network with headhunters, recruiters, and people from the companies that I could potentially work at. My boss at Nextail (the company I work at now) was at that demo day!
IE also has a lot of relationships with companies within Spain, which was extremely useful for me. I started applying for jobs in Week 10. I got interviews quite quickly. I had a job within four weeks of graduating from the bootcamp. For the job that I ended up accepting, I had four rounds of interviews.
Tell us about your interview with Nextail – how did you get the job?
It was great because my boss got to see me present and saw what my communication and presentation skills were like so we got to skip over that part of the interview process. He scheduled a one-hour interview with me on the day of my presentation. I interviewed with two different Data Scientists within the company who asked me a lot of technical questions about modelling techniques and certain scenarios. It seemed like they were most interested in seeing the way that I think and how I solve problems.
That was something I saw in interviews across the board. In every single interview that I went on, the interviewer presented me with different scenarios and asked what I would do in that scenario. They'll usually add some kind of complication to the scenario after I explained what I would do and then had me explain how I would get around that complication. They want to see how you would go about solving a problem that you’re not familiar with. Obviously I was nervous going on interviews, but the experience was positive because they gave me interesting problems to think about.
What is your job like as a Data Scientist at Nextail Labs?
Going into the bootcamp, I knew I wanted to be a Data Scientist but I also knew that I wanted a a client-facing job. So all of the jobs I was looking for had that description.
I’ve been a Data Scientist at Nextail Labs for about eight months. We offer a solution that automates several core retail merchandising processes and churns out data-driven decisions for merchandisers. So instead of spending a lot of time on manual tasks and trying to dig through data for insights for decision making, Nextail does this all for them. The Data Scientists at Nextail are often working on improving the algorithm and making changes to the product so that it is constantly improving.
At the moment, my role is to make sure that we can show our customers how we add value to their companies. I extract the data from our databases, analyze it in Jupyter Lab (using python) and prepare datasets and dashboards so that the data can be accessible without any coding knowledge. My teammates and I use fashion retail KPIs to compare how the customer does with and without Nextail. Then, I get to present the results to the customer and work closely with the customer to make sure we are considering all the factors that are important to their business .
Is the Data Science job what you expected after your time in academia?
Yes! I can’t say anything bad about academia except that sometimes it moved a little bit too slowly for me. In the business world, things move very quickly which is a good fit for my personality. I knew that conceptually, but I guess I didn't have enough experience with it until I started at Nextail. That was a pleasant surprise for me. I'm very lucky with Nextail because it's a start-up so I get some benefits that you may not have at a more traditional company such as working remotely. The part of academia that I really loved was problem solving and investigating and that's exactly what I get to do as a data scientist.
Are you using the technologies that you learned at IE in your job now?
I use Python and SQL every day and those are the two things that I learned at IE bootcamp. I haven't used R since I've joined Nextail. I'm definitely using the things that I learned at the bootcamp but I've had to learn a lot on the job. In data science, it feels like you're going to be learning throughout your whole career. The other thing I learned at IE was data visualization tools. In addition to programming, I have to use PowerBI to make sure that my code and findings can be accessible to somebody who does not know how to code.
What's been the biggest challenge for you in this journey to become a Data Scientist?
The main challenge – and this is true for my experience in academia as well – is the moment when you're facing some kind of big challenge and you're not sure that you can do it. When we first got the data sets from our client in bootcamp and we still hadn't learned a lot of the technologies, you feel like, "This is impossible. We're not going to be able to do this." The biggest roadblock for me has been getting over that. Because you really can do it. I did it, so I know it's possible! When something seems insurmountable, make sure that you are able to break it down into little pieces until you can conquer every single little piece and get over the "I can't do it" feeling. Once you've experienced that a few times, it becomes part of the job. The key is not giving up, being persistent, being resilient, and not listening to that little part of yourself that keeps telling you to give up.
Looking back, do you think that the bootcamp tuition was worth it?
For me, It was totally worth it. I would do it all over again. IE in particular taught me a lot more than how to use Python and SQL. It teaches the technical skills, but at the same time it gives you access to professors that are going to make the process a lot less frustrating and more fun so that you have the motivation to keep going. Plus all of the extras. You get to network with companies and those companies come to give talks.
I made a lot of friends in this bootcamp, too. I started the IE bootcamp about a year ago, and now my classmates are some of my closest friends.
Have you stayed involved and mentored IE students since you graduated?
I definitely want to be more involved in the next bootcamp because I feel very connected to IE. I wasn't involved in this, but Nextail actually gave data for the last bootcamp at IE. I went to the last demo day and saw their projects – one of the things that I noticed is that the new students learned things that I hadn't learned. They're constantly updating the curriculum.
Do you have any advice for people transitioning from academia to data science through a bootcamp?
People who are transitioning out of academia often feel guilty about that – I know I did. My top advice is: don't feel guilty! If data science is something that you feel you want to do, then it's something that is really worth pursuing. It's intellectually stimulating and you're going to be happy, I promise. It's a very rewarding career choice.
And I’m speaking as a psychologist now: whatever ideas that you have about what a data scientist is or should be; if you don't think that you're good enough or smart enough. You need to just get that out of your head and go for it. If you work hard and are resilient, then you can totally do this.
Liz is the cofounder of Course Report, the most complete resource for students researching coding bootcamps. Her research has been cited in The New York Times, Wall Street Journal, TechCrunch, and more. She loves breakfast tacos and spending time getting to know bootcamp alumni and founders all over the world. Check out Liz & Course Report on Twitter, Quora, and YouTube!
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