Name: Ivanna Yurkiv
Major: Policy Science (GED)
Master: MSc Business and Data Science, Copenhagen
Hey Ivanna, thanks for reaching out! When you said you wanted to talk about data, I was surprised to realize this was a topic we had never discussed in our alumni highlights, despite the surrounding it hype. I’m sure I’m not the only one that’s watched videos on Artificial Intelligence and Machine Learning, and wished I paid a little more attention in (Advanced) Quantitative Research Methods (AQRM).
It’s funny you say that. QRM and AQRM, and everything Lucy Zicha did were my starting point for getting into data and coding. These courses opened me up to the simpler models like regression and showed me that this was something within my reach.
Maybe it’s good to start there: how did you get to where you are now, how did you get into data?
Looking back, I was always fascinated by math. I was in international school, doing the International Baccalaureate. I don’t know whether you’re familiar with the IB, but like most systems there are several levels of maths. I started in the highest level, and I did quite well at first. When I got further into the programme, I felt that I wasn’t always keeping up, and it might be better to go to a lower level. It was something that interested me, but not something I felt comfortable with.
And then I took QRM and AQRM out of curiosity, and I did well. That gave me confidence. I realized I like coding. Once I got going, it gave me this peace of mind, and I did really well in the course. Lucy really helped; she was very motivating, and I don’t think she graded us too harshly. As long as you showed you tried on the final project you would be okay. And with that confidence, I realized I might pursue data science when I had to decide on a Master’s.
But you didn’t pursue this straight away; you took a bit of a detour, right?
Yes. My first job out of LUC wasn’t in data. I worked for Philip Morris of all places, as an accountant processing invoices. One of the things my team was struggling with was doing everything on time. But when I tried looking at how we could solve it, I realized we had absolutely no data on it. We didn’t know whether we didn’t have enough people, whether invoices already came in late, things like that. This helped me realize two things:
- It would be super interesting to look into data to understand this issue.
- The company I was working for was unfortunately not tracking this data, and did not use it to make decisions.
Back then, in early 2017, data wasn’t quite the hype it is today. But that’s when I realized that I wasn’t in a place I wanted to be, and that the place where I wanted to be was data. So I applied to the MSc Business and Data Science at Copenhagen Business School. I pursued this Master’s rather than one in pure Data Science because there’s only so far you can go with a Policy Science degree. The degree at CBS included public policy, and that was my way in.
When I was finishing up at CBS, I managed to land an internship at Novo Nordisk, a pharmaceutical company. I was happy to extend my stay, as I love Denmark. It was really cool, because there I got to work with real data, from all kinds of real time sensors in their factory, to predict when the chemical process reached equilibrium. I liked it so much that I turned that internship into the analyst job I have right now. I’ve switched the factory processes to supply chain, but I still love the work and the people. The fun part about data is that as long as you’re open to learning about new areas, you can apply it in many ways.
Hold up, Philip Morris isn’t the place you’d expect an LUC degree to take you. How did that happen, and how was it to work there?
When I was younger, I always wanted to go back to Ukraine and get into public service, or maybe into journalism. My parents really weren’t fans of that, as it’s actually quite dangerous to try and change things there, especially as someone with an international background. After doing my own research during my thesis, I thought it might be better for me to try something else. I had done an internship at their headquarters back in 2014, so it was easy for me to get a job. After that, I already knew a lot of people there, so I thought it would be a good place to start. I got a job in accounting, in Poland, and worked there for a while.
So why did you change your mind?
When I was there, I never really felt like I fit in, both with Philip Morris, and in Poland in general.
When I started working at Philip Morris, I felt that I was just doing my job. I was just processing invoices, and it didn’t really matter where I did that. I wasn’t involved with tobacco or anything, I was quite far away from it. But over time, I realized that I was still associated with the company and what it stood for. Because at the end of the day, you are involved by working there and by contributing your time. Nowadays I am a lot more careful about where I spend my time.
And when it comes to Poland – it was a shock to me after Holland. The Netherlands is very liberal as you know, and people are (mostly) free in the way they think, and it felt like you could be who you wanted to be without being judged. Poland was the opposite. The city I was in was very conservative and religious. And that was reflected in the people around me; my co-workers and my neighbours. I realized I agreed much more with Dutch values.
In the end I look back on that period of my life as a lesson in what I don’t want. Which I suppose is worth something too, as it got me to where I am now, where I do feel like I fit in.
Back to the present, one question I had was: is data a hype? Is it too late for current LUC students or recent graduates to get in?
I definitely think it’s a hype. It pops up in every company and organization. They’re all like “we’re using data”, “we have driven decision making”, etc. It’s these cookie cutter phrases that keep repeating on company websites and vacancies. But I think that doesn’t mean that you shouldn’t get into it if you’re interested. There’s definitely work in it in the foreseeable future. So if you want to get into it—not because of the hype, but because you’re genuinely interested in working with numbers, you get excited playing with data, or love looking at statistics—then you should.
And the fun part about data is that it’s a very quickly evolving field. So even if you’re only learning it now, you’re not that far behind others, because the field is so new and there’s constantly new things popping up. If you want to get into the field, I recommend (for starters) understanding what data science is, and what specific area within data science interests you and then developing an expertise in that. One thing for sure is that you cannot know it all – you cannot learn all the tools and know all the models. But you can choose an area or a tool and become really good at it.
Ivanna has a public Tableau profile, where she makes her own visualizations of things she finds interesting. Her favourite visualization is a comparison of her own activity between different jobs, one far and one closer to home, pictured below:
So say I was a second or third year student, and I want to learn more about data, where should I start?
The great thing about data is that there’s so much available online. At the same time, there is so much that it might get overwhelming. I would recommend focusing on some simple data analysis courses, for the tool that you find interesting. That can be Python, or R, or STATA, or even a more visual tool like Tableau or PowerBI. As you learn the basics, play with some real-world data you find interesting, and figure out whether this is something you want to really invest your energy in. Something important to remember is that you’re going to spend 80% of your time cleaning data and doing very basic analysis.
Then once you get further, I really recommend some basic SQL. In companies you’re often going to need it to get the data you want.
Some tips to get you started:
- Data Camp is a good place to learn Data Science in R and Python online.
- edX and Udemy have a lot of courses from great universities
- Automate the Boring Stuff with Python is a great free way to learn about Python
- You can get Tableau for free as a student and it comes with online courses
Thank you so much for sitting down with me Ivanna!
This post was part of a larger series of “portraits” of Alumni. Want to be featured, have questions for Ivanna, or about alumni in general? Do not hesitate to reach out to Evolucio via our website, LinkedIn, Facebook or email@example.com.