3 Ways Studying Math Made Me a Better Data Scientist. And 3 ways it left me unprepared for the real world
Top data scientists come from a wide variety of backgrounds. Some study computer science and excel from day one at programming elegant models to analyze the data. Others study statistics and leverage their knowledge of using data to respond to a well-structured question. Nowadays, of course, many data scientists are actually studying in data science programs and develop a cross-section of all of these skills. Some, however, are like me and studied math.
When I started university, I knew math was the right degree program for me. I’ve always enjoyed math: its pure logic and the satisfaction that comes from solving an equation. Plus, it came easily to me. My mind’s always been well-adapted to math, and I was curious to explore more complex math topics.
I also knew, however, that theory was not where I wanted to focus my career. When it came time to write my master’s thesis, an opportunity to apply math to a data science problem presented itself. I was paired with a company, Evo Pricing, and challenged to use my knowledge of math to build a model that would more accurately forecast sales. I was hooked, and I’ve worked for Evo as a data scientist ever since.
Studying math gave me a broad skill set that helped me quickly adapt to practical work as a data scientist, yet there were some profound gaps that I had to work hard to bridge to succeed. I’m passionate about my career as a data scientist, and I think my mathematics background gave me a strong foundation for what I do every day. In hindsight, though, there are a few things I would have changed about my university experience. Aspiring data scientists can learn from my experience and graduate even better prepared for the real world.
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