Thread by @FlorisgoesF

Yesterday I shared a small thread about getting into #DataScience. Today I’ll build on that and share a bit about my own journey into sports analytics, specifically as a #DataScientist in the #football industry. 🧵

https://twitter.com/FlorisgoesF/status/1508453394536620040

My path began with a MSc in Sport & Movement Science @VU_FBW. It’s not computer science or anything, but it does involve quite some #Math, #Statistics and #Physics, as well as a course in programming. Mainly it learned me Science, and gave me a lot of domain knowledge in sports.

I wasn’t planning to become a #DataScientist, but I wanted to work in sports. I did various stints as an embedded sports scientist, mostly internships/part-time, before joining @ZZLEIDENBASKETB. Those jobs involved data & science, but it wasn’t anything close to #DataScience.

Eventually, I decided to start a #PhD. I wasn’t planning to go into academics either, but my MSc thesis supervisor inspired me to give it a chance. I joined @BW_Groningen for a PhD in sports data science, doing research on tactical analysis in #football.

I’ll give you my take on #phdlife another time. But what it gave me was the freedom to invest a lot in my personal development, and build a network in sports. A learning experience that really helped start my career, as well as building a name in the industry.

At this point I started developing into a #DataScientist. During the first year of my PhD I set the goal to spent 4 hours of every workday on #programming & #MachineLearning. I learned #python, mainly through reading, online courses and a lot of coding.

I wrote multiple research papers applying #MachineLearning, assisted @kempe_matthias teach a Sports #DataScience course to master students and kept on learning at this point, slowly developing into a #DataScientist.

Through the network I build during my #PhD, I got a chance to move into a #DataScience role in the sports analytics industry. Working as part of a development team building an application learned me a lot of new things, most of all that there is so much I don’t know yet.

All of sudden I had to think about scalability, integration with BE & FE, architecture, cloud technology, storage, and I got to work with 2 week deadlines instead of 6 months. Academics learned me about (data) science, but not that much about technology and productization.

Fast forward 3 years, I’m leading a #DataScience team. I feel comfortable in being a #DataScientist, and the one thing I know is how much I don’t know. I learn everyday from my team, and from other colleagues / clients we work with. Which is what #Science is all about: learning.

The path (or at least mine) to a career in #DataScience and Sports Analytics is marked by coincidence, perseverance, rejection, connections, taking the opportunities that arise and a bit of luck.

As said before, the road is hard but rewarding. My first jobs in sports were unpaid or parttime. I didn’t get an invite for an interview the first 10x I applied for a PhD. In the end I kept on learning about the things I like to do most, and eventually things came my way.

Still want to get in #DataScience? Learn, a lot. Build a network, do research and find people to learn from, not only #DataScientists. Don’t give up, it will take time. Being a scientist teaches you more about what you don’t know everyday, so don’t be afraid of not knowing.