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. 🧵
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.
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.
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Tactical behavior in #Football has a spatial and a temporal component, and results from interaction with the opponent. It’s key to account for all these aspects in data-driven tactical analysis, as well as to respect the complexity of the temporal and spatial dimensions 🧵
Two years ago I published a systematic review in @EurJSportSci on using big data in #soccer for tactical performance analysis that illustrates the associated challenges and provides a data-driven scientific framework. #DataSciencetinyurl.com/mrxky6ca
The most common analysis issue is the fact that spatial and/or temporal complexity is not respected. For example by aggregating data over multiple minutes, or constructing spatial features aggregating 11 player positions into a single variable.
Preparing for a technical interview for a #DataScience position? These are some of the questions that typically allow me as an interviewer to quickly distinguish between juniors and mediors, including some quick tips 🧵. #Python#pythonprogramming#DataScientist#Jobs
All questions about SQL. Not the hardest thing to learn, but many #DataScientists only start to learn the value of SQL when they actually become part of a dev team. I’m not only talking about SELECT * FROM table, but also about joins, truncates, partitions and constraints.
Interacting with an API. Make sure you know your requests (GET, POST, PUT, DELETE, PATCH), as well as the #Python requests library.
#DataScientist in a software dev team and #pythonprogramming code for production pipelines? You should think carefully about scalability and integration. One of the things to consider is datatypes, here are some helpful tips 🧵
#Python is a dynamically typed language, but that doesn't mean you shouldn't care about types. Know you dtypes, from "str" to "bool" to "int8" to "float64", and understand their memory footprint and restrictions. Especially when working with larger objects, choose wisely.
Loose the strings. 9/10 times strings can be replaced by categoricals (Pandas) or even better by Enums (docs.python.org/3/library/enum…). This can reduce memory footprint of large dataframes with >30%, and improves performance.
Many young people ask me how they can become a #DataScientist, specifically in #football. Lately I have also seen a lot of posts on how to get into #DataScience in (1)50 days or so, which is a joke imo. Here is my realistic take on it. Warning: it will be closer to 1500 days. 🧵
#DataScience is an umbrella of roles & fields that require different competencies. But they all have two things in common: you have to know #Science and you have to be able to work with #data. The first requires learning to do research, the second learning to do #programming.
Go to uni and get a masters degree that at least requires some #math skills. I’m not saying you need a #PhD and 5 publications before calling yourself a #DataScientist, nor that you can’t be one without a MSc, but is helps a lot in acquiring the right competencies.