The project that kickstarted my analytics career
My Python programming journey started in 2012, where I took two online courses on it, one from Coursera and one from MIT. While both courses gave me my programming fundamentals, I wanted deeper programming skills specifically for data analytics. I thought an analytics side project would be a great way to boost my Python data analytics skills, and keep me motivated at it.
My Side Project
The first thing I had to do was to decide on a topic. I wanted something that can keep me motivated and engaged, while also having enough data to analyse. As an avid NBA basketball fan, I felt predicting NBA games using their in-game data would be a really interesting project for me to explore.
Skipping the boring parts, these were the highlights of what went down with my analytics side project:
- I learned to web scrape two websites to get the NBA in-game data I needed. This took almost two years of learning and multiple web scraping iterations, all while holding a full-time job. I had to change the website I was scraping midway because the first website implemented some impressive web scraping blocker that I couldn’t overcome.
- After scraping the data, I had to clean up some weird issues, such as players changing names and changing number of playoff games.
- The “fun” modelling part had me try different scikit-learn prediction models, different feature engineering techniques and applied the train-test-validate framework.
- Finally, with my prediction model, I redid my workflow to scrape data of newly completed games to predict next day game results.
Project Outputs
This side project took three years to complete, as I had various life commitments that made me have to stop my side project for a while. I also didn’t manage to automate the flow fully as my life got even busier and I could not spend more time to further maintain or improve on the project. The scraping and prediction flow was manually triggered by me on a few Jupyter notebooks.
Along the way, I documented my work on Github, which I decided to remove a few years ago because I was too ashamed of my code and couldn’t maintain it further. I also presented my three years of learnings from this project to a Singapore tech community, going from learning basic Python to “deploying” a machine learning model that could give me daily NBA game predictions during the season and playoffs.
While I didn’t become a machine learning data scientist, my side project did come up as a topic with my founders’ interview for an analytics role with a Singapore tech company. As I ran the entire project myself, I was able to use different learnings about dealing with real-world data from my project to substantiate my interest in analytics, and my willingness to learn the skills needed to get analytics done. I did all these, without having prior professional data analyst experience. And while I never confirmed this, I felt my side project did contribute to me landing my first analytics role in a Singapore tech company and forever changing my career trajectory.
Restarting the project? Potential extensions
Nowadays, NBA analytics has evolved greatly. There are now many open-source NBA analytics projects that can be found online. The rise of analytics has also been criticised for making the NBA boring, aka three-points galore. The rise of NBA analytics is also somewhat complicated by the legalisation of sports betting in the US. Although five years have passed since I stopped this project, I am constantly tempted to see what else I can do to restart my this side project, and what other things I can learn from it. Set up a more structured data engineering workflow? Figure a basic MLOps structure? Learn cloud deployments? What about having a full frontend to share my model results? As always, I have more ideas and desire than I have time for, especially with more life commitments adding up. Nonetheless, I also learned to never say never on many things in life. Lebron is still playing at age 39, while Jordan came out of retirement, twice.
Concluding thoughts
Competition for analytics roles is intense nowadays in 2024. An analytics side project cannot guarantee a job, but it can potentially boost your odds in such a crowded job marketplace. The right analytics side project can expose you to real-world data challenges that gives you a flavour of what real-world analytics may be. And because such side projects can take a lot of time and effort ( mine took three years ), having such side projects under your belt can help make your resume and even interviews more unique.
A positive side effect of my side project was it made me realise how much I love the investigative nature of analytics, which further fuelled my desire to make analytics my career, and not just a side hobby. So if you are still wondering if an analytics career is suitable for you, such a side project may help you find out more about your preferences about this field.
All in all, if you are willing to commit some time and effort into an analytics side project, it can potentially give you some interesting learnings, insights and opportunities in your analytics journey!
I once applied to the Houston Rockets as their data analyst, to be under the basketball analytics pioneer, Daryl Mory. I was obviously rejected by an email bot, but hey, we miss all shots we don’t try, right? ( puns intended )
Thanks to everyone who has read this post. If you are interested in analytics side projects with a social science spin, follow me on Medium or Linkedin. Some topics I have explored include (1) Singapore housing prices and the updated Singapore million dollar public home analysis, (2) accessibility of Singapore hotels, (3) Taiwan housing prices, and (4) I even built a small web app for Singaporeans to track the library books they want to borrow. I also share less technical topics, like (5) how I learned to deal with uncertaintyand (6) how I end up being a freelance analytics consultant.
Lastly, I have a Substack (it is still alive) as well, where I share ideas on data concepts and strategies for targeted at busy business people.
