Sports analytics can be hard and competitive

Knowing what to work on to help yourself standout can be just as tough

Here are 5 project ideas to give you some ideas 1. A data collection project

Data collection is a great beginner project

- Scrape data from a site
- Clean, transform and prepare that data
- Store that data into a database of some sort

I've been teaching myself prompt engineering and working with LLMs over the past year and a half

Here are my favorite resources to learn & get started 1.

Prompting Engineering Guide is so comprehensive and I find myself referring to it constantly as I work with different prompts and LLMs

Great resource to get started with promptingpromptingguide.ai

Maybe a hot take --

Learning data & software engineering skills will help you improve as a data scientist a lot faster than just knowing how to run code in a Jupyter Notebook To clarify, being able to run and test in a Jupyter Notebook is great. I love notebooks.

But if you have an understanding of software and building actual pipelines you'll be far more valuable to a company/employer.

10 things I would do differently if I were to start data science/sports analytics over: 🧮 ⬇️ 1. Focus on learning coding fundamentals

Learning the basics of how a coding language actually works and building a base on the fundamentals of that language will take you very far.

Before I first started getting into sports analytics I didn’t know how people were creating such cool graphics and visualizations.

For everyone who wonders how it all works, here’s the inside scoop on how to create them and resources for each: 1. Python 🐍

Definitely the most popular programming language. Packages such as @matplotlib and seaborn make it easy to create great visualizations

mplsoccer (@numberstorm), @FC_Python, and my YouTube channel are great Python guides for getting started

Here is a list of all of the tutorials I have created so far to make it easier to find and access them: How to scrape understat: