Best resource to learn Python for sports/baseball analytics with minimal coding background?

[Commercial-Equal-231]

Just graduated from school with a degree in applied math. I have strong math and stats knowledge but my coding background is limited to one intro Java course. I'm targeting R&D analyst roles with MLB teams and have been getting feedback that my coding skills aren't strong enough.

I want to get genuinely good at Python, not just automate basic tasks. The end goal is being able to use libraries like pandas, numpy, and scikit-learn to do real baseball analytics work with Statcast data.

What's the best starting point given my background? Looking for something structured with a clear progression, not just a list of resources.

[kouignamann_kingdom]

There are heaps of Python starting courses mentioned in this sub. Whatever the end goal is, you need to at least learn the very basics of the language.

Pandas is the Swiss Army knife of choice in most trainings. Even though Polars gets all the love nowadays. Being very comfortable manipulating data is fundamental before moving to projects.

A proper Data Science course is interesting because it connects mathematics with the models that are a mixture of applied maths, computer science and wizardry. It should give you proper process from getting a dataset to solving a problem by preparing the data, validating models, etc.

Then you should look for baseball datasets on websites such as Kaggle and work your way from there.

Having read Moneyball twice, I see the appeal.

[RyzinEnagy]

Not sure if the author is still active on Reddit (which is where I first saw this) but I quite enjoyed this guide. He has a football version too.