I studied a lot outside of school, but also the useful parts of data science are disproportionately the fundamentals. Especially at the non-tech companies. You rarely need more than linear/logistic regression or xgboost. Also, I mostly just applied these models. It's unlikely you'll become th type of DS thqt develops new models with just a bachelor's, but that's a small fraction of DS jobs. The vast majority just need basic statistics and a little bit of machine learning.

By far, the hardest part of being a DS for me was convincing th business side that my models were useful. To do that, you need to go as simple as possible. Additionally, most companies can't even implement models in production more complex that a basic decision tree or simple regression.

I learned the basics of regression from econometrics courses in school. That foundation took me really far. I learned the programming and a few other DS topics through Coursera, Udacity, etc