[fractionalhare]

Lots of tech and finance companies (particularly those with standardized interview processes) will blacklist questions if they're found online. Those companies will constantly check GitHub, GeeksForGeeks and Leetcode to see if their questions are listed there with solutions.

This probably won't be the case for a question as basic as, "what is regression?" But for any intermediate to advanced interview question involving regression, I would expect companies to jealously guard it.

If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.

[jointpdf]

A couple recommendations piggybacking off of yours:

A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home

Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/

There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learnin...

[disgruntledphd2]

Also, Regression and Other Stories is the new edition of the Regression with Multilevel models book, and it's much, much better (especially for n00bs).

[logarhythmic]

A bit off topic but how much of data science work requires this probability/statistics knowledge on the job? I've heard you basically need a PhD to do modelling and "real" data science