There’s a misconception out there about the data science skills gap - the truth is there is a huge demand for highly skilled data scientists, a big demand for data and ml literate developers, and a moderate demand for entry level data scientists.

These resources from google and courses like Fast AI are great for getting devs up to speed so they can meaningfully contribute to data science projects - filling that big demand for data + ml literate devs, especially internally. They’re not designed to get people jobs (disclosure, getting people jobs in data science is what we do at thisismetis.com)

If you want to go deeper? The open source data science masters is a good set of resources[0]. The first few sections of Goodfellow’s deep learning book are a great crash course in ML math/stats theory[1]. Introduction to Statistical Learning is a staple in most people’s library[2]. There’s a glut of intro level data science content out there on the internet, but intermediate to advanced stuff usually means putting in serious effort or breaking out your checkbook and going back to school (whether traditional or otherwise).

[0]http://datasciencemasters.org/ [1]https://www.deeplearningbook.org/ [2]http://faculty.marshall.usc.edu/gareth-james/ISL/