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The book for this course is a fantastic practical introduction to Bayesian statistical modeling. If you've ever been curious about probabilistic programming give it a shot.

Also if you're rusty in probability and worried about prerequisites, Harvard's Stat110 can serve as a very good foundation (book and lectures are free online).

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For anyone looking for a refresher, I highly recommend this: https://bookdown.org/probability/beta/

This is actually co-written/edited by Joe Blitzstein, who teaches Harvard's Stat110. What originally sold me on taking a look is this snippet from the foreword:

"This book is primarily a teaching tool, and thus we are not very concerned with proofs unless they promote understanding...There are no Statistics or linear algebra prerequisites."

Yes. I've read the first half of the book and it can be read without any deep statistical inference knowledge at all. But it is important to know about linear regression.

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