I am currently going through Statistical Rethinking [0], by R. McElreath. So far it's been great, all the code example and practice problems help comprehension.
I've also heard good things about Probabilistic Programming and Bayesian methods [1] by Cam Davidson Pilon.
Gelman's (one of the authors behind Stan) Bayesian Data Analysis is considered to be one of the best resources on this subject, so I suggest giving that a read. It's not the easiest book to work through though.
Here's a course from another author behind Stan on YouTube, working through it myself so can't say how good it bad it is, I think it uses the book too.
https://github.com/melling/Probabilistic_Programming
Currently trying to learn RStan (and R)
Found interesting examples for the Kaggle Titanic competition:
https://wiekvoet.blogspot.com/2015/09/predicting-titanic-dea...
https://www.r-bloggers.com/2015/10/predicting-titanic-deaths...
Putting examples here:
https://github.com/melling/Probabilistic_Programming/tree/ma...
Anyway, if anyone else has stumbled upon any other blogs, books, etc, I could use other good resources.