If you want to learn Stan I highly recommend the book Statistical Rethinking (2nd Ed) by Richard McElreath. It’s a pedagogical masterpiece and light years away the best resource I’ve found on learning Bayesian inference.
https://www.youtube.com/watch?v=4WVelCswXo4&list=PLDcUM9US4X...
Personally, I think https://probmods.org/ is an exceptionally good introduction to probabilistic programming for someone that knows CS or just some programming and likes a SICP-like textbook that goes into the essence of the topic.
Learning Stan is great, but not as a first probabilistic programming language, because it's quite limited (it trades model expressiveness for performance). So you can't represent a large set of models, such as infinite mixtures, which may become really relevant in the future developments of deep learning. It also has poor performance in models that involve many discrete variables.
For people wanting Python, Jupyter notebooks with Python code examples are here:
* https://github.com/pymc-devs/resources/tree/master/Rethinkin...