CmdStanPy if our primary interface to Stan.
ArviZ provides valuable tools for parsing Stan results.
The Distribution Explorer provides quick references for various distributions and the syntax for using them in Python and Stan.
The bebi103 package provides utility functions for MCMC-based inference and other useful functionality for the class.
Michael Betancourt’s writings contain some of the best explanations and case studies about Bayesian inference, especially with Stan, you can find anywhere.
Stan tutorials YouTube channel has a great series of videos.
Statistical Rethinking, 2nd Ed. by Richard McElreath is a good pedagogical book about Bayesian inference.
A Student’s Guide to Bayesian Statistics by Ben Lambert is another good reference, especially for beginners.
Bayesian Logical Data Analysis for the Physical Sciences by Phil Gregory. There is also a supplement available. This is a nice book to discuss how Bayesian inference fits in with logical approaches to science.