While this tutorial is self contained and doesn't require large amounts of prior knowledge, having an understanding of Bayes Theorem, why it's useful and how things like Markov chain Monte Carlo and PPls relate will be helpful.
Below is a list of resources that will add additional context to the content here. Do not feel like you need to read or watch them. But if you do decide to I encourage you to find the one or two that you like the best and deeply focus on those resources. The cost of the resource is marked in the header.
Bayesian Analysis using Python is the most "related" to the tutorial as it's the only one that covers ArviZ and Bayes Theorem. Osvaldo Martin is a core contributor to both PyMC3 and ArviZ and is able to relate the theory to both libraries. For this tutorial Chapters 1 and 2 are sufficient. Chapter 3 will add additional context.
Probabilistic Programming for Hackers dives straight into topics like MCMC, Bayes Theorem, and PPLs in a "enough talk let's see it working" fashion. The book itself is a jupyter notebook that is meant to be executed. Chapter 1 is all that's needed, if you have spare time Chapter 2 and 3 will add more details
Professor Richard McElreath approaches Bayesian teaching in a methodical "let's build a strong foundation, for a strong understanding" fashion.
For this tutorial I would suggest watching Lecture 1 and Lecture 2 from his Winter 2019 class
He's also written a book, Statistical Rethinking. If you decide to buy the book I suggest reading Chapters 1 to 3. Note that he's about to release a second edition which is not out yet.
Both Pystan and PyMC3 have a number of freely available tutorials. The tutorials will primarily help with understanding how to use probabilistic progamming languages to apply Bayes Theorem, and are the least structured. I would suggest one of these if you are already familiar with Bayes Theorem and are looking to brush up on a PPL.