This is a review of Rasmus Baath’s Fundamentals of Bayesian Data Analysis in R DataCamp course.
I really enjoyed this course. Rasmus takes us through an intuitive understanding of Bayesian data analysis without introducing Bayes’s Theorem until the 4th chapter. The best part is, that’s not even a criticism: by the time he introduces the theorem, you already know the parts and the theorem is more a formalization of what you have already done.
If you’re new to Bayesian thought, give this course a try. The examples are clear and interesting, and Rasmus does a good job of mixing tabular results with histograms and other methods for visualizing results.
One of the nicest things I can say about the course is that during the exercise phases, I almost never had to go look things up independently of the course materials. Pretty much every concept was already on the slides or in the instructions and it was a matter of putting the pieces together rather than spending an hour trying to research some function somewhere which might get you through the problem at hand. I did have to read the help files to figure out parameters for a couple of functions, but that’s fine—the problem comes instead when an instructor expects you to know something not mentioned at all anywhere. In setting up these exercises, Rasmus does a great job.
If there’s one thing I would have liked, it was a bit more detail on BEST and other Bayesian estimation tools. Fortunately, there are a couple of courses dedicated to STAN and JAGS, so those should satisfy my curiosity.