Intuitive Bayes is the course I wish I had when I was starting to learn Bayesian statistics. The subject can be pretty intimidating (especially if you’re like me, coming at it from industry without a heavy stats background or PhD), but the practical, example-first, code-first approach is how I prefer to learn. This course built a solid foundation, and since taking the course I’ve started to use Bayesian methods at work. If you’re on the fence, I hope this data point updates your priors. 

Thomas, Ravin, and Alex have created something special with their IntuitiveBayes course. Having arrived in Data Science by accident and without a rigorous background in mathematics at university, everything I've learned has been self-taught and hard-fought. Going through this course has helped meo instead build a uniform (pun intended) intuitive framework from distributions to hierarchical models. You build this by coding things yourself and playing around with the examples, asking questions, and rewatching the material. Thanks for the course guys! 

 I took a whirlwind tour of the course. I'm impressed. The layout is clean and clear, motivation is good, and points of emphasis are well chosen. One area perhaps to add is the Bayesian t-test a la John Kruschke which allows parameters for non-normality and non-equal variance. This highlights that Bayes can allow you to be honest about what you don't know, instead of dichotomously looking at model diagnostics.