Anybody knows a book on similar topic with lots of practical examples and exercises? By "practical" I mean that instead of mathematical definition of covariance and example of how to use it in R/Python author would provide us with some real world use case where we're trying to obtain some information about the real world, given some data. Like list of water temperature measurements over the years, number of pirates… you've got the idea.
Try ISLR (for learning/prediction-based stats). Probably the best for someone of your level. Incredibly well written, based around examples, reproducible code. The complex parts – the parts where you need math or abstract concepts – are VERY intuitive.
Cam Davidson-Pilon's "Bayesian Methods For Hackers" is a great resource. It's actually written as a group of IPython notebooks so you can actually download them and play with the cells to really understand what's going on.
