[mmasu]

Thanks for posting this as it seems very interesting. Did you go through the whole book/course? the preface claims that “You don’t need to know calculus or linear algebra. You don’t need any prior knowledge of statistics”. I am always a bit skeptical when i read these claims as the texts tend ultimately to be a bit superficial; it would be nice to have an informed opinion about it before starting it out :-) thanks in advance.

[bradyat]

Yeah I have completed it, and I really like his teaching style. I’d say it might be a little tough if you have absolutely no knowledge of statistics. But as someone who wanted to do a deeper dive into Bayesian stats specifically I found it a good resource. The reason I ended up doing it was precisely because I felt that the section on bayes in most stats courses is too superficial

[mmasu]

thanks!

[compumetrika]

I worked through large chunks of the book a long time ago (perhaps an earlier version). I agree with the other responder-- if you've had zero stats it may be more difficult, but the author does an excellent job of discretizing almost everything, which means that integrals and derivatives are replaced with adding and subtracting. For this book I think the most advanced math you need is multiplication and division. It's a clever trick that relies on the fact that computers are really good at multiplying 10,000 things by 10,000 other things and adding them up (which is what discretization implies here).

Before computers that was much harder to do so you needed clever math tricks to "do it all at once," which gets you to all the analytical methods -- calculus, etc. Still all very useful! Just hard to teach to a broad audience. Downey really leans into the technical advantage to computers provide and thus can cover a lot of material before getting into complicated math.

[proamdev123]

> the preface claims that “You don’t need to know calculus or linear algebra. You don’t need any prior knowledge of statistics”.

Downey makes this statement because his premise is that if you know Python, you can use that knowledge to learn the concepts without the pure math approach.

For example, he will do iteration to calculate integrals rather than teaching integration. And he will plot statistical distributions using a Python library to explore and teach the statistical concepts.

He also has a book called “Think Statistics” that is excellent in this regard.