This has all the probability (set theory, distributions, etc) and statistics (both descriptive and inferential) that you would need to get started in a data science context.I say this because that's exactly what I bought the book for, and it served that role well!To get the most out of this book, I would do the problems listed in the book, and also write down the formula(s) and steps for solving them. Most of the later chapters depend on material covered earlier in the book, so I wouldn't recommend moving on to another chapter or even section of the book until you know you can follow the steps laid out in the equations (if any).One quick tip: for things like probability distributions, you should focus more on knowing why you would use one over the other rather than try to memorize the steps by heart (because you probably won't, and don't need to).When you can successfully solve a problem using the formula or listed steps alone, you can usually move to the next section of the book.The book is useful for reference too, if it's been a while since you've done a problem type you can just review a section to review the process of pulling it off correctly. Knowing when you would use a certain technique described in the book is always more important than trying to memorize it by heart.Good for initial learning and good for review as far as undergrad-level math is concerned. If you want to dive even deeper, I'd recommend this as your next read: