Foundations of Data Science (2018) [pdf]
None of the comments here discuss anything about the book. They're just knee jerk responses, either saying the book is too mathematical, or that other books are better. I wish the quality of comments on HN was higher.Here are some actual thoughts about the book:- Having actually read a few chapters of this book, I find it to be poorly written on the whole. The chapters are extremely verbose and written in a bit of a circuitous and winding style. I find the explanations to be strained and not very pedagogical. They might be insightful if you already understand the material well, but since this aims at being a "foundations" book, the style of writing seems to miss the mark. I think these guys might have been the wrong people to write this book.- I don't think the selection of topics makes any sense. It's a bit of a weird mish mash of topics which are arguably more or less fundamental, and have been popular at different times in recent years in the applied math, statistics, computational science, machine learning, and electrical engineering communities. If you are a student, it seems unlikely that you will benefit from taking a course taught out of this book, since you would be better served by taking more focused courses on the individual topics from this book that happen to be relevant to your research. If you are a researcher and need to learn one of the topics in this book, it's unlikely that this book is a better reference than any of the many existing books on these topics (or review papers). If you are self-studying, I think you are bound to fail with this book. You will benefit from something which is more thought through pedagogically.Ultimately, I don't really see the point of this book.