Introduction to Probability for Data Science

by Stanley Chan

[from the Preface] This introductory textbook in undergraduate probability emphasizes the inseparability between data (computing) and probability (theory) in our time. It examines the  motivation ,  intuition , and  implication  of the probabilistic tools used in science and

Genres: Mathematics

704 pages, Hardcover

Published November 5, 2021

Reviews

[Mohamed · Rating 5 out of 5]

Picked a few chapters for some self-study topics, and found it so beautifully written. My biggest achievement is that now I can confidently talk about confidence intervals, while still not having a clue what am I talking about.

[Alex Sizykh · Rating 5 out of 5]

A wonderful, well-written book that covers pretty much all of probability theory a data scientist might need at the early stages of career, with even some insights into statistics - this book, actually, delivers the foundations of hypothesis testing better than all bigger, "more serious" statistics books/courses that I've read and seen. The same can be said about e.g. the foundations of time series analysis, theory behind different kinds of estimates (ML, MAP), and other topics from this book - it allows one to develop an understanding of probability/statistics that should be deep enough to start working in the field, and help with understanding more specialized literature.
To stay fair, cons: the book contains a small number of mistakes in formulas/symbols, and it could probably use some exercises with real data in the later chapters, e.g. on parameter estimation.