Endorsement‘‘This is one of the best introductory books on probability that I have seen. It is rigorous, yet intuitive. It is full of beautiful illustrations and easy-to-understand code samples (in Python and Matlab). Before introducing each new theoretical concept, the author gives reasons for why the material is important in practice, thus providing motivation for learning it. The title focuses on “Data Science” but in fact this book could be used to provide a thorough introduction to probability for any STEM student.’’ ‘‘This is an excellent textbook for undergraduate EE and CS students, with thorough coverage of a wide range of topics, including fundamentals such as probability spaces, random variables, and sample statistics, as well as more applied problems such as regression, estimation, and hypothesis testing. New concepts are introduced with clear, intuitive explanations, followed by more rigorous theory. The book is beautifully illustrated with numerous diagrams, plots, and other visual illustrations, and the frequent computational examples play a valuable role in connecting theory and practice. It is also worth noting that the author has made this book available at no cost, despite the enormous effort that was clearly dedicated to writing it.’’ |