This book stands out for its motivation–intuition–implication approach, which makes abstract probability accessible without losing mathematical depth. Its progression from probability fundamentals to modern data-science methods and core ECE applications, reinforced by MATLAB and Python examples, clear visualizations, and computational examples makes it particularly valuable for undergraduate teaching.
A refreshingly clear bridge between theory and practice—Chan's book shows exactly why probability is the bedrock of data science, pairing sharp intuition with the rigor students will draw on throughout their careers.
What I particularly like about this book is the way it connects the foundations of probability with the kinds of questions that arise naturally in modern data science. The presentation is mathematically careful, yet remains intuitive, with well-chosen, reproducible computational examples and code snippets that make the concepts easy to explore in practice. I can see it being useful well beyond the classroom, particularly for researchers and practitioners across data science, machine learning, and signal processing.
An exceptionally accessible introduction to probability, with a strong emphasis on the concepts and tools most relevant to modern data science. The companion e-book, code, slides make learning both engaging and practical.
Probability theory is a cornerstone of science and technology, and is especially important for data science. This exceptionally ambitious and vividly illustrated textbook approaches the subject from a distinct perspective. It not only presents the core probability concepts that are typically covered in the undergraduate engineering curriculum, but it also ventures into some advanced topics that are increasingly relevant to modern applications but rarely found outside of graduate-level texts. The book is also filled with beautifully illustrated examples that make sophisticated material more tangible and help build intuition, with many of these based on common practical situations that arise in real-world data science applications. A motivated student will be able to get themselves a very long way with this book.
Introduction to Probability for Data Science is an excellent resource for building a strong foundation in probability through intuitive explanations and practical data science applications. Its clear examples and real-world focus make it a valuable reference for students and aspiring data scientists.
Probability is one of the most fear-inducing subjects for undergraduates interested in data science. This newly revised textbook lets students conquer that fear through a systematic approach, leveraging notes, exercises, and useful video examples to demystify the topic and build a strong mathematical foundation to launch further investigations and careers in data science.
Introduction to Probability for Data Science is the rare textbook that presents probability as a way of thinking that students carry into machine learning and artificial intelligence. The second edition is a substantial improvement, with a clearer exposition, a wealth of new exercises and solutions, and integrated code that helps students move between theory and computation. What stands out most is the book's clarity. It develops difficult ideas patiently and intuitively without sacrificing rigor. This is an excellent first course for aspiring data scientists, and a book I'm happy to recommend to my own students.
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 (Python, Matlab, R, Julia). 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.
Prof. Stanley Chan's book does what few probability texts manage: it offers a masterful and rigorous treatment of probability while consistently illuminating its profound relevance to modern data science and machine learning. Now, with its expanded code, exercises, and worked solutions, the second edition is the book that I want all my students to have before they touch a machine learning paper.
Stanley Chan is a gifted educator with a rare ability to reveal the intuition behind mathematical ideas without sacrificing rigor. Introduction to Probability for Data Science reflects that gift throughout, connecting probability to computation, machine learning, and real-world problems in a way that helps students understand not only how the mathematics works, but why it matters.
As AI moves faster, a real command of probability has become more important, not less. Professor Stanley Chan has written the book I would want all my own students to learn from: mathematically serious, exceptionally intuitive, and consistently connected to how modern data analysis actually works.
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.