This self-contained guide introduces two pillars of data science, probability theory and statistics, side by side, illuminating the connections between probabilistic concepts and the statistical techniques they employ, such as the relationship between nonparametric and parametric models and random variables. Other topics covered include hypothesis testing, principal component analysis, correlation, and regression. Examples throughout the book draw from real-world datasets, quickly demonstrating concepts in practice and confronting readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.

‘Fernandez-Granda's Probability and Statistics for Data Science is a comprehensive yet approachable treatment of the fundamentals required of all aspiring Data Scientists-whether they be in academia, industry or elsewhere. The language is clear and precise, and it is one of the best-organized treatments of this material I have ever seen. With lucid examples and helpful exercises, it deserves to be the leading text for these topics among undergraduate and graduate students in this technical, fast-moving discipline. Instructors take note!’

Arthur Spirling - Princeton University

‘If you're mathematically inclined and want to master the foundations of data science in one go, this book is for you. It covers a broad range of essential modern topics - including nonparametric methods, causal inference, latent variable models, Bayesian approaches, and a thorough introduction to machine learning - all illustrated with an abundance of figures and real-world data examples. Highly recommended.’

David Rosenberg - Office of the CTO, Bloomberg