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The book is a self-contained guide to the two pillars of data science, probability theory, and statistics, which are presented side by side, in order to illuminate the connections between them. The topics include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Throughout the book, real-world datasets are used to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference.

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