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Full Description
This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book 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. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront 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.
Contents
Preface; Book Website; Introduction and Overview; 1. Probability; 2. Discrete variables; 3. Continuous variables; 4. Multiple discrete variables; 5. Multiple continuous variables; 6. Discrete and continuous variables; 7. Averaging; 8. Correlation; 9. Estimation of population parameters; 10. Hypothesis testing; 11. Principal component analysis and low-rank models; 12. Regression and classification; A. Datasets; References; Index.
