Statistical Foundations for Data Science

Statistical Foundations for Data Science is an academic text designed to develop statistical judgment in the age of data, artificial intelligence, and machine learning. The book integrates conceptual foundations, mathematical rigor, and computational applications to help students, professionals, and researchers understand not only how to apply statistical methods, but also when to use them, how to interpret them, and what their limitations are.Throughout its chapters, the book covers the nature of data, descriptive statistics, probability, distributions, inference, regression, classification, resampling, machine learning, dimensionality reduction, clustering, causality, algorithmic ethics, communication of results, and reproducibility.It includes definitions, formulas, interpreted graphs, tables, case studies, exercises, critical questions, and reproducible code in Python and R. Its central purpose is to bridge the gap between classical statistics and the current demands of data science, promoting rigorous, transparent, ethical, and decision-making-oriented analyses.