Statistics and Data Science

Statistics and Data Science: A Modern College Introduction is a comprehensive introductory textbook designed for undergraduate students encountering statistics for the first time in a data-driven world. The text balances conceptual clarity with practical application, presenting statistical reasoning as a disciplined method for understanding uncertainty, evidence, and inference across modern scientific and social contexts.

The book begins with foundational concepts, including types of data, measurement scales, and descriptive statistics, before developing probability theory and statistical distributions as the basis for statistical inference. Core methods such as confidence intervals, t-tests, analysis of variance (ANOVA), correlation, regression, chi-square tests, and nonparametric procedures are presented with step-by-step explanations and worked examples that emphasize interpretation rather than rote computation.

Beyond traditional coverage, the text introduces topics increasingly expected in contemporary curricula, including Bayesian statistics, resampling methods such as bootstrap and permutation tests, logistic regression, survival analysis, and an introduction to machine learning concepts. Ethical considerations and the societal implications of statistical decision-making are addressed in a concluding section, reinforcing responsible data use.

The mathematical level is intentionally accessible, assuming only basic algebra and logical reasoning. Formulas are introduced carefully, assumptions are stated explicitly, and limitations of methods are discussed throughout. Examples are drawn from technology, health sciences, social research, and data science applications, helping students connect statistical theory to real-world problems.

By integrating classical statistical foundations with modern data science perspectives, this text prepares readers not only to compute results, but to reason critically, interpret evidence, and apply statistical thinking responsibly..