Applied Statistics with Python : Two-Volume Set

The set focuses on applied and computational statistics, ANOVA, multivariate models like multiple regression, model selection, reduction techniques, regularization methods like lasso, ridge, logistic regression, K-nearest neighbors, support vector classifiers, nonlinear models, tree-based methods, clustering and principal component analysis.

Based on Dr. Leon Kaganovskiy’s 15 years of experience teaching statistics courses at Touro University and Brooklyn College, Applied Statistics with Python, Two-Volume Set focuses on applied and computational aspects of statistics, ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis. Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning and the books heavily rely on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process. Applied Statistics with Python has been expanded from eight chapters to thirteen chapters in two volumes, and is intended for undergraduate students in business, economics, biology, social sciences, and natural science, while also being useful as a supplementary text for more advanced students and professionals. While some familiarity with basic statistics is helpful, it is not required—core concepts are introduced and explained along the way, making the material accessible to a wide range of learners. Key Features:Covers both introductory topics such as descriptive statistics, probability, probability distributions, proportion and means. hypothesis testing, one-variable regression, as well as advanced machine-learning topics. Employs Python as an organic part of the learning process. Removes the tedium of hand/calculator computations. Weaves code into the text at every step in a clear and accessible way. Uses tools from Standardized sklearn Python package.