Applied Statistics with Python Two-Volume Set

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.