Applied Statistics With Python
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Applied Statistics with Python
"Applied Statistics with Python concentrates on applied and computational aspects of statistics, focussing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics at Touro College and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python is intended for undergraduate students in business, economics, biology, social sciences, and natural science, whilst also being useful as a supplementary text for more advanced students"--
Applied Statistics with Python
Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics courses at Touro University and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python 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. Key Features: Concentrates on more introductory topics such as descriptive statistics, probability, probability distributions, proportion and means hypothesis testing, as well as one-variable regression The book’s computational (Python) approach allows us to study Statistics much more effectively. It removes the tedium of hand/calculator computations and enables one to study more advanced topics Standardized sklearn Python package gives efficient access to machine learning topics Randomized homework as well as exams are provided in the author’s course shell on My Open Math web portal (free)
Applied Statistics with Python
Applied Statistics with Python, Volume II focuses on 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. As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily 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. This book is based on the author’s 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. 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: 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 Covers advanced machine-learning topics Uses tools from Standardized sklearn Python package