{"product_id":"applied-statistics-with-python-9781041191704","title":"Applied Statistics with Python","description":"\u003cp\u003e • Author(s): Leon Kaganovskiy\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis Ltd\u003cbr\u003e • Publisher Imprint: Chapman and Hall\/CRC\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eBased on Dr. Leon Kaganovskiy's 15 years of experience teaching statistics courses at Touro University and Brooklyn College, \u003ci\u003eApplied Statistics with Python\u003c\/i\u003e\u003ci\u003e, Two-Volume Set\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003ePython 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.\u003c\/p\u003e\u003cp\u003e \u003ci\u003eApplied Statistics with Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003eKey Features: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCovers 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\u003c\/li\u003e \u003cli\u003eEmploys Python as an organic part of the learning process\u003c\/li\u003e \u003cli\u003eRemoves the tedium of hand\/calculator computations\u003c\/li\u003e \u003cli\u003eWeaves code into the text at every step in a clear and accessible way\u003c\/li\u003e \u003cli\u003eUses tools from Standardized sklearn Python package\u003c\/li\u003e \u003c\/ul\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Hardcover","offer_id":46931148439703,"sku":"9781041191704","price":16116.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781041191704.webp?v=1771611480","url":"https:\/\/atlanticbooks.com\/products\/applied-statistics-with-python-9781041191704","provider":"Atlantic Books","version":"1.0","type":"link"}