{"product_id":"statistics-for-data-science-and-analytics-hardcover","title":"Statistics for Data Science and Analytics - Hardcover","description":"\u003cp\u003eby \u003cb\u003ePeter C. Bruce\u003c\/b\u003e (Author), \u003cb\u003ePeter Gedeck\u003c\/b\u003e (Author), \u003cb\u003eJanet Dobbins\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eIntroductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntroductory Statistics Using Python\u003c\/i\u003e is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. \u003c\/p\u003e\u003cp\u003eA range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A\/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of \"kitchen sink\" formulas. Regression is taught both as a tool for explanation and for prediction. \u003c\/p\u003e\u003cp\u003eThis book is informed by the authors' experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntroductory Statistics Using Python\u003c\/i\u003e includes information on sample topics such as: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eInt, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets\u003c\/li\u003e \u003cli\u003eExperiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data\u003c\/li\u003e \u003cli\u003eSpecialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels--the workhorses of data science--and how to get the most value from them\u003c\/li\u003e \u003cli\u003eStatistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten by and for data science instructors, \u003ci\u003eIntroductory Statistics Using Python\u003c\/i\u003e is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eIntroductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eStatistics for Data Science and Analytics \u003c\/i\u003eis a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. \u003c\/p\u003e\u003cp\u003eA range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A\/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of \"kitchen sink\" formulas. Regression is taught both as a tool for explanation and for prediction. \u003c\/p\u003e\u003cp\u003eThis book is informed by the authors' experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eStatistics for Data Science and Analytics \u003c\/i\u003eincludes information on sample topics such as: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eInt, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets\u003c\/li\u003e\n\u003cli\u003eExperiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data\u003c\/li\u003e\n\u003cli\u003eSpecialized Python packages like \u003ci\u003enumpy, scipy, pandas, scikit-learn\u003c\/i\u003e and \u003ci\u003estatsmodels\u003c\/i\u003e--the workhorses of data science--and how to get the most value from them\u003c\/li\u003e\n\u003cli\u003eStatistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWritten by and for data science instructors, \u003ci\u003eStatistics for Data Science and Analytics\u003c\/i\u003e is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 384\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.88 x 9 x 6 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 04, 2024\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51773777772832,"sku":"9781394253807","price":172.8,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/0cc06f60dbdc9ec0c1d503726f54de3f_dabfb3d6-12f9-4a4e-8038-dcaddfcea405.webp?v=1780432590","url":"https:\/\/ebocreations.com\/products\/statistics-for-data-science-and-analytics-hardcover","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}