{"product_id":"practical-statistics-for-data-scientists-50-essential-concepts-using-r-and-python-paperback","title":"Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003ePeter Bruce\u003c\/b\u003e (Author), \u003cb\u003eAndrew Bruce\u003c\/b\u003e (Author), \u003cb\u003ePeter Gedeck\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. \u003c\/p\u003e\u003cp\u003e Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. \u003c\/p\u003e\u003cp\u003e With this book, you'll learn: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eWhy exploratory data analysis is a key preliminary step in data science \u003c\/li\u003e\n\u003cli\u003eHow random sampling can reduce bias and yield a higher-quality dataset, even with big data \u003c\/li\u003e\n\u003cli\u003eHow the principles of experimental design yield definitive answers to questions \u003c\/li\u003e\n\u003cli\u003eHow to use regression to estimate outcomes and detect anomalies \u003c\/li\u003e\n\u003cli\u003eKey classification techniques for predicting which categories a record belongs to \u003c\/li\u003e\n\u003cli\u003eStatistical machine learning methods that \"learn\" from data \u003c\/li\u003e\n\u003cli\u003eUnsupervised learning methods for extracting meaning from unlabeled data \u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePeter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists. He has authored or co-authored several books in statistics and analytics, and he earned his Bachelor's degree at Princeton, and Masters degrees at Harvard and the University of Maryland.\u003c\/p\u003e\u003cp\u003eAndrew Bruce, Principal Research Scientist at Amazon, has over 30 years of experience in statistics and data science in academia, government and business. The co-author of Applied Wavelet Analysis with S-PLUS, he earned his bachelor's degree at Princeton, and PhD in statistics at the University of Washington\u003c\/p\u003e\u003cp\u003ePeter Gedeck, Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Co-author of Data Mining for Business Analytics, he earned PhD's in Chemistry from the University of Erlangen-Nürnberg in Germany and Mathematics from Fernuniversität Hagen, Germany\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 360\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.9 x 9.1 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 16, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":45176760533071,"sku":"9781492072942","price":73.59,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0638\/8328\/0463\/files\/OWQwZGd0TE1SdFcxVk4xbDM4TDJuQT09.webp?v=1778198312","url":"https:\/\/easonbooks.com\/products\/practical-statistics-for-data-scientists-50-essential-concepts-using-r-and-python-paperback","provider":"EasonBooks","version":"1.0","type":"link"}