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Practical Statistics For Data Scientists: 50+ Essential Concepts Using R And Python Paperback English by Peter Bruce - 43998

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Publisher	O'Reilly Media
ISBN 13	9781492072942
ISBN 10	149207294X
Author	Peter Bruce
Book Format	Paperback
Language	English

Book Description	Statistical 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 whats important and whats not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If youre 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.With this book, youll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data.
About the Author	Peter 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 Bachelors degree at Princeton, and Masters degrees at Harvard and the University of Maryland.^Andrew 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 bachelors degree at Princeton, and PhD in statistics at the University of Washington^Peter 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 PhDs in Chemistry from the University of Erlangen-Nrnberg in Germany and Mathematics from Fernuniversitt Hagen, Germany.
Language	English
Edition Number	2
Publication Date	43998
Number of Pages	360

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