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Practical Statistics for Data Scientists - 2nd Edition by Peter Bruce & Andrew Bruce & Peter Gedeck (Paperback)

Practical Statistics for Data Scientists - 2nd Edition by  Peter Bruce & Andrew Bruce & Peter Gedeck (Paperback)
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Last Price: 31.99 USD

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<p/><br></br><p><b> About the Book </b></p></br></br>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 practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>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 what's important and what's not.</p><p>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.</p><p>With this book, you'll learn: </p><ul><li>Why exploratory data analysis is a key preliminary step in data science</li><li>How random sampling can reduce bias and yield a higher-quality dataset, even with big data</li><li>How the principles of experimental design yield definitive answers to questions</li><li>How to use regression to estimate outcomes and detect anomalies</li><li>Key classification techniques for predicting which categories a record belongs to</li><li>Statistical machine learning methods that learn from data</li><li>Unsupervised learning methods for extracting meaning from unlabeled data</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>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 Bachelor's degree at Princeton, and Masters degrees at Harvard and the University of Maryland.</p><p>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 bachelor's degree at Princeton, and PhD in statistics at the University of Washington</p><p>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 PhD's in Chemistry from the University of Erlangen-Nürnberg in Germany and Mathematics from Fernuniversität Hagen, Germany</p>

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