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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python Paperback - 2020

Name: Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
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ISBN: 9781492072942

by Peter Bruce; Andrew Bruce; Peter Gedeck

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O'Reilly Media, 2020-06-16. 2. paperback. Good. 23.3X17.8X2.3. Buy with confidence. Excellent Customer Service & Return policy.

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Title Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Author Peter Bruce; Andrew Bruce; Peter Gedeck
Binding Paperback
Edition 2
Condition Used - Good
Pages 360
Volumes 1
Language ENG
Publisher O'Reilly Media
Publication date 2020-06-16
Features Bibliography, Index
Bookseller's Inventory # ANAIS-149207294X
ISBN 9781492072942 / 149207294X
Weight 1.3 lbs (0.59 kg)
Dimensions 9.1 x 7 x 0.9 in (23.11 x 17.78 x 2.29 cm)
Size 23.3X17.8X2.3
Category Computers - Data Base Management
Library of Congress subjects Statistics - Data processing, R (Computer program language)
Dewey Decimal Code 001.422
Quantity available 1

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From the publisher

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.

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.

With this book, you'll learn:

Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher-quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that "learn" from data
Unsupervised 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 Bachelor's 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 bachelor's 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 PhD's in Chemistry from the University of Erlangen-Nrnberg in Germany and Mathematics from Fernuniversitt Hagen, Germany

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