Practical Statistics for Data Scientists

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

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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
Statistical machine learning
Unsupervised learning
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Product details

  • Paperback | 350 pages
  • 178 x 233mm
  • Sebastopol, United States
  • English
  • New edition
  • 2nd New edition
  • 149207294X
  • 9781492072942

About Peter Bruce

Peter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at, 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. eter 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-Nurnberg in Germany and Mathematics from Fernuniversitat Hagen, Germany
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