Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2/e (Paperback)

Bruce, Peter, Bruce, Andrew, Gedeck, Peter




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





- 探索性數據分析
- 數據和抽樣分佈
- 統計實驗和顯著性檢驗
- 迴歸和預測
- 分類
- 統計機器學習
- 非監督學習


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

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


Peter Bruce是統計教育研究所Statistics.com的創辦人和首席學術官,該研究所提供約80門統計和分析課程,其中大約一半針對數據科學家。他是統計和分析領域的多本書籍的作者或合著者,並在普林斯頓大學獲得學士學位,並在哈佛大學和馬里蘭大學獲得碩士學位。

Andrew Bruce是亞馬遜的首席研究科學家,擁有30多年的學術界、政府和商業界的統計和數據科學經驗。他是《應用小波分析與S-PLUS》的合著者,在普林斯頓大學獲得學士學位,並在華盛頓大學獲得統計學博士學位。

Peter Gedeck是Collaborative Drug Discovery的高級數據科學家,專注於開發機器學習算法來預測藥物候選物的生物和物理化學性質。他是《商業分析的數據挖掘》的合著者,在德國的埃爾朗根-紐倫堡大學獲得化學博士學位,並在德國的Fernuniversität Hagen獲得數學博士學位。