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.
Practical Statistics explains how to apply key statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
If you’re familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn a deeper statistical perspective, this quick reference bridges the gap in an accessible, readable format, and covers:
- Exploratory data analysis
- Data and sampling distributions
- Statistical experiments and significance testing
- Regression and prediction
- Classification
- Statistical machine learning
- Unsupervised learning
The first edition of this book was a best-seller for O’Reilly and was translated into Japanese; the second edition adds Python. Co-Authored by Peter Bruce, Andrew Bruce, and Peter Gedeck.
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