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 analysisData and sampling distributionsStatistical experiments and significance testingRegression and predictionClassificationStatistical machine learningUnsupervised learning

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