A second edition, *Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python* is scheduled for publication on June 9, 2020.

Who is this book for?

According to the preface:

"This book is aimed at the data scientist with some familiarity with the R
programming language, and with some prior (perhaps spotty or ephemeral)
exposure to statistics."

"Two goals underlie this book:
1) to lay out, in digestible, navigable, and easily referenced form, key concepts from statistics that are relevant to data science, and
2) to explain which concepts are important and useful from a data science perspective, which are less so, and why."

Book outline

1. Exploratory Data Analysis Elements of Structured Data Rectangular Data Estimates of Location Estimates of Variability Exploring the Data Distribution Exploring Binary and Categorical Data Correlation Exploring Two or More Variables Summary 2. Data and Sampling Distributions Random Sampling and Sample Bias Selection Bias Sampling Distribution of a Statistic The Bootstrap Confidence Intervals Normal Distribution Long-Tailed Distributions Student’s t-Distribution Binomial Distribution Poisson and Related Distributions Summary 3. Statistical Experiments and Significance TestingA/B Testing[March 23, 2020] Hypothesis Tests[March 25, 2020] Resampling[March 26, 2020] Statistical Significance and P-values t-Tests Multiple Testing Degrees of Freedom ANOVA Chi-Square Test Multi-Arm Bandit Algorithm Power and Sample Size Summary 4. Regression and Prediction Simple Linear Regression Multiple Linear Regression Prediction Using Regression Factor Variables in Regression Interpreting the Regression Equation Testing the Assumptions: Regression Diagnostics Polynomial and Spline Regression Summary 5. Classification Naive Bayes Discriminant Analysis Logistic Regression Evaluating Classification Models Strategies for Imbalanced Data Summary 6. Statistical Machine Learning K-Nearest Neighbours Tree Models Bagging and the Random Forest Boosting Summary 7. Unsupervised Learning Principal Components Analysis K-Means Clustering Hierarchical Clustering Model-Based Clustering Scaling and Categorical Variables Summary

Impressions

Strengths

Book is divided up into fairly bite-sized concepts, which provide useful chunks to guide learning

Weaknesses

The amount of equations included in the text can interrupt flow and be confusing to readers without a significant statistics background