Statistics Slam Dunk is an engaging how-to guide for statistical analysis with R. Each chapter contains an end-to-end data science or statistics project delving into NBA data and revealing real-world sporting insights. Written by a former basketball player turned business intelligence and analytics leader, you’ll get practical experience tidying, wrangling, exploring, testing, modeling, and otherwise analyzing data with the best and latest R packages and functions.
In Statistics Slam Dunk you’ll develop a toolbox of R programming skills including:
- Reading and writing data
- Installing and loading packages
- Transforming, tidying, and wrangling data
- Applying best-in-class exploratory data analysis techniques
- Creating compelling visualizations
- Developing supervised and unsupervised machine learning algorithms
- Executing hypothesis tests, including t-tests and chi-square tests for independence
- Computing expected values, Gini coefficients, z-scores, and other measures
If you’re looking to switch to R from another language, or trade base R for tidyverse functions, this book is the perfect training coach. Much more than a beginner’s guide, it teaches statistics and data science methods that have tons of use cases. And just like in the real world, you’ll get no clean pre-packaged data sets in Statistics Slam Dunk. You’ll take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team.
Foreword by Thomas W. Miller.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Statistics Slam Dunk is a data science manual with a difference. Each chapter is a complete, self-contained statistics or data science project for you to work through—from importing data, to wrangling it, testing it, visualizing it, and modeling it. Throughout the book, you’ll work exclusively with NBA data sets and the R language, applying best-in-class statistics techniques to reveal fun and fascinating truths about the NBA.
About the book
Is losing basketball games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Does spending more on player salaries translate into a winning record? You’ll answer all these questions and more. Plus, R’s visualization capabilities shine through in the book’s 300 plots and charts, including Pareto charts, Sankey diagrams, Cleveland dot plots, and dendrograms.
What's inside
- Transforming, tidying, and wrangling data
- Applying best-in-class exploratory data analysis techniques
- Developing supervised and unsupervised machine learning algorithms
- Executing hypothesis tests and effect size tests
About the reader
For readers who know basic statistics. No advanced knowledge of R—or basketball—required.
About the author
Gary Sutton is a former basketball player who has built and led high-performing business intelligence and analytics organizations across multiple verticals.
Table of Contents
1 Getting started
2 Exploring data
3 Segmentation analysis
4 Constrained optimization
5 Regression models
6 More wrangling and visualizing data
7 T-testing and effect size testing
8 Optimal stopping
9 Chi-square testing and more effect size testing
10 Doing more with ggplot2
11 K-means clustering
12 Computing and plotting inequality
13 More with Gini coefficients and Lorenz curves
14 Intermediate and advanced modeling
15 The Lindy effect
16 Randomness versus causality
17 Collective intelligence
