Learn statistics by analyzing professional basketball data! In this action-packed book, you'll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language.
“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.
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.
