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Statistics Slam Dunk you own this product

Statistical analysis with R on real NBA data

5 reviews

Gary Sutton
Foreword by Thomas W. Miller

January 2024
ISBN 9781633438682
672 pages

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Look inside

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.

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.

eBook

$47.99 $26.39

include audio $24.99 $13.74

In this journey of exploration, every computer scientist will find a valuable ally in understanding the language of data.

Kim Lokøy, areo

Transcends other R titles by revealing the hidden narratives that lie within the numbers.

Christian Sutton, Shell International Exploration and Production

Seamlessly blending theory and practical insights, this book serves as an indispensable guide for those venturing into the field of data analytics.

Juan Delgado, Sodexo BRS

“This book teaches statistics concepts by using case studies and data from NBA. The examples dispersed throughout the book make it an enjoyable read, despite the heavy mathematical statistics involved. Highly recommended for anyone heading in the machine learning direction, to get a better understanding of the underlying concepts.”

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