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.
In Statistics Slam Dunk you’ll develop a toolbox of R data 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
Execute hypothesis tests, including t-tests and chi-square tests for independence
Compute expected values, Gini coefficients, and z-scores
Statistics Slam Dunk upgrades your R data science skills by taking on practical analysis challenges based on NBA game and player data. Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these. And just like in the real world, you’ll get no clean pre-packaged datasets 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
Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modeling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R’s visualizations are stunning, with best-in-class plots and charts.
about the book
Statistics Slam Dunk: Statistical analysis with R on real NBA data is an interesting and engaging how-to guide for statistical analysis using R. It’s packed with practical statistical techniques, each demonstrated using real-world data taken from NBA games. In each chapter, you’ll discover a new (and sometimes surprising!) insight into basketball, with careful step-by-step instructions on how to generate those revelations.
You’ll get practical experience cleaning, manipulating, exploring, testing, and otherwise analyzing data with base R functions and useful R packages. R’s visualization capabilities shine through in the book’s 300 visualizations, and almost 30 plots and charts including Pareto charts and Sankey diagrams. Much more than a beginner’s guide, this book explores advanced analytics techniques and data wrangling packages. You’ll find yourself returning again and again to use this book as a handy reference!
about the reader
Requires a beginning knowledge of basic statistics concepts. No advanced knowledge of statistics, machine learning, R–or basketball–required.
about the author
Gary Sutton is a vice president for a leading financial services company. He has built and led high-performing business intelligence and analytics organizations across multiple verticals, where R was the preferred programming language for predictive modeling, statistical analyses, and other quantitative insights. Gary earned his undergraduate degree from the University of Southern California, a Masters from George Washington University, and a second Masters in Data Science, from Northwestern University.
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