Overview

Learn statistics by analysing professional basketball data! Statistics Slam Dunk is an action-packed book that will help you build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. This textbook will upgrade your R data science skills by taking on practical analysis challenges based on NBA game and player data. You will take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. And just like in the real world, you will get no clean pre-packaged datasets in this book. You will 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 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. 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 modelling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R's visualisations are stunning, with best-in-class plots and charts.

Full Product Details

9781633438682

Table of Contents

Reviews

"""An excellent way to learn exploratory data analysis and statistical analysis with R and sports statistics from the NBA."" Bob Quintus ""This book is very impressive. Different from other similar books, this book integrates the technology of R language through storytelling."" Chen Sun ""A great example of using R and applying it to a machine learning problem."" John Williams ""Very interesting subject matter. The author’s enthusiasm for it really shows."" Lachman Dhalliwal ""For users looking to get experience with real world datasets, this book will provide a great methodological approach."" Eli Mayost"

Author Information

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 modelling, 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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