Synopsis
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.
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
"synopsis" may belong to another edition of this title.
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.
From the Back Cover
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 is packed with practical statistical techniques, each demonstrated using real-world data taken from NBA games. In each chapter, you will discover a new (and sometimes surprising!) insight into basketball, with careful step-by-step instructions on how to generate those revelations. You will get practical experience cleaning, manipulating, exploring, testing, and otherwise analysing data with base R functions and useful R packages. R's visualisation 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 will find yourself returning again and again to use this book as a handy reference!
About the readerRequires a beginning knowledge of basic statistics concepts. No advanced knowledge of statistics, machine learning, R - or basketball - required.
"About this title" may belong to another edition of this title.
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Hardback. Condition: New. 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 dataInstalling and loading packagesTransforming, tidying, and wrangling dataApplying best-in-class exploratory data analysis techniquesCreating compelling visualizationsDeveloping supervised and unsupervised machine learning algorithmsExecute hypothesis tests, including t-tests and chi-square tests for independenceCompute 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. Seller Inventory # LU-9781633438682
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Hardcover. Condition: new. Hardcover. 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 dataInstalling and loading packagesTransforming, tidying, and wrangling dataApplying best-in-class exploratory data analysis techniquesCreating compelling visualizationsDeveloping supervised and unsupervised machine learning algorithmsExecute hypothesis tests, including t-tests and chi-square tests for independenceCompute 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781633438682
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