Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
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.
Quotes
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
Table of contents
- Chapter 1. Getting started
- Chapter 1. Why R?
- Chapter 1. How this book works
- Chapter 1. Summary
- Chapter 2. Exploring data
- Chapter 2. Importing data
- Chapter 2. Wrangling data
- Chapter 2. Variable breakdown
- Chapter 2. Exploratory data analysis
- Chapter 2. Writing data
- Chapter 2. Summary
- Chapter 3. Segmentation analysis
- Chapter 3. Loading packages
- Chapter 3. Importing and viewing data
- Chapter 3. Creating another derived variable
- Chapter 3. Visualizing means and medians
- Chapter 3. Preliminary conclusions
- Chapter 3. Sankey diagram
- Chapter 3. Expected value analysis
- Chapter 3. Hierarchical clustering
- Chapter 3. Summary
- Chapter 4. Constrained optimization
- Chapter 4. Loading packages
- Chapter 4. Importing data
- Chapter 4. Knowing the data
- Chapter 4. Visualizing the data
- Chapter 4. Constrained optimization setup
- Chapter 4. Constrained optimization construction
- Chapter 4. Results
- Chapter 4. Summary
- Chapter 5. Regression models
- Chapter 5. Importing data
- Chapter 5. Knowing the data
- Chapter 5. Identifying outliers
- Chapter 5. Checking for normality
- Chapter 5. Visualizing and testing correlations
- Chapter 5. Multiple linear regression
- Chapter 5. Regression tree
- Chapter 5. Summary
- Chapter 6. More wrangling and visualizing data
- Chapter 6. Importing data
- Chapter 6. Wrangling data
- Chapter 6. Analysis
- Chapter 6. Summary
- Chapter 7. T-testing and effect size testing
- Chapter 7. Importing data
- Chapter 7. Wrangling data
- Chapter 7. Analysis on 2018-19 data
- Chapter 7. Analysis on 2019-20 data
- Chapter 7. Summary
- Chapter 8. Optimal stopping
- Chapter 8. Importing images
- Chapter 8. Importing and viewing data
- Chapter 8. Exploring and wrangling data
- Chapter 8. Analysis
- Chapter 8. Summary
- Chapter 9. Chi-square testing and more effect size testing
- Chapter 9. Importing data
- Chapter 9. Wrangling data
- Chapter 9. Computing permutations
- Chapter 9. Visualizing results
- Chapter 9. Statistical test of significance
- Chapter 9. Effect size testing
- Chapter 9. Summary
- Chapter 10. Doing more with ggplot2
- Chapter 10. Importing and viewing data
- Chapter 10. Salaries and salary cap analysis
- Chapter 10. Analysis
- Chapter 10. Summary
- Chapter 11. K-means clustering
- Chapter 11. Importing data
- Chapter 11. A primer on standard deviations and z-scores
- Chapter 11. Analysis
- Chapter 11. K-means clustering
- Chapter 11. Summary
- Chapter 12. Computing and plotting inequality
- Chapter 12. Loading packages
- Chapter 12. Importing and viewing data
- Chapter 12. Wrangling data
- Chapter 12. Gini coefficients
- Chapter 12. Lorenz curves
- Chapter 12. Salary inequality and championships
- Chapter 12. Salary inequality and wins and losses
- Chapter 12. Gini coefficient bands versus winning percentage
- Chapter 12. Summary
- Chapter 13. More with Gini coefficients and Lorenz curves
- Chapter 13. Importing and viewing data
- Chapter 13. Wrangling data
- Chapter 13. Gini coefficients
- Chapter 13. Lorenz curves
- Chapter 13. For loops
- Chapter 13. User-defined functions
- Chapter 13. Win share inequality and championships
- Chapter 13. Win share inequality and wins and losses
- Chapter 13. Gini coefficient bands versus winning percentage
- Chapter 13. Summary
- Chapter 14. Intermediate and advanced modeling
- Chapter 14. Importing and wrangling data
- Chapter 14. Exploring data
- Chapter 14. Correlations
- Chapter 14. Analysis of variance models
- Chapter 14. Logistic regressions
- Chapter 14. Paired data before and after
- Chapter 14. Summary
- Chapter 15. The Lindy effect
- Chapter 15. Importing and viewing data
- Chapter 15. Visualizing data
- Chapter 15. Pareto charts
- Chapter 15. Summary
- Chapter 16. Randomness versus causality
- Chapter 16. Importing and wrangling data
- Chapter 16. Rule of succession and the hot hand
- Chapter 16. Player-level analysis
- Chapter 16. League-wide analysis
- Chapter 16. Summary
- Chapter 17. Collective intelligence
- Chapter 17. Importing data
- Chapter 17. Wrangling data
- Chapter 17. Automated exploratory data analysis
- Chapter 17. Results
- Chapter 17. Summary
- Chapter 18. Statistical dispersion methods
- Chapter 18. Importing data
- Chapter 18. Exploring and wrangling data
- Chapter 18. Measures of statistical dispersion and intra-season parity
- Chapter 18. Churn and inter-season parity
- Chapter 18. Summary
- Chapter 19. Data standardization
- Chapter 19. Importing and viewing data
- Chapter 19. Wrangling data
- Chapter 19. Standardizing data
- Chapter 19. Summary
- Chapter 20. Finishing up
- Chapter 20. Significance testing
- Chapter 20. Effect size testing
- Chapter 20. Modeling
- Chapter 20. Operations research
- Chapter 20. Probability
- Chapter 20. Statistical dispersion
- Chapter 20. Standardization
- Chapter 20. Summary statistics and visualization
Product information
- Title: Statistics Slam Dunk, Video Edition
- Author(s):
- Release date: January 2024
- Publisher(s): Manning Publications
- ISBN: None
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