Название: Statistics Slam Dunk: Statistical analysis with R on real NBA data (Final Release)Автор: Gary Sutton
Издательство: Manning Publications
Год: 2024
Страниц: 672
Язык: английский
Формат: pdf (true)
Размер: 16.8 MB
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!
With its many packages and tools, R is the obvious choice for this book. We can expect Sutton’s work to be well-received by R enthusiasts, especially those who wish to transition from base R to tidyverse functions. The mechanics of R programming are well-illustrated in Statistics Slam Dunk.
Who should read this book:
It goes without saying that some prior exposure to R, or at least some previous experience with another statistical programming language, will help you get the most out of this book. In addition, some foundational knowledge of basic statistical concepts, a background in data visualization and best practices thereof, and even a basic understanding of the game of basketball and some familiarity with basic basketball statistics will be beneficial. However, Statistics Slam Dunk presumes very little. R and RStudio, the integrated development environment (IDE) for R, are both free downloads. Statistics Slam Dunk presumes you can download software and then install it by clicking Next a few times. Otherwise, every operation is explained at a level of detail that is most appropriate for undergraduate and graduate students with little or no background in either R or statistics, junior-level or mid-level data scientists and data analysts with some prior R experience looking to upskill themselves, and other data scientists and data analysts transitioning from another programming language.
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