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Statistics Playbook
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Book Details

Format Hardback or Cased Book
ISBN-10 1633438686
ISBN-13 9781633438682
Publisher Manning Publications
Imprint Manning Publications
Country of Manufacture GB
Country of Publication GB
Publication Date Feb 9th, 2024
Print length 672 Pages
Weight 1,234 grams
Dimensions 23.40 x 19.10 x 4.00 cms
Ksh 10,050.00
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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.

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.


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