000			03670nam a22002177a 4500
005			20250719162610.0
008			250719b |||||||| |||| 00| 0 eng d
020			_a9781032116556
082			_a006.312 _bIRI
100			_aIrizarry, Rafael A. _920955
245			_aIntroduction to data science: _bdata wrangling and visualization with R
250			_a2nd
260			_bCRC Press _aNew York _c2025
300			_axviii, 327 p.
365			_aGBP _b59.99
500			_aTable of contents: Preface Acknowledgements Introduction Part 1: R 1. Getting started 2. R basics 3. Programming basics 4. The tidyverse 5. data.table 6. Importing data Part 2: Data Visualization 7. Visualizing data distributions 8. ggplot2 9. Data visualization principles 10. Data visualization in practice Part 3: Data Wrangling 11. Reshaping data 12. Joining tables 13. Parsing dates and times 14. Locales 15. Extracting data from the web 16. String processing 17. Text analysis Part 4: Productivity Tools 18. Organizing with Unix 19. Git and GitHub 20. Reproducible projects [https://www.routledge.com/Introduction-to-Data-Science-Data-Wrangling-and-Visualization-with-R/Irizarry/p/book/9781032116556?srsltid=AfmBOoo-L-iehVO2r3D587XSXhVxfXa-IIoDPiWCSAMMBX-ExQdxbOgE]
520			_aUnlike the first edition, the new edition has been split into two books. Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. The new edition includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book will cover topics including probability, statistics and prediction algorithms with R. Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist’s experience. For each of the skills covered, we start by asking specific questions and answer these through data analysis. Examples of the case studies included in the book are: US murder rates by state, self-reported student heights, trends in world health and economics, and the impact of vaccines on infectious disease rates. This book is meant to be a textbook for a first course in Data Science. No previous knowledge of R is necessary, although some experience with programming may be helpful. To be a successful data analyst implementing these skills covered in this book requires understanding advanced statistical concepts, such as those covered the second book. If you read and understand all the chapters and complete all the exercises in this book, and understand statistical concepts, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert. (https://www.routledge.com/Introduction-to-Data-Science-Data-Wrangling-and-Visualization-with-R/Irizarry/p/book/9781032116556?srsltid=AfmBOoo-L-iehVO2r3D587XSXhVxfXa-IIoDPiWCSAMMBX-ExQdxbOgE)
650			_aData Secience _924707
650			_aR programming language _924708
942			_cBK _2ddc
999			_c10103 _d10103