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Modern Data Science with R

Medium: Buch
ISBN: 978-0-367-19149-8
Verlag: Taylor & Francis Ltd
Erscheinungstermin: 14.04.2021
Lieferfrist: bis zu 10 Tage

From a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician).

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.

The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.


Produkteigenschaften


  • Artikelnummer: 9780367191498
  • Medium: Buch
  • ISBN: 978-0-367-19149-8
  • Verlag: Taylor & Francis Ltd
  • Erscheinungstermin: 14.04.2021
  • Sprache(n): Englisch
  • Auflage: 2. Auflage 2021
  • Serie: Chapman & Hall/CRC Texts in Statistical Science
  • Produktform: Gebunden
  • Gewicht: 1464 g
  • Seiten: 650
  • Format (B x H x T): 179 x 260 x 36 mm
  • Ausgabetyp: Kein, Unbekannt
  • Vorauflage: 978-1-4987-2448-7
Autoren/Hrsg.

Autoren

I Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.