Data Science for Business With R
eBook - ePub

Data Science for Business With R

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Data Science for Business With R

About this book

Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business's customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available.

Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field.

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Yes, you can access Data Science for Business With R by Jeffrey S. Saltz,Jeffrey M. Stanton in PDF and/or ePUB format, as well as other popular books in Economics & Marketing Research. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Brief Contents
  7. Detailed Contents
  8. Instructor Preface
  9. Introduction: Data Science, Many Skills
  10. 1 Begin at the Beginning With R
  11. 2 Rows and Columns
  12. 3 Data Munging
  13. 4 What’s My Function?
  14. 5 Beer, Farms, Peas, and the Use of Statistics
  15. 6 Sample in a Jar
  16. 7 Storage Wars
  17. 8 Pictures Versus Numbers
  18. 9 Map Mashup
  19. 10 Lining Up Our Models
  20. 11 What’s Your Vector, Victor?
  21. 12 Hi Ho, Hi Ho—Data Mining We Go
  22. 13 Word Perfect (Text Mining)
  23. 14 Shiny® Web Apps
  24. 15 Time for a Deep Dive
  25. Index