An Introduction to Data Science
eBook - ePub

An Introduction to Data Science

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

An Introduction to Data Science

About this book

An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions.

 

The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks.  


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

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. About the Authors
  9. Introduction Data Science, Many Skills
  10. 1 About Data
  11. 2 Identifying Data Problems
  12. 3 Getting Started with R
  13. 4 Follow the Data
  14. 5 Rows and Columns
  15. 6 Data Munging
  16. 7 Onward with RStudio®
  17. 8 What’s My Function?
  18. 9 Beer, Farms, and Peas and the Use of Statistics
  19. 10 Sample in a Jar
  20. 11 Storage Wars
  21. 12 Pictures Versus Numbers
  22. 13 Map Mashup
  23. 14 Word Perfect
  24. 15 Happy Words?
  25. 16 Lining Up Our Models
  26. 17 Hi Ho, Hi Ho—Data Mining We Go
  27. 18 What’s Your Vector, Victor?
  28. 19 Shiny® Web Apps
  29. 20 Big Data? Big Deal!
  30. Index