Table of contents

  1. Front Matter
    Pages i-xviii
  2. A. Ohri
    Pages 1-7
  3. A. Ohri
    Pages 9-23
  4. A. Ohri
    Pages 25-55
  5. A. Ohri
    Pages 57-101
  6. A. Ohri
    Pages 103-169
  7. A. Ohri
    Pages 171-191
  8. A. Ohri
    Pages 193-223
  9. A. Ohri
    Pages 225-240
  10. A. Ohri
    Pages 241-258
  11. A. Ohri
    Pages 259-262
  12. A. Ohri
    Pages 263-280
  13. A. Ohri
    Pages 281-291
  14. A. Ohri
    Pages 293-307
  15. Back Matter
    Pages 309-312

About this book

Introduction

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. 

 

This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.  

Keywords

Business Analytics Data Mining Data Visualization Forecasting GUI Graphical User Interface R software Text Mining

Authors and affiliations

  • A Ohri
    • 1
  1. 1.Founder- Decisionstats.comDelhiIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4343-8
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4342-1
  • Online ISBN 978-1-4614-4343-8
  • About this book