R for Business Analytics

This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples.

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.

The book utilizes Albert Einstein's famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.

Covers full spectrum of R packages related to business analytics Step-by-step instruction on the use of R packages, in addition to exercises, references, interviews and useful links Background information and exercises are all applied to practical business analysis topics, such as code examples on web and social media analytics, data mining, clustering and regression models Includes supplementary material: sn.pub/extras

Autorentext

Ajay Ohri is the founder of analytics startup Decisionstats.com. He has pursued graduate studies at the University of Tennessee, Knoxville and the Indian Institute of Management, Lucknow. In addition, Ohri has a mechanical engineering degree from the Delhi College of Engineering. He has interviewed more than 100 practitioners in analytics, including leading members from all the analytics software vendors. Ohri has written almost 1300 articles on his blog, besides guest writing for influential analytics communities. He teaches courses in R through online education and has worked as an analytics consultant in India for the past decade. Ohri was one of the earliest independent analytics consultant in India, and his current research interests include spreading open source analytics, analyzing social media manipulation, simpler interfaces to cloud computing and unorthodox cryptography.

Zusammenfassung
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.

Inhalt
Why R.- R Infrastructure.- R Interfaces.- Manipulating Data.- Exploring Data.- Building Regression Models.- Data Mining using R.- Clustering and Data Segmentation.- Forecasting and Time-Series Models.- Data Export and Output.- Optimizing your R Coding.- Additional Training Literature.- Appendix.