With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today's most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises.

FEATURES:

Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later
Uses Excel and R for datasets in case studies and exercises
Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining
Companion files with numerous datasets and figures from the text