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:
**
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Organized by tool or technique, with the basic techniques presented
first and the more sophisticated techniques presented later
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Uses Excel and R for datasets in case studies and exercises
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Features the CRISP-DM data mining standard with early chapters for
conducting the preparatory steps in data mining
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Companion files with numerous datasets and figures from the text.
The companion files are available online by emailing the publisher with
proof of purchase at info@merclearning.com.
