Text Analytics for Business Decisions - 2 Angebote vergleichen

Lieferung aus: Vereinigte Staaten von Amerika, In Stock, plus shipping.
With the rise in data science development,we now have many remarkable techniques and tools to extend data analysis fromnumeric and categorical data to textual data. Sifting through the open-endedresponses from a survey, for example, was an arduous process when performed byhand. Using a case study approach, thisbook 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 basictext 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.

Lieferung aus: Vereinigte Staaten von Amerika, In Stock, plus shipping.
With the rise in data science development,we now have many remarkable techniques and tools to extend data analysis fromnumeric and categorical data to textual data. Sifting through the open-endedresponses from a survey, for example, was an arduous process when performed byhand. Using a case study approach, thisbook 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 basictext 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.