TEXT ANALYTICS AND BUSINESS APPLICATION
An introduction to text mining and natural language processing for business applications. Provides an overview of text data and steps to make it usable and approaches for making text data useful in descriptive and predictive analytics applications. Topics include representation approaches, topic modeling, and an overview of key applications of natural language processing, such as chatbots and recommender systems.
Topics
- representation approaches
- topic modeling
- and an overview of key applications of natural language processing
- such as chatbots and recommender systems
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: Graduate/professional standing or declared in graduate Business Exchange program Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Retrieve, assemble, and clean text data for use in analytics applications. Audience: Graduate 2. Utilize text representation approaches, including the bag of words and Term Frequency - Inverse Document Frequency (TF-IDF), for visualizing text data. Audience: Graduate 3. Build classification and regression models using features created from text data. Audience: Graduate 4. Utilize Latent Dirichlet Application (LDA) for topic modeling in the context of a business application. Audience: Graduate Learning Outcomes: 1. Retrieve, assemble, and clean text data for use in analytics applications. Audience: Graduate 2. Utilize text representation approaches, including the bag of words and Term Frequency - Inverse Document Frequency (TF-IDF), for visualizing text data. Audience: Graduate 3. Build classification and regression models using features created from text data. Audience: Graduate 4. Utilize Latent Dirichlet Application (LDA) for topic modeling in the context of a business application. Audience: Graduate
Sources
- Course catalogue ↗2026-2027