INTRODUCTION TO TEXT MINING

Prerequisites & course information

Requisites: Junior standing and satisfied Quantitative Reasoning (QR) A, or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Understand and apply the general natural language processing (NLP) pipeline to process text data Audience: Both Grad & Undergrad 2. Understand and use computational models to represent text contents and meanings Audience: Both Grad & Undergrad 3. Perform exploratory text analysis to understand the contents, topics, and characteristics of a text corpus Audience: Both Grad & Undergrad 4. Design, implement, and evaluate solutions to categorize texts and extract information from texts Audience: Both Grad & Undergrad 5. Understand ethical issues and concerns related to natural language processing and language technologies Audience: Both Grad & Undergrad 6. Critically evaluate and compare different text mining solutions Audience: Graduate Learning Outcomes: 1. Understand and apply the general natural language processing (NLP) pipeline to process text data Audience: Both Grad & Undergrad 2. Understand and use computational models to represent text contents and meanings Audience: Both Grad & Undergrad 3. Perform exploratory text analysis to understand the contents, topics, and characteristics of a text corpus Audience: Both Grad & Undergrad 4. Design, implement, and evaluate solutions to categorize texts and extract information from texts Audience: Both Grad & Undergrad 5. Understand ethical issues and concerns related to natural language processing and language technologies Audience: Both Grad & Undergrad 6. Critically evaluate and compare different text mining solutions Audience: Graduate