Spring 2025

Text Analytics

Listed in: Mathematics and Statistics, as STAT-325


Nicholas J. Horton (Section 01)


Text analytics is a form of natural language processing that utilizes computational systems to process, find patterns, classify, and model information contained within unstructured text documents. These methods are attractive because they can be applied to large collections of documents that would be infeasible to undertake by hand. In this course, students will interact with a variety of text sources with the goal of finding insights, identifying patterns, extracting meaning, and communicating results. Topics will include data wrangling, tokenization, regular expressions, n-grams, named entity recognition, sentiment analysis, topic modeling, classification, cloud computing, dynamic visualization tools, and ethical considerations.

Students may not receive credit for both STAT210 and STAT325

Spring semester. Professor Horton

How to handle overenrollment: null

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, quizzes or exams, group work, use of computational software, projects, oral presentations

Course Materials


Other years: Offered in Spring 2025