The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
5643. Text Analytics
3.00 credits
Prerequisites: EPSY 5641. Recommended preparation: This course requires an understanding of introductory statistics and regression at the level of EPSY 5605 and EPSY 5610 as well as some prior experience with statistical programming in a language like R or Python.
Grading Basis: Graded
This course provides an applied introduction to text analytics with special emphasis on its application to education. Students will learn to use common toolkits in the Python ecosystem to analyze large-scale text data in order to generate insights into educational, cognitive, and social processes.
Last Refreshed: 28-MAR-24 05.20.12.635703 AM
Term | Class Number | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Enrollment | Location | Credits | Grading Basis | Notes | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1243 9942 1 001 | Spring 2024 | 9942 | Storrs | In Person | Anglin, Kylie | 001 | Reg | We 2:30pm‑5:00pm |
15/20 | FSB 202 | 3.00 | Graded | EPSY 5641. Recommended preparation: This course requires an understanding of introductory statistics and regression at the level of EPSY 5605 and EPSY 5610 as well as some prior experience with statistical programming in a language like R or Python |