CLASS 190: LIT TEXT MINING
University of California, Santa Barbara
Literary text-mining is a decades-old field that uses quantitative methods to answer enduring literary questions about texts’ meaning, significance, p olitics, context, and more. Text- mining methods offer researchers the chan ce to answer new questions at larger scales. This course introduces student s to a variety of computational methods, from foundational counting methods to machine-learning. We will investigate several literary datasets from th e ancient world using Python programming language. No prior experience in C lassics, literary theory or Python is required; different paths through the course are available for students with significant coding experience.
Average GPA: 3.86
Grade distribution records: 22 students across 1 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 7 | 31.8% |
| A | 13 | 59.1% |
| B | 1 | 4.5% |
| C | 1 | 4.5% |
Based on 22 student grade records across 1 term and 1 professor.
Instructors
- Lamar A 22 students, Average GPA 3.86