FIN 4773 · Not scheduled for Fall 2026
Big Data and Machine Learning in Finance
Machine learning concepts in finance, applied to market data through lab projects in R and Quarto.
- Term
- Not scheduled for Fall 2026
- Level
- Undergraduate
- Meets
- Mon / Wed 15:30–16:45 · BSN 118
- Office hours
- By appointment · BSN 3127
Overview
An introduction to financial technology built around the concepts of machine learning in finance. Students apply big data and machine learning techniques to market data in hands-on lab sessions, covering data wrangling, model validation, and the technical writing that turns a result into something a reader can follow.
This is the undergraduate counterpart to FIN 6776, which covers the same ground at greater depth and pace.
Per university policy, the syllabus and course materials (handouts, slides, code, and course repositories) aren’t posted publicly. When the course runs, enrolled students receive the course page by invitation through Canvas. If you’d like a syllabus or have any questions about a course, I’m happy to help. Just email me at gson@usf.edu.
Schedule
| Wk | Topic | Materials |
|---|---|---|
| 1–4 | R and Quarto — financial data manipulation basics · technical documentation for finance · pivoting and tidying financial data · visualizationApplications: time-series visualization, CAPM, portfolio performance analysis | — |
| 5–7 | Big data — financial databases and big data workflows · SQL and database backends | — |
| 8–10 | Unsupervised learning — introduction to machine learning in finance · k-means clustering and PCAApplications: credit risk analysis with ML, stock classification with ML | — |
| 11–15 | Supervised learning — linear and logistic regression · decision trees · ensemble methods · random forests and gradient boostingApplications: Fama–French factor models, real estate pricing, credit decision models | — |
Grading
| Component | Weight |
|---|---|
| Participation | 10% |
| ML assignment | 15% |
| In-class quizzes | 30% |
| Lab problems | 15% |
| Final exam | 30% |
Resources
- R for Data Science, 2nd editionWickham & Grolemund; free online
- Quarto documentationLab reports are Quarto documents