FIN 4770 · Not scheduled for Fall 2026
Programming for FinTech
From basic programming concepts to financial analysis in code, using the Unix shell, R, Python, and generative AI.
- Term
- Not scheduled for Fall 2026
- Level
- Undergraduate
- Meets
- Mon / Wed 12:30–13:45 · BSN 2205
- Office hours
- By appointment · BSN 3127
Overview
The course builds a working understanding of programming for finance, FinTech, and business more broadly. Students start from basic programming concepts and methods and move into practical financial analysis, working across the three languages that matter most in this field — the Unix shell, R, and Python — and learning to use generative AI to work faster without losing track of what the code does. The technical skills it develops, financial modelling, quantitative analysis, and technical documentation for business communication, are the ones the technology-heavy side of finance actually asks for.
The schedule follows the syllabus in blocks rather than individual weeks, since the pace through each language depends on the class.
I am not teaching this course this year. 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 | Unix shell — course introduction and installation · Git and GitHub · command line tools · file systems and paths · calling web APIs | — |
| 5–14 | R — objects, symbols, values, and attributes · object classes, vectors, lists, and subsetting · functions and loops · pipe chaining · control structures · logicals, integers, and numerics · dates and times · strings and regular expressions · data import and export · data frames and manipulation · visualization · LLMs for sentiment analysisFinance applications: cash flows, time value of money, free cash flow and ratio analysis, bond pricing, CAPM, portfolio analysis and sorting, textual analysis, options pricing, crypto analysis | — |
| 15 | Python and LLMs — converting your R knowledge to Python · object-oriented programmingApplications: prompt engineering, AI code generation and translation, debugging | — |
Grading
| Component | Weight |
|---|---|
| Participation | 10% |
| Quizzes | 20% |
| Assignments | 15% |
| Midterm | 25% |
| Final exam | 30% |
Resources
- R for Data Science, 2nd editionWickham & Grolemund; free online
- Pro GitVersion control reference