MFIN 2270 Data Analytics in Finance
Corporations, investment banks, and asset management firms increasingly base their operational and investment decisions on the statistical analysis of " big data." In this course, you will be introduced to Python, a popular open-source modern programming language, and learn how to use it to extract data from online sources, estimate univariate and multivariate regressions on real-world financial data, and perform Monte Carlo simulations.
Course overview
- Department
- Finance
- School
- CSOM
- Credits
- 3
- Level
- Undergraduate
- Offered
- Annually
Catalog details
Requirements fulfilled
Course evaluations
- Course rating
- 4.08 / 5 · Very Good
- Instructor rating
- 4.32 / 5 · Very Good
- Terms evaluated
- 10
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Ran Xu | 4.3 | 4.6 | 4.5 | 4.5 | 4.6 | 4.7 | 4.5 | 4.6 | 1–3 hrs/week | 21 of 27 |
| Fall 2025 | Jonathan Reuter | 4.1 | 4.6 | 4.4 | 4.7 | 4.8 | 4.7 | 4.5 | 4.6 | 4–6 hrs/week | 51 of 69 |
| Spring 2025 | Ran Xu | 3.8 | 3.8 | 4.5 | 4.4 | 4.7 | 4.5 | 4.3 | 4.1 | 4–6 hrs/week | 36 of 45 |
| Fall 2024 | Jonathan Reuter | 4.2 | 4.5 | 4.5 | 4.6 | 4.8 | 4.6 | 4.5 | 4.5 | 4–6 hrs/week | 96 of 110 |
| Spring 2024 | Ran Xu | 4.1 | 4.4 | 4.6 | 4.5 | 4.5 | 4.6 | 4.5 | 4.2 | 4–6 hrs/week | 81 of 90 |
| Fall 2023 | Jonathan Reuter | — | — | — | — | — | — | — | — | — | 78 of 100 |
| Spring 2023 | Ran Xu | 4.0 | 4.2 | 4.7 | 4.8 | 4.7 | 4.8 | 4.5 | 4.3 | 4–6 hrs/week | 76 of 84 |
| Fall 2022 | Jonathan Reuter | 4.1 | 4.5 | 4.5 | 4.5 | 4.6 | 4.6 | 4.4 | 4.3 | 4–6 hrs/week | 89 of 109 |
| Spring 2022 | Ran Xu | 4.0 | 4.2 | 4.6 | 4.6 | 4.7 | 4.5 | 4.4 | 4.2 | 4–6 hrs/week | 61 of 95 |
| Fall 2021 | Gang Nathan Dong | 3.8 | 3.9 | 4.3 | 4.0 | 4.5 | 4.3 | 4.2 | 4.5 | 1–3 hrs/week | 73 of 103 |
Data freshness
Instructors
Sections
- Section 01
- Section 02
- Section 03