MATH 2250 Mathematical Foundations of Data Science
Introduction to the mathematical foundations of data science, including calculus, linear algebra and probability. The first part of the course covers linear algebra, including matrices, systems of linear equations, vector spaces, and eigenvalues and eigenvectors. The second part of the course introduces random variables and provides an introduction to calculus based probability. The third part of the course introduces optimization techniques used in data science. Prerequisite: MATH1101 or MATH1103 or equivalent Calculus II background.
Course overview
- Department
- Mathematics
- School
- MCAS
- Credits
- 3
- Level
- Undergraduate
Requirements fulfilled
Course evaluations
- Course rating
- 4.44 / 5 · Very Good
- Instructor rating
- 4.69 / 5 · Excellent
- Terms evaluated
- 6
| 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 | Liyang Zhang | 4.5 | 4.8 | 4.8 | 4.7 | 4.7 | 4.8 | 4.8 | 4.7 | 4–6 hrs/week | 38 of 46 |
| Fall 2025 | Liyang Zhang | 4.7 | 4.9 | 4.7 | 4.7 | 4.6 | 4.6 | 4.6 | 4.6 | 4–6 hrs/week | 25 of 33 |
| Spring 2025 | Liyang Zhang | 4.2 | 4.5 | 4.7 | 4.6 | 4.5 | 4.6 | 4.5 | 4.3 | 4–6 hrs/week | 29 of 36 |
| Fall 2024 | Liyang Zhang | 4.4 | 4.5 | 4.4 | 4.5 | 4.7 | 4.9 | 4.6 | 4.8 | 4–6 hrs/week | 16 of 20 |
| Spring 2024 | Liyang Zhang | 4.3 | 4.6 | 4.7 | 4.5 | 4.6 | 4.7 | 4.4 | 4.7 | 4–6 hrs/week | 41 of 42 |
| Fall 2023 | Liyang Zhang | — | — | — | — | — | — | — | — | — | 15 of 16 |