MATH 2250 Mathematical Foundations of Data Science

Name: Mathematical Foundations of Data Science
Rating: 4.44 (164 reviews)

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
Offered: null

Requirements fulfilled

Biology B.S.: Quantitative corequisite options (Current University Catalog; students should confirm their catalog year)

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Official evaluation summary

4.44 / 5

Based on 164 aggregate responses from BC Avalanche/Blue evaluations.

Data freshness

Course and evaluation data last updated 2026-08-02. Source details and limitations are documented in Data Sources and Methodology.

Instructors

Caleb Ashley
Fall 2026
Liyang Zhang
Spring 2026, Fall 2025, Spring 2025, Fall 2024, Spring 2024, Fall 2023 · Official rating 4.69/5

Sections

Section 01
Fall 2026 · Caleb Ashley · Gasson Hall 301 MWF 10:00AM-10:50AM · Offered