MATH2250 · Mathematics
Morrissey College of Arts & Sciences
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 experience
Averages use the original five-point historical evaluation scale.
Organization
4.6 / 5
How well the course was organized
Challenge
4.6 / 5
How intellectually challenging students found it
Attendance
4.6 / 5
How necessary attendance was
Assignments
4.7 / 5
How helpful assignments were
Weekly effort
~5
hours per week
Estimated from the original workload response buckets. Individual sections may differ.
Instructor options
Ratings below reflect only recovered evaluations connected to this course.
Across time
Section-level results available in the recovered archive.
Spring 2025
1 sectionFall 2024
1 sectionSpring 2024
1 sectionFall 2023
1 section