MATHEMATICS FOR DATA SCIENCE
Mathematical background for students wanting to take Data Science 3000A/B, but missing background in linear algebra and calculus. Vector and matrix algebra, norms, linear dependence, inverses, vector spaces, eigenvectors and eigenvalues, Gradients, Hessians, basics of optimization. All concepts are explained in the context of data science examples.
Prerequisites & course information
Antirequisite(s): Mathematics 1600A/B, Mathematics 1700A/B, Numerical and Mathematical Methods 1411A/B, the former Applied Mathematics 1411A/B. Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (1000 and higher) with a minimal grade of 60%. Data Science 2000A/B or Integrated Science 2002B can be used to fulfil 0.5 of the requirements. Extra Information: 3 lecture hours/week, 1 tutorial hour/week. Course Weight: 0.50
Sources
- Course catalogue ↗2026-2027