Mathematics for Data Science

Mathematics for Data Science

88-105-DS01-H-0825

Master (UNI)

MA Data Science

Modul

Mathematisch-Geographische Fakultät

Oliver, Marcel

10

Students
• Have a structured understanding of university mathematics, with a focus on advanced linear algebra and analysis in metric spaces
• Are able to communicate and reason about these concepts efficiently, using contemporary mathematical language and notation
• Are able to navigate the subleties of high- and infinite-dimensional settings in which these concepts might occur
• Can independently solve standard problems
• Are able to generalize standard problems into new settings

• Review of linear algebra, including inner product spaces, least squares, diagonalization and singular value decomposition
• Calculus in Banach spaces (metric space topology, Fréchet-derivative, general Taylor theorem, fundamental theorem of calculus)
• Measures and integration
• Basic harmonic analysis (Fourier transform, sampling, wavelets)
• Review of elementary probability theory

Admission into the program

Undergraduate mathematics education comparable to the mandatory mathematics modules in the KU Bachelor of Data Science program

- lecture (VL) (4 SWS)
- tutorial (UE) (2 SWS)

performance record assessed with at least "sufficient" of portfolio containing:
• written exam (100%)

The workload per module is 300 hours; 1 credit point corresponds to 30 hours.
Contact/Self-study: 5 credit points
Preparation and follow-up work (including coursework): 2 credit points
Exam preparation: 3 credit points

Module grade

WS

• J. Humpherys, T.J. Javis and E.J. Evans, “Foundations of Applied Mathematics”, SIAM. (Selected chapters from Volume 1 and Volume 2) • W. Cheney, “Analysis for Applied Mathematics