Seminar Mathematical Data Science
Seminar Mathematische Datenwissenschaft
4 LPjedes SemesterEN
Voraussetzungen
Empfohlen, im Wortlaut
Good command of basic linear algebra, analysis and probability theory is recommended, e.g. as taught in the modules • 11103: Analysis I • 11104: Analysis II • 11101: Lineare Algebra und analytische Geometrie I • 11217: Wahrscheinlichkeitstheorie or 11917: Mathematik W-3 (Statistik) or 11212: Statistics
Inhalte
Selected topics from the field of mathematical data science
Lernziele
On completion of the course, students will have • acquired in-depth technical knowledge of a topic • developed their skills in modeling and in the use and further development of mathematical solution methods • learned how to narrow down and precisely formulate scientific problems as well as effective time management • improved their oral communication skills by speaking freely in front of an audience and in group discussions • improved their skills in the use of scientific word processing systems (LaTeX).
Prüfungsleistung
• Seminar presentation (30-45 min.) including report (8-10 pages), depending on subject (70 %) • Active participation throughout the semester (30%) The module is passed if 75% of the examination requirements are met.
Literatur (3)
- Leo Breiman: Statistical Modeling: The Two Cultures. Statistical Science, Vol. 16, No. 3, 199-215, 2001.
- Brad Efron: Prediction, Estimation and Attribution, Journal of the American Statistical Association, 115(530), 636–655, 2020
- David B. Dunson: Statistics in the big data era: Failures of the machine. Statistics & Probability Letters, Vol. 136, 4-9, 2018.
Bemerkungen
• Study programme Mathematical Data Science M.Sc.: Mandatory module • Study programme Informatik M.Sc.: Compulsory elective module in complex „Seminare oder Praktika" (level 400)
Quelle: Modulbeschreibung der BTU · abgerufen 29.09.2026Original