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Module Number:
| 13798
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| Module Title: | Seminar Mathematical Data Science |
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Seminar Mathematische Datenwissenschaft
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| Department: |
Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
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| Responsible Staff Member: | -
Prof. Dr. rer. nat. Hartmann, Carsten
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every semester
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| Credits: |
4
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| Learning Outcome: | 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).
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| Contents: | Selected topics from the field of mathematical data science |
| Recommended Prerequisites: | 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
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Seminar
/ 2 Hours per Week per Semester
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Self organised studies
/ 90 Hours
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| Teaching Materials and Literature: | - 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.
Additional literature will be provided at the beginning of the semester. |
| Module Examination: | Continuous Assessment (MCA) |
| Assessment Mode for Module Examination: | - 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. |
| Evaluation of Module Examination: | Study Performance – ungraded |
| Limited Number of Participants: | 40 |
| Part of the Study Programme: | -
Master (research-oriented) /
Mathematical Data Science /
PO 2025
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| Remarks: | - Study programme Mathematical Data Science M.Sc.: Mandatory module
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| Module Components: | - Seminar Mathematical Data Science
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| Components to be offered in the Current Semester: | |
