Contents
The achievement of the learning goals is pursued by practicing on the basis of personally assigned in-depth scientific topics as well as by actively participating in the presentation dates. The organizer will choose subject areas within the field of Data-Science and provide scientific papers to students to work through.
Learning outcomes
Skills: On the basis of suitable literature, in particular original scientific articles, students independently familiarize themselves with a topic in data-science, classify and narrow down the topic appropriately and develop a critical evaluation. Students work out concepts, procedures and results of a given topic clearly and with appropriate formalisms in a timely manner and to a defined extent in depth in writing; Evidence of independent development by presenting self-selected examples. Descriptive oral presentation of an in-depth data science topic using suitable media and examples in a given format.
Necessary prerequisites
completed bachelor degree
Recommended prerequisites
lectures offered by the chair
ECTS credits | 6 |
Graded | yes |
Workload | 180h |
Language | English |
Form of assessment | Written report with oral presentation |
Restricted admission | yes |
Further information | Website of the Chair / “Student Portal” |
Examiner Performing lecturer | ![]() | Prof. Dr. Markus Strohmaier Prof. Dr. Markus Strohmaier, Stefano Balietti |
Frequency of offering | Fall semester |
Duration of module | 1 semester |
Range of application | M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., MMDS |
Preliminary course work | – |
Program-specific Competency Goals | CG 5 |
Literature | Depending on the assigned topics. |
Course outline | The seminar will kick off with an assignment of subtopics in data-science, afterwards students will work through papers and related literature. A written report is produced and the work is presented and discussed in joint meetings. |