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 Public Blockchains 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
–
Recommended prerequisites
lectures offered by the chair
ECTS credits | 6 |
Graded | yes |
Workload | 180h |
Language | English |
Form of assessment | Written report (max. 12 p. excl. references) with oral presentation |
Restricted admission | yes |
Further information | https://www.bwl.uni-mannheim.de/en/information-systems/chairs/prof-dr-strohmaier/teaching/ |
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 4 |
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. |