Technische Universität Ilmenau

Master Seminar Data Science - Interactive curriculae of TU Ilmenau

The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.

Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).

You can find all details on planned lectures and classes in the course catalogue.

Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.

module properties Master Seminar Data Science in degree program Master Data Science 2026
module number201354
examination number2400950
departmentDepartment of Mathematics and Natural Sciences
ID of group 24 (Fakultät für Mathematik und Naturwissenschaften)
module leader Manja Krümmer
term winter and summer term
languageEnglisch
credit points5
on-campus program (h)22
self-study (h)128
obligationobligatory module
examalternative examination performance
details of the certificate

Presentation followed by a discussion

The lecturer and/or the examination office will inform about details and specific times.

link to Moodle course
teacher

Lecturers from the computer science disciplines of Faculty Computer Science and Automotion and from the Institute of Mathematics

signup details for alternative examinations
maximum number of participants
previous knowledge and experience
learning outcome

​Students are able to independently work their way into a challenging and complex data science topic. They can familiarize themselves with current scientific findings, analyze, and evaluate them. They are capable of summarizing insights from the literature, contextualizing them, completing missing steps, and preparing them for presentation. They can present the developed content in a lecture and reflect on it in a subject-specific discussion.

content

The seminar teaches working with scientific literature and other relevant content as well as presenting results.
- Development of a data science topic under supervision
- Documentation of the work (literature review, state of knowledge)
- Presentation with subsequent discussion

media of instruction and technical requirements for education and examination in case of online participation

Paper, Presentations

literature / references

Topic-specific literature will be provided by the supervisor, and additional ressources should be independently researched by the student.
It is also recommended to consult literature on academic writing, literature research (such as resources provided by the library), and presentation techniques.

evaluation of teaching