Introduction to 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Please note that this page is no longer updated.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 Introduction to Data Science - reference subject | |
|---|---|
| module number | 201292 |
| examination number | 6700353 |
| department | Central Institute for Continuing Education |
| ID of group | 672 (General Studies) |
| module leader | Dr. Nicola Henze |
| term | winter term only |
| language | Deutsch |
| credit points | 1 |
| on-campus program (h) | 11 |
| self-study (h) | 19 |
| obligation | elective module |
| exam | alternative pass-fail certificate |
| details of the certificate | Aktive Teilnahme an mind. 80% der Vorlesungen und am Abschlusstutorium |
| link to Moodle course | |
| teacher | Prof. Dr. Thomas Hotz, Prof. Dr. Patrick Mäder, Prof. Dr. Jens Wolling, Prof. Dr. Emese Domahidi, Prof. Dr. Kai-Uwe Sattler, Prof. Dr. Jana de Wiljes |
| signup details for alternative examinations | This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer. |
| maximum number of participants | |
| previous knowledge and experience | keine |
| learning outcome | Die Studierenden kennen die grundlegenden Bausteine der Data Science und können den Ablauf eines durchgängigen Zyklus in der Data Science von der Datenerhebung über die Verarbeitung bis hin zur Evaluation beschreiben und einordnen. Zudem sind sie in der Lage, statistische Fallstricke zu erkennen und Analysen aus ethischer Sicht zu bewerten. |
| content | Hauptaufgaben der Data Science: Datenerhebung und -erfassung, Datenverarbeitung, Darstellung von Daten, maschinelles Lernen, mathematische Modelle und Beweise, Interpretation und Evaluation, ethische und rechtliche Aspekte. |
| media of instruction and technical requirements for education and examination in case of online participation | Folien, Tafel, eigenes Endgerät (Laptop, Tablet) |
| literature / references | Literaturhinweise werden in der Lehrveranstaltung gegeben |
| evaluation of teaching | |