Scientific Teamproject KMD
(in German: Wissenschaftliches Teamprojekt KMD - )
Module-ID: FIN-INF-999997 |
| Link: | LSF |
| Responsibility: | Myra Spiliopoulou |
| Lecturer: | Myra Spiliopoulou |
| Classes: | Teamproj_KMD |
| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen |
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Abbreviation Teamproj_KMD |
Credit Points 6 |
Semester every |
Term 2. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
When completing this module successfully, the students can:
* solve a realistic data mining and data engineering task in teamwork
* assess the business implications (costs and benefits) of a solution they propose
* built-up a team and organize themselves in it, distributing subtasks among themselves according to their competences, setting milestones and pursuing a joint schedule
* search for scientific papers of relevance
* search appropriate libraries for data preparation, learning and visualization
* design a learning workflow, apply AI methods and evaluate the induced models
* develop a software solution jointly, using public domain tools
* justify their design decisions and software suite selections
* present their work as a team
* write a joint report where they summarize and justify their approachBitte nachtragen nach neuen Constructive Alignment Vorgaben.
Content:
CONTENT: Assignment in the form of a realistic task that involves the analysis of static or dynamic, structured or unstructured data with mining methods, including supervised, unsupervised and semi-supervised methods, stream miners, forecasters and elaborate AI tools. The assignment involves design, development and evaluation of a software solution in teamwork.
STRUCTURE: The assignment is for a team of students, typically three; for larger teams, the assignment is extended to ensure that the effort of each student is 6 ECTS. The students are called to solve the task as a team. The task involves understanding a realistic problem, designing, developing and evaluating a solution for it, presenting this solution to the class, defending it and documenting it on paper.
Workload:
For each team member:
28 h in class (including meetings) + 124 h self-study
The teamproject is typically for three students. For larger teams, the assignments is extended to ensure that the effort of each team member is as above.
| Type of examination: | Teaching method / lecture hours per week (SWS): |
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Referat |
Wissenschaftliches Teamprojekt (2 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
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| Media: | Literature: |
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Literature depends on topic assignment and is given to each team together with the assignment |
Comments:
Als Implementierung des generischen PFLICHTMODULS "Wissenschaftliches Teamprojekt" entsprechend anrechenbar.
COURTESY TRANSLATION:
This module implements the COMPULSORY generic module 'Wissenschaftliches Teamprojekt'.