After successful completion of the course, students are able to process and analyse data in the selected domain, select appropriate methods based on the requirements, apply methods to real data, and develop solutions for domain-specific tasks.

Project addressing a domain-specific challenge.

Steps for the Interdisciplinary Project in Data Science

1. Select a main supervisor for the project

• Usually not from the Faculty of Informatics (or Mathematics)
• Not necessarily from the TU Wien
• A list of possible names is below, but you are not restricted to this list

2. Discuss the project with the selected supervisor, agree on a 1-page outline and identify the corresponding domain-specific lecture in data science (194.068)

3. Select a co-supervisor for the project

• Usually from the Faculty of Informatics (or Mathematics)
• Must be from the TU Wien
• E.g., anybody that has lectured any of the Data Science courses

4. Discuss the 1-page outline with the co-supervisor

5. Refine the outline until both supervisors agree

6. Do the project

7. Discuss regularly with the supervisors

8. Write the report    

Potential Main Supervisors

If no topic is given in the list below, see the Interdisciplinary Lecture Series talk for details on the topic:

• Dominik Eder (dominik.eder@tuwien.ac.at)
• Wolfgang Wagner (Wolfgang.Wagner@geo.tuwien.ac.at)
• Jörg Krampe (jkrampe@iwag.tuwien.ac.at)
• Tanja Zseby (tanja.zseby@tuwien.ac.at)
• David Garcia (garcia@csh.ac.at) - Computational Social Science
• Andreas Grüneis (andreas.grueneis@tuwien.ac.at)
• Peter Klimek (peter.klimek@meduniwien.ac.at)
• Georg Madsen (georg.madsen@tuwien.ac.at)
• Günther Tschabuschnig (Guenther.Tschabuschnig@zamg.ac.at)
• Markus Valtiner (Markus Valtiner@tuwien.ac.at)
• Tina Wakolbinger (Tina.Wakolbinger@wu.ac.at)

Solve a practical problem in inter-disciplinary project work

Write a report

Give a presentation

Steps in the Module “DSA – Domain-Specific Aspects of Data Science”

1. Attend the Interdisciplinary Lecture Series on Data Science (194.046)
2. Choose an area
3. Get theoretical knowledge through attending a lecture in this area (3,0/2,0 VO/VU/SE Fachspezifische Lehrveranstaltungen)
4. Solve a practical problem in inter-disciplinary project work – Interdisciplinary Project in Data Science (194.060/194.047)

Report on the results, presentation