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
• All presenters of the associated Lecture Series (194.046) of the past years (as listed in the TUWEL course of the lecture series) are available to act as main supervisor for the project.

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
• specifically anybody that has lectured any of the Data Science courses (see list below)

4. Discuss the 1- to 2-page outline with the co-supervisor

5. Refine the outline until both supervisors agree and upload it in TUWEL to register the topic

6. Do the project

7. Discuss regularly with the supervisors

8. Write the report  and upload it in TUWEL

9. Create a poster (following the guidelines of the faculty for the Master Thesis posters). The poster doe NOT have to be printed, you only need to upload it to TUWEL.

Note that a project with the company that you are currently working for is generally not a good fit to the requirements for this inter-disciplinary project.

Potential Main Supervisors

Look at the list of lecturers of the "Domain-Specific Lectures in Data Science" over the last years.

Potential Co-Supervisors

All lecturers of Data Science courses, including:

Alessio Arleo

Alexander Schindler

Allan Hanbury

Andreas Rauber

Cem Okulmus

Christian Bors

Davide Ceneda

Dimitrios Sacharidis

Elmar Kiesling

Fajar Ekaputra

Florina Piroi

Ivona Brandic

Jesper Larsson Träff

Klaus Nordhausen

Kresimir Matkovic

Manuela Waldner

Marta Sabou

Matthias Lanzinger

Nysret Musliu

Peter Filzmoser

Peter Knees

Rudolf Mayer

Sascha Hunold

Silvia Miksch

Theresia Gschwandtner

Tomasz Miksa

Victor Schetinger

Wolfgang Aigner

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