Interdisciplinary Project in Data Science

Submitted by webmaster on Thu, 10/08/2020 - 11:17
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Allan Hanbury

Interdisciplinary Project in Data Science


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 (see list below)

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    
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
The following people are potential co-supervisors:
A Min Tjoa
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
Margit Pohl
Markus Zlabinger
Marta Sabou
Matthias Lanzinger
Nysret Musliu
Peter Filzmoser
Peter Knees
Rudolf Mayer
Sascha Hunold
Sebastian Hofstätter
Silvia Miksch
Theresia Gschwandtner
Tomasz Miksa
Victor Schetinger
Wolfgang Aigner


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)

<p>Report on the results, presentation</p>