194.047 Interdisciplinary Project in Data Science
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020W, PR, 4.0h, 5.0EC
TUWEL

Properties

  • Semester hours: 4.0
  • Credits: 5.0
  • Type: PR Project
  • Format: Hybrid

Learning outcomes

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.

Subject of course

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 and upload the final version on TUWEL

6. Do the project

7. Discuss regularly with the supervisors

8. Write the report  and prepare the poster

9. Once approved by both supervisors upload the final report and the poster 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

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

Teaching methods

Solve a practical problem in inter-disciplinary project work

Write a report

Prepare a poster for presentation

Mode of examination

Immanent

Additional information

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)

Lecturers

Institute

Examination modalities

Report on the results

 

Final Certificate

If the supervisor and co-supervisor do not have the rights to issue a certificate, the supervisor or co-supervisor should send an e-mail to allan.hanbury@tuwien.ac.at (with all involved people in cc) containing the final grade for the project. The certificate will then be issued in the names of the supervisor and co-supervisor (as possible given their affiliation to the TU Wien).

Course registration

Begin End Deregistration end
23.09.2020 12:00 31.01.2021 23:59 28.11.2020 23:59

Curricula

Study CodeSemesterPrecon.Info
066 645 Data Science 3. Semester

Literature

No lecture notes are available.

Language

English