194.068 Domain-Specific Lectures 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.

2021W, VU, 2.0h, 3.0EC

Course evaluation


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to

  • understand and explain the terminology of the selected discipline,
    understand the multitude of challenges in applying data science methodologies in the selected domain
  • name and explain the tasks, data types and tools of the selected discipline
  • process and analyze data in the selected specialization domain,
    select appropriate methods based on the data requirements,
    apply  these methods to real data, and develop solutions for domain-specific tasks.

Subject of course

The domain-specific lecture is part of the corresponding module in the Data Science curriculum and forms a thematic block together with the lecture series 194.046 Interdisciplinary Lecture Series on Data Science, as well as the project course 194.047 Interdisciplinary Project in Data Science.

The domain-specific LVA, to be selected from the list below, forms the basis for the work in the corresponding interdisciplinary project. Therefore, this course should ideally be completed before, or possibly during, the corresponding project.

The list of approved domain-specific lectures (partially at TU Wien, partially at other universities) comprises of:

  • 226.048 SE 2.0/2.0 Ecology
  • 120.031 VO 1.0/1.5 Introduction to Earth Observation
  • 120.034 VO 1.0/1.5  Data Retrieval from Earth Observation
  • 120.035 UE 1.0/1.5  Data Retrieval from Earth Observation
  • 389.159 VU 3.0/2.0 Network Security
  • 202.064 Computational Biomaterials and Biomechanics
  • 1564 Humanitarian Logistics (WU Wien)
  • 220029 VO 3.0/2.0 Journalismus im Wandel medialer Bedingungen (Uni. Wien, in German)
  • 840.036 Methoden der Medizin (Med. Uni. Wien, in German)
  • 851.099 Epidemiological Methods (Med. Uni. Wien)
  • 100015 VO NdL: Germanistik digital (Uni. Wien)
  • 166.142 Biologie 
  • 185.329 Grundlagen der Klinischen Medizin 
  • 185.334 Klinische Medizin
  • 330.214 Project and Enterprise Financing 
  • 301905 Information-processing in neuronal networks (Uni. Wien)
  • 311.114 Industrial Manufacturing Systems 
  • 330.273 Assistance Systems in Manufacturing 2
  • 330.289 Cobot Studio @Pilot Factory for Industry 4.0
  • 230.016 Road Operations
  • 226.052 VO Freshwater quality and ecology + 226.039 Seminarreihe Wassergütewirtschaft

Further lectures will be added in alignment with the Interdisciplinary Lecture Series in Data  Science (194.046) These will likely include (provisional announcement):

  • 931.300 Agricultural engineering in plant production - seminar (in Eng.)  (BOKU)
  • 931.307 Technologiefolgenabschätzung für die Landwirtschaft VO+UE  (BOKU)
  • 915.326 Life cycle assessment nachwachsender Rohstoffe VO + UE (BOKU)
  • 931.305 Post-harvest technology (in Eng.) VO + EX (BOKU)
  • 931.314 GPS-based agriculture (in German) VO+EX (BOKU)
  • 832.313 Grundlagen der Wildtierökologie VO (BOKU)
  • 832.307Biologie heimischer Wildtiere VO (BOKU)
  • 832.332 Conservation BiologieVO (BOKU)
  • <...>

The selection of the respective lecture must align with the topic chosen for the course 194.047 Interdisziplinary Project in Data Science.

Teaching methods

Contents are presented in lectures and may be elaborated by students in accompanying exercises or supplemented by excursions.  In addition, the students may have to solve homework and larger case studies alone or in groups. If necessary,  appropriate tools are used.

Mode of examination

Written and oral



Examination modalities

The assessment is based on written tests, continuous evaluation as part of exercises, as well as by the evaluation of assignments and/or presentations. Details are available in the respective course descriptions.


Course registration

Not necessary


Study CodeSemesterPrecon.Info
066 645 Data Science


No lecture notes are available.