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.

2023S, VU, 2.0h, 3.0EC

Properties

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

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.

For courses on the list below, the certificate issued for the course itself must be submitted to the Dekanat when completing your studies. It is not necessary to take any further steps to get one of these courses recognised as "Domain-Specific Lectures in Data Science".

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 + 120.033 Seminar für Photogrammetrie und Fernerkundung SE 2.0
  • 120.034 VO 1.0/1.5  Data Retrieval from Earth Observation (no longer offered)
  • 120.035 UE 1.0/1.5  Data Retrieval from Earth Observation (no longer offered)
  • 120.110 VU 2.0/3.0 Data Retrieval in Earth Observation
  • 120.029 Microwave Remote Sensing
  • 389.159 VU 3.0/2.0 Network Security
  • 202.064 Computational Biomaterials and Biomechanics
  • 1564 Humanitarian Logistics (WU Wien)
  • 220.029 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)
  • 100.015 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 
  • 301.905 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
  • 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)
  • 296.717 Wildtierökologische Forschungsmethoden (BOKU)
  • 832.307Biologie heimischer Wildtiere VO (BOKU)
  • 832.332 Conservation Biologie VO (BOKU)
  • 160107 Einführung in die Semantik und Pragmatik VO (Univ. Wien)
  • 136.020 VO Lecture Series: Introduction to Digital Humanities (Uni. Wien)
  • 4694 Foundations of International Business (WU Wien)

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

 

  • <...>

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.  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

Additional information

In order to take a course at another university, you generally need to complete a "Mitbelegung" form and have it signed by the Vice-Dean for Academic Affairs. Below are links to the instructions for various universities:

Lecturers

Institute

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

Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified

Literature

No lecture notes are available.

Language

English