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.

2021S, VU, 2.0h, 3.0EC

Properties

  • 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

Domain-specific content

  • min 3 ECTS from 226.052 VO Freshwater quality and ecology and a seminar
    • 226.039 Seminarreihe Wassergütewirtschaft,
    • 226.048 SE 2.0/2.0 Ecology
  • min 3 ECTS from a combination of
    • 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

 

Further lectures will be added in alignment with the Interdisciplinary Lecture Series in Data  Science (194.046)

The selection of the respective lecture has to be coordinated 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

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 CodeSemesterPrecon.Info
066 645 Data Science

Literature

No lecture notes are available.

Language

English