Universität Wien
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220078 SE SE Advanced Data Analysis 3 (2024W)

5.00 ECTS (2.00 SWS), SPL 22 - Publizistik- und Kommunikationswissenschaft
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 09.10. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 23.10. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 06.11. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 20.11. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 04.12. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 08.01. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00
  • Wednesday 22.01. 09:45 - 12:45 Seminarraum 6, Kolingasse 14-16, EG00

Information

Aims, contents and method of the course


Network analysis:
In the digital age, networks are ubiquitous, be it social networks of friends or interaction partners on social media, semantic networks of words or concepts, or technical networks such as hyperlink networks connecting information sources on the web. Switching back and forth between lectures and hands-on exercises in R and Gephi, you will learn the basics of quantitative network analysis and apply metrics and visualization techniques on a sample network in the scope of a group project.

Topics:
• What are networks, and why network analysis?
• Basic graph theory
• Network measures and metrics
• Visualization
• Community detection

Assessment and permitted materials

Course grading is based on the presentation and written report of a group project. In this project students apply the learnt techniques of analysis and visualization on a sample network they can choose freely (secondary data analysis). Further details will be provided in class.

Minimum requirements and assessment criteria

Ongoing in-class participation and additional readings are basic requirements.
For successfully passing the course, participants have to achieve at least 50% of the total points. Full details on the grading system will be given in class and on Moodle.

Examination topics

All lectures and tutorials taught in class as well as related readings and materials on Moodle.

Reading list

Borgatti, S. P., Everett, M. G., Johnson, J. C. (2018). Analyzing social networks (2 ed.). Thousand Oaks, CA: Sage.

Luke, D. A. (2015). A user’s guide to network analysis in R. Cham: Springer.

Association in the course directory

Last modified: Th 26.09.2024 12:06