Universität Wien

220043 SE SE Advanced Data Analysis 1 (2022S)

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

  • Tuesday 08.03. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 15.03. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 22.03. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 29.03. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 05.04. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 26.04. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 03.05. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 10.05. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 17.05. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 24.05. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 31.05. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 14.06. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 21.06. 09:45 - 11:15 Seminarraum 6, Kolingasse 14-16, EG00
  • Tuesday 28.06. 09:45 - 11:15 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

The course will be taught on-site as much as possible, with an option to zoom in if necessary (hybrid mode). Moodle will be our home base where you will find all necessary information and materials.

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

Core Readings (more to be announced in class):

Barabási, A.-L. (2016). Network Science. Cambridge University Press.

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

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

Menczer, F., Fortunato, S. & Davis, C. A. (2020). A first course in network science. Cambridge University Press.

Association in the course directory

Last modified: Fr 11.03.2022 09:08