Universität Wien

220078 SE SE Advanced Data Analysis 3 (2020W)

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

The course will be completely online. Moodle will be the homebase where you will find all necessary information and materials. Tutorials will take place in web conferences via BigBlueButton.

  • Thursday 15.10. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 29.10. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 12.11. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 26.11. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 10.12. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 07.01. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG
  • Thursday 21.01. 09:45 - 12:45 Seminarraum 9, Währinger Straße 29 2.OG

Information

Aims, contents and method of the course

Quantitative 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 hyperlinks 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 the first session.

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 the first session and on Moodle.

Examination topics

Required knowledge, readings and practical skills will be conveyed via Moodle and in the online tutorials.

Reading list

Will be provided in class.

Association in the course directory

Last modified: Th 24.09.2020 09:09