Universität Wien

220078 SE SE Advanced Data Analysis 3 (2020W)

5.00 ECTS (2.00 SWS), SPL 22 - Publizistik- und Kommunikationswissenschaft
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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.

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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

Literatur

Will be provided in class.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 24.09.2020 09:09