220043 SE SE Advanced Data Analysis 1 (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 20.02.2023 09:00 bis Mi 22.02.2023 18:00
- Abmeldung bis Fr 31.03.2023 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
22.03.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
19.04.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
03.05.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
17.05.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
31.05.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
14.06.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Mittwoch
28.06.
09:45 - 13:00
Seminarraum 6, Kolingasse 14-16, EG00
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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 class.
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 class and on Moodle.
Prüfungsstoff
All lectures and tutorials taught in class as well as related readings and materials on Moodle.
Literatur
b. a.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 28.02.2023 11:46
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 detectionThe 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.