220043 SE SE Advanced Data Analysis 1 (2023S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 20.02.2023 09:00 to We 22.02.2023 18:00
- Deregistration possible until Fr 31.03.2023 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 22.03. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 19.04. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 03.05. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 17.05. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 31.05. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 14.06. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
- Wednesday 28.06. 09:45 - 13:00 Seminarraum 6, Kolingasse 14-16, EG00
Information
Aims, contents and method of the course
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
b. a.
Association in the course directory
Last modified: Tu 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.