Network Science for Business, Economics, Informatics and Social Sciences

(fall semester lecture)

"Complexity is a sign of intelligence. Simplicity is a sign of wisdom"

Med Jones


This lecture has been repositioned and given a new title. The content of the lecture is comparable to the lectures formerly known as „Network Analytics for Marketing and Business", „Network Analytics for Marketing, Management and Economic“, „Network Analytics I„ or „Network Analytics„. Students who have already participated and successfully finished one of the four lectures mentioned above cannot additionally receive credits for this course."Network Analytics" cannot additionally receive credits for this course.



Online social media, organization networks, decentralized computing systems are just a few examples of how interlinked our world has become. The goal of the course is to provide MA students with an introduction to network theory and social network analysis. This course is grounded in theory and applied towards practice. Students will get an overview of network science, starting from basic network models to advanced topics such as temporal network and percolation. Besides, it also introduces students to the tools that are typically used in network science. Students should be able to develop abilities to identify and apply suitable methods to deal with network analysis problems.

Prof. Dr Claudio J. Tessone, Dr. Flavio Iannelli


Target Audience:
Master students assigned to “Wahlpflichtbereich" BWL 4

Each fall semester


Advanced network science including both network theory and social network analysis.


Fundamental courses in statistics (e.g. Emprical Method MOEC0021). Solid R and/or Python programming skills (or the willingness to develop the knowledge PRIOR to the lecture) are a necessary requirement.

Active participation, assignments given in class, multiple choice tests, peer evaluation.

Dates and Location:
Monday 14:00 -18:00
30th September - 16th December 2020
Location: tba

Further information:

Don’t forget to officially register yourself using the registration tools at the University of Zurich.

The syllabus is available for download here. (, 100 KB)

OLAT element: