The following courses on topics related to our research are given (among others) by members of the Infolab. Normally, students willing to work on a master thesis with us are required to have read at least an introductory course in Data Mining, and PhD students must take the Network Science and the Readings in Network Science courses.

Regularly given courses

Introduction to computational social science

Level: Bachelor. Frequency: Once a year.
Description: The course introduces computational approaches to model human behaviour and social phenomena. Core concepts in computational social science are covered, such as observational studies (what types of data exist, possible biases and how to use data for modelling), basic concepts and techniques for running experiments (asking vs. observing, natural experiments, simulations, validity and generalisation) and discuss key issues such as ethical considerations. The course has both a theoretical and a practical perspective, where students learn basic principles and also how to apply them in practice in three main areas: social network analysis, text analysis, and agent-based modelling and simulation.

Data Mining I

Level: Master. Frequency: Twice a year.
Description: An introduction to data mining, its terminology and different approaches to extract knowledge from data, including classification, clustering, and association analysis. We cover different types of data, including tables, transactions, graphs, and text.

Data Mining

Level: Master. Frequency: Once a year.
Description: This is an extended version of Data Mining I, not covering classification methods (that are expected as a prerequisite) and going more in depth in the areas of association analysis and cluster validation. A project consists in studying and replicating state-of-the-art methods from the literature.

Network Science

Level: PhD. Frequency: Every second year.
Description: An introduction to the field of Network Science targeted to PhD students in all disciplinary domains. After introducing the basic terminology and concepts, (guest) lectures on specific topics and a review of selected literature allow the students to go more in depth in their own domain.

Readings in Network Science

Level: PhD. Frequency: At any time, depending on interest.
Description: This course consists in reading literature in network science and discussing it at monthly meetings.

Reviewing scientific papers

Level: PhD. Frequency: Every second year.
Description: This course is about the theory and practice of reviewing scientific papers.


We regularly give tutorials at international venues (past venues include the ESSLLI school and the SunBelt, IC2S2, Asonam, ICWSM, SocInfo and ARS conferences). The material for the tutorials on multilayer network analysis is available on the GitHub pages of our R library and Python library (forthcoming).