Teaching

In the age of "big data" and "advanced analytics", data processing faces new challenges. Queries become more complex and often involve data mining and machine learning tasks, and the scale of the datasets requires new algorithmic approaches. This course covers the theoretical foundations of modern data processing and analytics.

Many real-world systems may be described as a network of dynamically interacting entities. We interact with each other in a social contact network, over which rumors as well as pathogens can spread; electrical energy is delivered by the power grid; the Internet enables almost instantaneous world-wide interactions; our economies rest upon a complex network of inter-dependencies spanning the globe. This course provides an introduction to such network dynamical systems.

Broad introduction to network science, covering core theory and practical analysis of complex networked systems.

Einführung in die Statistik für Biowissenschaften mit Grundlagen und praktischer Anwendung.

In this software lab, we cover a range of tools to analyse networks and processes on networks such as epidemic spreading. Specifically, we cover network analysis tools including spectral clustering, network embeddings and graph neural networks. You will work in small teams of 2–3 people.

This hands-on course introduces students to network science and its applications in biology, combining theoretical foundations with practical computational modeling, simulation, and data analysis in Python through exercises and a team-based research project.

In the research internship, students work on a scientific topic under supervision. This includes researching the area, planning and executing computational experiments to answer a research question, and interpreting and visualizing the resulting data.

Networks are a widely-used model for a wide range of systems from different disciplines. This seminar mainly covers two perspectives: First, networks can be seen as relational data, with tasks related to characterization of networks and individual nodes. Second, we can look at networks as dynamical systems and model their behavior, for example in epidemic spreading.

This proseminar teaches the basics of academic writing and literature research, using topics selected from a few milestone papers in the area of network science.

We offer a broad range of thesis topics related to Network and Data Science, both at the Bachelor's and Master's level. If you are interested in some of our research or want to suggest a topic, please have a look at our general procedures for hannding out thesis topics.