Networks matter. They underpin communication and transportation systems, structure the flow of information on the Web and social media, and shape interactions in social, economic, and biological systems. But what can we learn from data capturing these complex interaction topologies? How do network structures influence dynamical processes such as diffusion or epidemic spreading? What roles do individual nodes play? How can we identify significant structural patterns or influential actors? And how can we analyze time series on systems with evolving network topologies?

This course introduces the foundations of network science and equips students with statistical and computational tools to analyze, model, and interpret network data across disciplines. Participants will learn how to quantify structural features, evaluate the influence of topology on dynamics, assess system robustness, and understand the emergence of collective behavior from local interactions.

The course combines lectures introducing core theoretical concepts with a research-led project, allowing students to apply methods to real-world network problems. Course materials include annotated lecture slides and a suite of interactive Jupyter notebooks. Students are expected to complete a final research paper to successfully pass the course.