How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to learn the dependencies between interrelated entities? How can we stop disease or information spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and biological questions.

Participants will be able to understand when a network approach is useful, understand different types of networks, understand the differences and similarities between a Complex Networks and a Social Network Analysis approach, describe network characteristics, infer edges or node attributes, and explore dynamical processes in networks.

The course has a hands-on focus, with lectures accompanied by programming practicals (in Python and R) to apply the knowledge on real networks, drawn from examples in sociology, economics and biology.

Participants should be proficient in spoken and written English. Participants should feel comfortable programming in either Python or R (we will be using both in the course), and have a basic understanding of algebra, probability and statistics. If participants only know either Python or R, following a short introduction course for the other language is strongly recommended.
A strong foundation for this course can be obtained through our winter courses Introduction to R and Introduction to Python or our summer course  Data Science: Statistical Programming with R (Course code S24).

Data Science specialisation

This course can be taken separately, but is also part of a series of eight courses in the Summer School Data Science specialisation taught by the department of Methodology & Statistics at Utrecht University:

1. Data Science: Programming with Python (Course code S17, 7-11 July 2025)
2. Data Science: Network Science (this course)
3. Data Science: Statistical Programming with R (Course code S24, 14-18 July 2025)
4. Data Science: Applied Text Mining (Course code S42, 14-18 July 2025)
5. Data Science: Machine Learning with Python (Course code S70, 21-25 July 2025)
6. Data Science: Advanced Techniques for Handling Missing Data (Course code S28, 2026)
7. Data Science: Data Analysis (Course code S31, 2026)
8. Data Science: Text Mining with R (Course code S41,2026)