Introduction to Social Network Science with Python

In the wake of the digital revolution, masses of digital behavioral data are becoming available for social research. This data resembles transactions or events that typically consist of both social relations and their communicative content. As such, it can facilitate understanding not only how networks emerge from actions, but also how actors are formed by networks. In this course, we convey basic network analysis skills and how relational methods and coding in Python can be deployed in practical application scenarios.

Course leader

Dr. Haiko Lietz, Lisette Espín-Noboa, Olga Zagovora (GESIS, Germany)

Target group

The course targets scientists who are seeking an introduction to relational methods for the purpose of analyzing social networks in Python. While the combination of introductory-level coding and network theory may be most attractive to social scientists, scientists from other disciplines may benefit from learning how networks are conceptualized in the social sciences.

Course aim

Participants are encouraged to prepare their own network datasets and research questions to work on in the exercises and projects.
Participants can expect to learn how to load network data, how to visualize networks, and how social network theoretic concepts can be operationalized to analyze social networks using Python's NetworkX package. On one day, participants will learn how to test hypotheses using network data.