COURSE CURRICULUM

The MSc Social Network Analysis program offers a comprehensive and interdisciplinary curriculum that equips students with the theoretical, methodological, and computational tools to study and interpret complex social structures. Core modules such as Social Network Analysis: Concepts and Measures and Theories of Social Relations, Networks, and Social Structure provide a strong foundation in understanding how social connections shape behavior, influence, and societal patterns. Practical modules like Data Analysis with R & RStudio, Doing Research with Social Network Data and Visualizations, and Statistical Models for Social Networks develop advanced analytical and technical skills essential for handling large datasets and applying statistical models to network research. The Dissertation allows students to conduct independent, original research, integrating theory and data analysis to address real-world problems. Optional modules enable further specialization, including Crime and Networks, Topological Data Analysis, Privacy, Confidentiality and Disclosure Control, and Qualitative Research Methods, offering flexibility to explore applications across criminology, data science, and social policy. Together, these modules prepare graduates to tackle complex data-driven challenges in academic, governmental, and industry settings. The program’s emphasis on both theory and applied analysis ensures students gain a deep understanding of social networks and the skills to use them for meaningful insight and decision-making.