COURSE CURRICULUM

The MSc Social Research Methods and Statistics with Computational Social Science program offers a cutting-edge curriculum that combines traditional social research techniques with advanced computational and statistical methods. Core modules such as Statistical Foundations, Introduction to Statistical Modelling, and Data Science Modelling provide a strong grounding in quantitative analysis, equipping students with the skills to interpret and model complex social data. Methodology and Research Design and Introduction to Qualitative Research Methods ensure a comprehensive understanding of both quantitative and qualitative research approaches. Modules like Statistical Models for Social Networks highlight the integration of computational tools with social analysis, allowing students to explore relationships and patterns within large datasets. The Dissertation module offers the opportunity to conduct independent research using real-world data and computational techniques. Optional modules such as Topological Data Analysis, Doing Research with Social Network Data and Visualizations, and Theory and Method in Demography enable further specialization in emerging areas of data-driven social science. Together, these modules develop students’ technical, analytical, and methodological expertise, preparing them for careers or doctoral research in academia, government, policy, and technology sectors, where the ability to apply computational and statistical tools to understand complex social systems is increasingly in demand.