COURSE CURRICULUM

The MSc in Data Science for Public Policy and Politics is a one-year full-time course that merges computing with the complexities of public policy and politics. The program is structured over two semesters, followed by an independent dissertation. In the first semester, students are introduced to Python and R programming, equipping them with essential data analysis and statistical modeling skills. Alongside technical training, professional development is emphasized through a module focused on communication, project planning, and understanding the ethical and societal impacts of data work. In the second semester, students build on this foundation by addressing contemporary political and policy challenges and learning how computational tools can be used to analyse and influence decision-making. Core modules delve into knowledge discovery, data mining, and the practical application of algorithms such as clustering, classification, and text mining. Students also explore how machine learning and big data impact governance, from political behavior to policy formulation. A standout component is the dissertation, which allows students to pursue independent research under expert guidance, applying their technical and analytical skills to a topic of their choice. By the end of the course, graduates are well-versed in data science tools such as Python, R, and Power BI, and have a critical understanding of machine learning applications in the public sector. They are trained to analyse political systems, assess policy outcomes, and effectively communicate findings through advanced data visualisation techniques. The course prepares students to influence and contribute to data-driven policy development and political decision-making, combining the rigour of computational methods with the depth of political insight. Whether entering government, think tanks, NGOs, or further research, graduates leave with a strong interdisciplinary toolkit suited to shaping public outcomes in a digital world.