COURSE CURRICULUM

The one-year MSc in Applied Social Data Science offers a comprehensive curriculum designed to enhance students’ expertise in quantitative research methods through a carefully structured set of modules. The program is divided into three main components: two taught semesters followed by a dissertation project. In the first semester, students build a strong foundation by taking modules in computer programming, applied statistical analysis, and research design. These courses are essential for developing the technical and methodological skills required for advanced data analysis in the social sciences. The second semester allows students to deepen their understanding and broaden their toolkit by focusing on more specialized and advanced topics. Modules in this term include machine learning, forecasting methods, and quantitative text analysis, each of which offers hands-on experience with techniques widely used in contemporary data science applications. These modules not only develop technical proficiency but also emphasize how these methods can be applied to address real-world social science questions. Following the completion of coursework, students undertake a dissertation project over the summer months. This component gives them the opportunity to conduct independent research on a topic of their choice, allowing them to apply the skills and knowledge gained throughout the program. The dissertation is a substantial piece of original work that involves formulating a research question, analyzing relevant data, and presenting findings in a clear and rigorous way. It is an important capstone to the program, reflecting both the academic and practical competencies students have developed. Overall, the structure of the course ensures that graduates are well-prepared to pursue careers in data-intensive roles across academia, the private sector, government, and non-profit organizations, with a solid understanding of both the theoretical and applied aspects of social data science.