The MSc program in Social Data Science is a two-year full time multi-disciplinary research-oriented program. The program offers advanced training in three ways: via a methodological training in Data Science, students will learn the core and advanced mathematical, statistical and computational tools to collect, curate, manage, and analyze massive datasets of human actions and interactions. At the same time, working across disciplines they will obtain an overall view on the application landscape of these methods in various social science disciplines and in-depth knowledge about disciplinary questions closest to their personal interest. Students will be confronted with state-of-the-art opportunities and challenges of Big Data technologies, which will help them to develop a reflexive and critical thinking about such technologies and their role in shaping human behavior and social phenomena. In the end of the training, they will be able to design data-driven projects and digital social experiments to measure, interpret, model and understand social phenomena, such as inequalities and segregation, migration, corruption, gender issues, populism, fake news, environmental problems, and the social consequences of artificial intelligence. Graduates will be well equipped to participate in interdisciplinary teams working on social problems with computational methods in academia, the public sector, civic organizations, and industry.
Students will be selected in a two-stage process, with a pre-selection by the organizers of the program, followed by a screening by the admissions committee. Applicants must pass a BMat exam, and should submit a statement of purpose. In the first year, the program starts with a diversified bootcamp with the aim of harmonizing mathematics and programming skills in the cohort, and to identify possible weaknesses in social science backgrounds, so that students become aware of what they should focus on over the year.
The courses of the MSc program are organized in three main modules on Fundamental Methods of Data Science, Advanced Methods and Concepts and Specialization. During the program, students will be able to follow the academics or Applied Social Data Science track, training them for different career paths accordingly. While the core mandatory courses highly overlap between these tracks, the modular structure offers flexible choices of elective courses for students who will be able to specialize according to their interest. Every student will have a research internship at the end of the first year and will complete a capstone project to obtain the degree. Goals of these projects are to apply knowledge and research in a new environment, gaining experience and building connections in an academic research group or in a data-oriented company.
Graduates will represent a new generation of scientists, entrepreneurs, and policy makers with knowledge about the fundamental questions and cutting-edge methods in data science with simultaneous sensitivity to socially relevant issues. The program will help them develop independent and critical thinking and actionable skills to address actual social problems like inequalities and segregation, migration, corruption, populism, fake news, environmental problems, and the social consequences of Artificial Intelligence.