CEU application details
Program Description
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.
Learning Outcomes
The SDS MSc program will provide two tracks for participating students: i) academic and ii) applied social data science training with different emphases on academic and practical skills. The program will provide the following knowledges, skills, and competences.
The students will acquire knowledge
- of an arsenal of tools of quantitative and data-driven approaches to study social phenomena;
- of the fairness and biases of social data science methods;
- of the legal and ethical framework of data collection and analysis in social sciences, including specificities of Big Data;
- about main concepts, ideas and challenges in at least one field of social sciences, as well as the important special quantitative and qualitative methods;
- of the new possibilities that socially related Big Data types enable for studying contemporary problems in business and academic research;
- to identify the societal potential of and challenges to working with Big Data.
The students will be equipped with skills in how to:
- understand and model complex, networked, dynamic, social, economic, political, technological, and ecological systems;
- apply a critical and reflexive view to the advantages and dangers of data driven methodologies in real world applications observing and predicting human behavior.
- master the state-of-the-art programming language for collection, curation, processing, preparation, and analysis of data;
- employ state of the art data science tools, including methods from supervised and unsupervised machine learning, web mining, network analysis, visualization, spatial analysis, natural language processing etc. to the analysis of societal and organizational problems;
- collect data in various ways using tracking, monitoring, crawling or transactional data collection methods or social experiments;
- analyze data of various kind recording temporal, spatial, relational, feature etc. information
- design online or digital social experiments, execute, measure, and interpret their results
- identify correlation patterns and causal relationships in social data and to build predictive models using human behavioral datasets;
- combine quantitative and qualitative empirical methods from social sciences, including statistical analysis, digital methods, and experimental methods with Data Science tools in order to analyze societal and organizational problems;
- communicate with researchers both in social sciences and data science;
- communicate research-based knowledge in writing, visualization, and verbal presentation.
By the end of the program students will be competent in
- planning and completing social data science studies/examination/research of social phenomena in various fields of social sciences;
- managing the ethical aspects of collecting and processing personal data as well as making decisions based on the data;
- participating in and coordinating cooperation in interdisciplinary teams with people from other scientific fields and traditions to work on research problems of social data science;
- independently taking responsibility for further personal scientific development and specialization in the academic and private sectors or in governance and NGOs.
While all these skills are important for a successful career in academia or the data industry, learning outcomes will differentiate between the two tracks. More emphasis will be put on the fundamental questions and social science applications for students on the academic track, while the training of students on the applied social data science track will focus more on the methodological tools and real-world applications.