MSc in Computational Social Science

Use statistical and computational methods to understand society and human behavior. The program prepares you to harness complex data and advanced computational tools to address these and other important social questions.

The increased integration of technology into our lives has created unprecedented volumes of data on everyday human behavior. Troves of detailed social data related to choices, affiliations, preferences, and interests are now digitally archived by internet service providers, media companies, other private-sector firms, and governments. New computational approaches based on predictive modeling, agent-based simulations, text analysis, and network science make it possible to analyze these data in insightful and novel ways.

This is a chance to develop skills in cutting-edge computational techniques alongside a strong grounding in the principles and practice of contemporary social research. The program’s quantitative methods training will help you harness complex data and use them to explore social theories and fundamental questions about human societies. The program’s theoretical and substantive training will introduce you to the principles of social inquiry and theories of human behavior and help you apply your technical skills to pressing social issues such as ethnic segregation in schools, income inequality, entrepreneurship, political change, and cultural diffusion.

Future opportunities

The skills you develop in social theory and data analysis during the program are in high demand in the private sector and in government. Graduates will be qualified to pursue social science research in a number of roles: data analyst, marketing analyst, sales researcher, user experience researcher, policy analyst, etc. After graduation, you will also qualify for many Ph.D. programs.

Syllabus and course details

Course details

The program runs over two years and encompasses 120 credits, including a thesis.

During your first year, you gain perspectives on the philosophy of social science, primers in the science of human decision-making, and frameworks for connecting individual behaviors to outcomes in social systems. You will also learn to apply advanced computational methods–including discrete choice modeling, social network analysis, agent-based simulation, and machine learning—to draw inferences about micro-level behaviors and macro-level outcomes.

With these building blocks in hand, you spend the third semester assembling critical knowledge of key theories and contemporary research in areas relevant to academic social science, government, and industry. During the third semester, you also have the option to study abroad at a partner institution.

In the final semester, you integrate the knowledge, skills, and theoretical approaches garnered in the first three semesters by writing a master’s thesis. As part of your thesis, you conduct your own, original, computational research addressing a social scientific topic of your choosing.

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Syllabus

Introduction

The Master’s Programme in Computational Social Science (CSS) is a second-cycle program that leads to a Degree of Master of Science in Computational Social Science. During the program, students train to apply computational methods to analyze large, complex datasets related to human social behavior, and to arrive at theoretically and empirically grounded explanations of social outcomes such as ethnic segregation in schools, income inequality, firm growth and survival, political change and cultural diffusion. In the process, students are inducted into multidisciplinary domains of research in the social sciences that connect sociology, political science, economics, management science, and related disciplines with technical innovations in mathematics, statistics, and computer science. The program provides:

1. grounding in the philosophy of social science research, with special attention paid to the scientific potential, practical limits, and ethical risks of growing troves of digital data describing human behaviors;
2. theoretically and empirically guided understandings of human decision making and cognitive processes, including cognitive biases, that guide choices and social interactions;
3. a framework for connecting micro-level social actions of individuals, families, firms, and other social actors, and macro-level social outcomes like segregation, inequality, cultural evolution, and industrial change;
4. formal training in key methodological components of computational social science, including traditional statistical methods, network analysis, computer simulation, agent-based modeling, web-scraping, and machine learning;
5. practical skills to implement computationally intensive social science research designs using appropriate methods;
6. a broad background in theories and empirical findings in key substantive areas of academic research in the social sciences, with connections to non-academic applications.

The program qualifies students to engage in research and knowledge production in the academic social sciences, the private sector, and the public sector.

Aim

National Qualifications according to the Swedish Higher Education Act

Knowledge and understanding

For a Master of Science (120 credits) the student shall

• demonstrate knowledge and understanding in Computational Social Science, including both broad knowledge of the field and a considerable degree of specialized knowledge in certain areas of the field as well as insight into current research and development work, and
• demonstrate specialized methodological knowledge in Computational Social Science, with special reference to critical traditions of the social sciences and the humanities.

Competence and skills

For a Master of Science (120 credits) the student shall

• demonstrate the ability to critically and systematically integrate knowledge and analyze, assess and deal with complex phenomena, issues and situations even with limited information
• demonstrate the ability to identify and formulate issues critically, autonomously, and creatively as well as to plan and, using appropriate methods, undertake advanced tasks within predetermined time frames and so contribute to the formation of knowledge as well as the ability to evaluate this work
• demonstrate the ability in speech and writing both nationally and internationally to report clearly and discuss his or her conclusions and the knowledge and arguments on which they are based in dialogue with different audiences, and
• demonstrate the skills required for participation in research and development work or autonomous employment in some other qualified capacity.

Judgment and approach

For a Master of Science (120 credits) the student shall

• demonstrate the ability to make assessments in Computational Social Science informed by relevant disciplinary, social, and ethical issues and also to demonstrate awareness of ethical aspects of research and development work
• demonstrate insight into the possibilities and limitations of research, its role in society, and the responsibility of the individual for how it is used, and
• demonstrate the ability to identify the personal need for further knowledge and take responsibility for his or her ongoing learning.

Teaching and working methods

The program’s teaching methods align with three broad skillsets in which it provides training: theoretical understanding, practical data analytic skills, and critical evaluation. Theoretical foundations are provided in lectures, where students become versed in important social theories, contemporary social science debates, and the theoretical premises of computational and statistical techniques.

Practical experience in data analysis is provided in computer laboratories. During labs, the application of techniques to real data is demonstrated by instructors, and students practice applying and extending these techniques. It is during laboratories that students learn to master commonly used software tools employed by computational social scientists.

Critical evaluation skills are developed in seminar settings. During seminars, students learn how to comprehend contemporary social research, synthesize insights from this research, and evaluate the strengths and weaknesses of particular studies or strands of social research. Students do this primarily through reading research articles and actively debating the merits of this research with fellow students. Special attention is paid to articles and texts that use computational approaches.

In addition to lectures, laboratories, and seminars, students are expected to engage in self-study. This includes reading extant social science research articles and methodological texts and completing take-home assignments designed to give students more practice in applying computational techniques.

Research

The Institute for Analytical Sociology

IAS conducts cutting-edge research on important social, political, and cultural matters. The research is sociological - in its original and broadly conceived meaning.

Cultural Dynamics

Formulating and testing theories of peer influence among interconnected consumers of culture i.e. songs, books, movies, etc. to understand the emergence of hits, the establishment of new artists and genres, and cultural change more generally.

Organizational Dynamics

Investigating how new organizations and industries are created and evolve, how labor market dynamics affect workforce segregation across organizations, and computational approaches to group and organizational decision-making.

Computational Text Analysis

The computational analysis offers new ways to derive meaning from text. We use large corpora of text as social sensors to measure what people feel, think, and talk about, which allows us to track the emergence of shared social understandings.

Segregation Dynamics

Examining how different ethnic, economic, and gender groups become segregated across urban spaces, schools, and firms, and the consequences of this segregation for those affected.

Social Network Analysis

Developing and applying novel methods to examine the formation of social ties and the consequences of these ties for patterns of segregation, inequality, polarization, and social change.

Semester 1 (Autumn 2024)

• 771A11 --- Logic of Social Inquiry --- 7.5
• 771A15 --- Statistics and Data Science I --- 7.5
• 771A13 --- Behavioural Mechanisms in the Social Sciences --- 7.5
• 771A17 --- Statistics and Data Science II --- 7.5

Semester 2 (Spring 2025)

Preliminary courses

• 771A19 --- Discrete Choice Modelling --- 7.5
• 771A21 --- Agent-Based Modelling --- 7.5
• 771A23 --- Social Network Analysis --- 7.5
• 771A25 --- Digital Strategies for Social Science Research --- 7.5

Semester 3 (Autumn 2025)

Preliminary courses

• 771A27 --- Inequality, and Segregation: Theory and Measurement --- 7.5
• 771A33 --- Big Data: Social Processes and Ethical Issues --- 7.5
• 771A37 --- Computational Approaches to the Study of Cultural Markets and Social Dynamics --- 7.5
• 771A39 --- Computational Approaches to the Study of Organizations --- 7.5

Semester 4 (Spring 2026)

Preliminary courses

• 771A35 --- Master’s Thesis in Computational Social Science --- 30

The skills you develop in social theory and data analysis during the program are in high demand in the private sector and in government. Graduates will be qualified to pursue social science research in a number of roles: data analyst, marketing analyst, sales researcher, user experience researcher, policy analyst, etc. After graduation, you will also qualify for many Ph.D. programs.