How do ideas spread? How do cities become segregated? The programme prepares you to harness complex data and advanced computational tools to address these and other important social questions.
Using statistical and computational methods to understand society and human behaviour
The increasing integration of technology into our lives has created unprecedented volumes of data on everyday social behaviour. 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 machine learning, agent-based modelling, natural language processing, and network science have made it possible to analyse these data in ways previously unimaginable.
This is a chance to develop skills in computational techniques alongside a strong grounding in the principles and practice of contemporary social research. The programme’s quantitative methods training will help you harness complex data and use them to explore social theories and fundamental questions about societies. The programme’s theoretical and substantive training will introduce you to the principles of social inquiry and theories of human behaviour, 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.
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 behaviours to outcomes in social systems. You will also learn to apply advanced computational methods–including discrete choice modelling, social network analysis, agent-based simulation, and machine learning—to draw inferences about micro-level behaviours 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.
Syllabus and course details
The programme runs over two years and encompasses 120 credits, including a thesis.
Logic of Social Inquiry – Formulate social scientific research questions and design computational research to answer these questions.
Behavioural Mechanisms in the Social Sciences – Review and critique the implicit and explicit assumptions about human cognitive and decision-making processes that underpin social scientific theories.
Statistics and Data Science I – Explore key concepts, theorems, and distributions in probability theory and statistics, with an emphasis on stochastic computer simulations.
Statistics and Data Science II - Estimate and interpret multivariate linear regression models, including extensions appropriate for causal inference.
Discrete Choice Modelling - Apply statistical models for categorical outcomes that are integral to social network analysis, machine learning, and the analysis of human decision-making.
Agent-Based Modelling – Develop and program Agent-Based Models (ABMs). Set-up and run experiments using ABMs for systematic theoretical inquiry.
Social Network Analysis – Explore social network concepts, data structures, and measures, and apply statistical models to social network data.
Digital Strategies for Social Science Research – Extract relevant information from online data sources, deal with the mass of extracted data, and apply appropriate tools for making sense of the data.
Inequality and Segregation: Theory and Measurement – Learn about commonly used measures of inequality and segregation employed in social science research, and calculate the measures using real data and a computational approach.
Organizations: Theory and Research – Review major theories, empirical research, and related literatures in the study of organizations including organizational demography, organizational decision-making, and internal dynamics.
Culture: Theory and Research – Review major theories, empirical research, and related literature in the study of cultural production and cultural consumption, with an emphasis on contemporary research using computational designs.
Big Data: Social Processes and Ethics - Examine the social processes involved in the creation, storage and use of large scale digital data sets and related ethical issues.
Studies Abroad – Optionally study abroad during the third semester at a partner institution where you will acquire training in topics and methods related to your social scientific interests and within the programme’s scope.
Master’s Thesis - Consult with a faculty advisor to devise research questions related to a topic of interest in the computational social sciences. Then perform original social research intended to answer these questions.
The dual skills you develop in social theory and data analysis are in high demand in public and private sectors. Graduates will be qualified for a number of roles: data analyst, marketing analyst, sales researcher, user experience researcher, policy analyst, etc. After graduation, you will also qualify for many PhD programmes.
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Last updated December 7, 2017