Unit I

Unit I – Introduction to Social Data Science: Questions, Concepts, and Methods. Foundations of theory-building. Social systems and social data. Applications of computational methods in social sciences: overview and case studies.

Unit II

Unit II – Critical Perspectives in Data Science. Production, analysis and use of quantified data: an anthropological approach. Thinking ethically and contextually about quantified data. Qualitative data and stakeholder engagement in social data science.

Unit III

Unit III – Data Science for Political Studies. Theories and open challenges in political science. Comparative environmental politics: social sciences and climate change. Case studies used for real-world understanding.

Unit IV

Unit IV – Data Science for Sociology. Theories and open challenges in sociology. Social network theory. Applications of social network analysis to inform policy decisions. Case studies used for real-world understanding.

Unit V

Unit V – Data Science for Anthropology. Theories and open challenges in anthropology. Digital ethnography. Spacial analysis using Geographic Information Systems (GIS) data. Linguistic anthropology and natural language processing. Case studies used for real-world understanding.

This course explores successful applications of computational approaches to social science based on the representation of complex data, information visualization, and model construction. We will reexamine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. The course also covers crucial issues in social data sciences surrounding privacy, gender bias, and fairness. By the end of the course students will gain a foundational understanding of the principles, methods, and applications of social data science using case studies for real-world applications.

Course Objectives:

1. To examine the theoretical underpinnings of social data analysis and theory-building.
2. To gain a basic understanding of methods and applications of social data science.
3. To develop ethical and contextual considerations when working with quantified data in social contexts.
4. To gain an overview of research areas in social science that can leverage data science techniques.
5. To explore key theories and challenges in political science, sociology and anthropology within the context of data science.

Course Outcomes:

CO1: Students can demonstrate an understanding of how theories inform data analysis and interpretation in social contexts.
CO2: Students can analyze quantified data through critical lenses, considering ethical and contextual implications.
CO3: Students understand the scope of application of data science techniques to contemporary issues in social science, addressing theoretical and practical challenges.
CO4: Students can explain approaches to integrating qualitative data analysis into social data science research, enriching the depth and context of their findings.
CO5: Students can synthesize findings from social data analysis across different disciplines, integrating insights from anthropology, sociology, and political studies.

Skills:

• Students will develop the ability to critically analyze data, theories, and methodologies from multiple disciplinary perspectives, enabling them to evaluate the strengths, limitations, and ethical implications of social data science approaches.
• Students will enhance their ethical decision-making skills by considering the social, cultural, and ethical implications of working with data in diverse contexts.

Program outcome PO – Course Outcomes CO Mapping

Program Specific Outcomes PSO – Course Objectives – Mapping

• Llaudet, E., & Imai, K. (2022). Data analysis for social science: a friendly and practical introduction. Princeton University Press.
• Kim, In Song and Dmitriy Kunisky. “Mapping Political Communities: A Statistical Analysis of Lobbying Networks in Legislative Politics.” Political Analysis (2020)
• Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The quarterly journal of economics, 133(1), 237-293.

• Rule, James. 1997. Theory and Progress in Social Science. New York: Cambridge University Press. Winch, Peter. 1958. The Idea of Social Science and Its Relation to Philosophy.
• London: Routledge. Hesse, Mary. 1966. Models and Analogies in Science. Notre Dame, IN: Notre Dame University Press