Course

Unit 1

Emergence of social data science: Key characteristics, concepts and perspectives; social systems and digital infrastructure; introduction to sociotechnical systems

Unit 2

Social Construction of Data: Data and Society interactions; Datafication and Sociological perspectives; System and Systems thinking approach; Social Network theory and role of data

Unit 3

Data, Power, and Inequality: Digital labour, automation and platform governance; Bias in datasets and algorithmic discrimination; Data justice and feminist critiques of data science

Unit 4

Mixed Methods in Social Data Science: Qualitative and Quantitative approaches; Participatory and inclusive data practices; sectoral and thematic Case studies related to governance, development and inclusion

Unit 5

Responsible and Ethical Data Science: Ethical frameworks: fairness, accountability, transparency; Regulatory landscapes: privacy, consent, data sovereignty; Data ethics labs and ethical impact assessments

Text Books / References

Core Textbooks and Papers Schfer, M. T., & van Es, K. (Eds.). (2017). The datafied society: Studying culture through data. Amsterdam University Press. Kitchin, R. (2023). Data and society: A critical introduction. Sage Publications. Stone, D. (2020). Counting: How we use numbers to decide what matters. Liveright Publishing. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green Publishing. Reference Books (Additional): Haraway, D. J. (1991). A cyborg manifesto: Science, technology, and socialist-feminism in the late twentieth century. In D. J. Haraway, Simians, cyborgs, and women: The reinvention of nature (pp. 149181). Routledge. Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press. Amoore, L. (2020). Cloud Ethics: Algorithms and the Attributes of Ourselves and Others. Duke University Press.

This course introduces students to the emerging field of sociology of data science, exploring how digital and computational methods transform social inquiry. It examines the sociotechnical construction of data and the role of datafication in shaping knowledge and wisdom, as well as the implications of data-driven practices for power, inequality, and social change. The course combines classical sociological theories with contemporary concerns in the digital divide, data governance, and ethics. Through case studies and hands-on exercises, students will develop a critical and applied understanding of how data is embedded in social processes and institutions, and how it can be collected, collated and interpreted.

Course Objectives:

1. To examine the theoretical underpinnings of data science as a sociotechnical system shaped by human, institutional, and technological factors
2. To gain a basic understanding of socially relevant data, including its meanings and applications
3. To apply mixed-methods approaches, integrating sociological insight into data-driven research and design
4. To develop ethical and contextual considerations when working with data in social contexts, through case studies on governance and development

Course Outcomes:

CO1: Students can demonstrate knowledge of foundational sociological concepts and their relevance to data-driven societies

CO2: Students can analyse data through critical lenses, to assess its ethical and political implications

CO3: Students can understand contemporary issues in social science, addressing theoretical and practical challenges. CO4: Students can apply sociological perspectives to assess the ethical and political implications of algorithmic systems CO5: Students can evaluate real-world applications of data, using sociological theories and frameworks

Skills:

• Students will develop sociologically informed critical thinking about digital technologies and their impacts, embedded in algorithms, datasets, and digital infrastructure
• Students will translate sociological insights into responsible data practices and design recommendations working with data in diverse contexts

Program outcome PO – Course Outcomes CO Mapping

Program Specific Outcomes PSO – Course Objectives – Mapping

*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar