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QS906 Big Data research: Hype or Revolution?

20/30 CATS - (10/15 ECTS)
Big data is said to be transforming science and social science. In this module, you will critically engage with this claim and explore the ways in which the rapid rise of big data impacts on research processes and practices in a growing range of disciplinary areas and fields of study.

In particular, the module considers the following questions: What is big data? To what extent is 'big data' different to other kinds of data? What key issues are raised by big data? To what extent is big data transforming research practices? How are the 'nuts and bolts' of research practice (e.g. ethics, sampling, method, analysis, etc.) transformed with big data? To what extent are core concepts relating to research practice - such as comparison, description, explanation and prediction - transformed? To what extent can we critically engage with big data? How is big data transforming the 'discipline'?

You will also examine how we might we use big data research both as a way to resist and/or shape global transformations, how big data might impact on the future of social science, and what challenges lie ahead for social science research given the impact of big data.

Module Convenor

Dr Emma Uprichard

Indicative Syllabus

Week 1: Big Data - The New Digital Enlightenment?

Key questions: What is social science? What is science? What are big data? How do big data present a paradigm shift in social science? How do big data transform and/or challenge social science research and practice? How might the politics of big data shape research?

Week 2: Big Data – Transforming Research Questions and Research Design?

Key questions: How do big data problematise research design and the research process? How are research questions formulated given big data problems? What are key research questions in the age of big data? How might global research areas be addressed using big data? Who are the 'producers' and 'owners' of big data? How might big data be used to research particular 'thematic areas' of study?

Week 3: Big Data – Transforming Data Access, Collection and Sampling?

Key questions: How do big data transform processes of data access, collection and sampling? To what extent do big data transform probability and non-probability sampling designs? In what ways might big data provide new kinds of sampling problems?

Week 4: Big Data – Transforming Ethics, Privacy and Security?

Key questions: How are big data infrastructures transforming key ethical issues, such as confidentiality, anonymity, and privacy? What constitutes 'deceit' or 'harm' in the new big data age? How is the issue of 'security' transformed in big data research? Who owns 'live' globally distributed data?

Week 5: Big Data - Transforming Methodological Practices?

Key questions: How are key concepts in the philosophy of social science and knowledge transformed with big data? How are methodological practices turned on their head? To what extent are quantitative and/or qualitative approaches altered? How might we need to rethink methodological approaches and assumptions underpinning the research process and the modes of analysis? To what extent are we moving into a new paradigm shift relating to research methods, methodologies and related epistemologies? How are digital modes of 'knowing' transformed?

Week 6: Big Data – Real-time Research and Big Temporal Transformations?

Key questions: To what extent do big data impact on the speed and process of research? How do big data transform the expectations and pace of the research process? How are the stages of the research process altered with big data? To what extent might real-time research benefit or hinder our capacity to produce change and continuity in the real world?

Week 7: Big Data – Data Spaces, Boundaries and Geographies

Key questions: How do big data transform the notion of the 'research site' or 'in the field'? How do big data carve new kinds of geographies and boundaries of research? How are the politics of big data transforming 'data territories'? To what extent do new kinds of data infrastructures, such as networks and data flows, transform how we understand space and place during the research process? How is the 'spatiality' of data transformed with big data?

Week 8: Big Data, Complexity, Policy and Governance

Key questions: To what extent can big data be used for policy planning and governance? How might we use big data to model complexity? How might we use big data to model complex systems for the purposes of policy practice and planning? What issues are raised? How do big data transform policy research and planning? How might we engage with big data to shape political and social change? To what extent can we resist or shape change through an engagement with big data? How do big data impact on our individual and collective agency? How can we use big data to shape social and political structures that are driven and produced in data-driven systems?

Week 9: Big Data Futures – Transforming Disciplines and Interdisciplinary Challenges?

Key questions: How are big data changing notions of the 'discipline'? How are the social sciences 'opened up' or 'closed down' with big data? To what extent are big data research approaches dependent on interdisciplinary teams? How might big data impact and transform the future of social science? What challenges lie ahead for social science research given the impact of big data?

Illustrative Bibliography

Bollier, D (2010) 'The Promise and Peril of Big Data'. The Aspen Institute.

Boyd, D and Crawford, K (2012) 'Critical questions for big data: Provocations '. Information, Communication and Society. 15(5): 662-679.

Crawford, K (2013) The hidden biases of big data. Harvard Business Review Blog.

Kitchin, D (ed) (2014 – in press) The Data Revolution, London: Sage.

Pentland, A (2012) 'Reinventing society in the wake of big data'. Edge.

Uprichard, E (2012) 'Being stuck in (live) time: the sticky sociological imagination'. The Sociological Review, 60: 124–138.

Uprichard, E (2013) ‘Big Data: Little Questions’, Discover Society. Issue 1; 'Focus' section.

Key journals

Learning Outcomes

By the end of the module, students should be able to:

  • Appreciate the rising issues and challenges at the forefront of big data research;

  • Critically engage with the ways in which big data problematise core methodological issues in research;

  • Apply general issues involved in doing research with big data to more specific thematic areas of study (e.g. cities, sport, health, etc.);

  • Understand key methodological and epistemological challenges involved in conducting social research with big data.

Important Information:

Please be advised that you may be expected to have access to a laptop for some of these courses due to software requirements; the Centre is unable to provide a laptop for external students.

Gaining permission of a member of CIM teaching staff to take a module does not guarantee a place on that module. Nor does gaining permission of a member of staff from your home department or filling in the eVision Module Registration (eMR) system with the desired module. You must contact the Centre Administrator ( to request a module place.

Please be advised that some modules may have restricted numbers. Places are not allocated on a first-come first-served basis, but instead all external students requesting a CIM module as optional, who submit their request by the relevant deadline are given equal consideration.

We are normally unable to allow students (registered or auditing) to join the module after the third week of it commencing. If you have any queries please contact the Centre Administrator.