Introductory description

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'?

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

MODULE WEEKLY CONTENT
Core and recommended reading will be set each week around the focused weekly topics. Students are expected to come to class having already read the core research and engaged with the recommended reading. Students will also be encouraged to contribute and discuss other reading that they have come across during their independent learning.

1. Big Data - The New Digital Enlightenment? Key words: Enlightenment; science; social science; paradigm; big data.
   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?
2. Big Data – Transforming Research Questions and Research Design?
   Key words: Research design; data; data structures; public data; open data; research questions; impact and governance; operationalisation.
   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? How might big data be used to research particular 'thematic areas' of study?
3. Big Data – Transforming Data Access, Collection and Sampling?
   Key words: Sampling; access; data collection; scraping; crawling; data streaming; probability and non-probability designs; generalisation.
   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?
4. Big Data – Transforming Ethics, Privacy and Security?
   Key words: ethics, privacy, security, confidentiality, anonymity, consent, data propriety, data provenance; data governance.
   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?
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5. Big data - Transforming Methodological Practices?
   Key words: Quantitative, qualitative, simulation, exploration, description.
   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? To what extent are we moving into a new paradigm shift relating to research methods, methodologies and related epistemologies?
6. Reading Week
7. Big data – Real-time Research and Big Temporal Transformations? Key words: Time, temporality, real time, speed, research stages, research process, live methods, duration, change and continuity.
   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?
8. Big Data – Data Spaces, Boundaries and Geographies Key words: storage, boundaries, borders, data spaces, geographies, flow, distribution, mobile.
   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?
9. Big Data, Complexity, Policy and Governance
   Key words: agency, structure, governance, politics of method and data, participatory research,
   policy planning, research for change.
   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?
    NB. Formative assignment due here. Work handed in after this date will not be marked.
10. Big Data Futures – Transforming Disciplines and Interdisciplinary Challenges? Key words: discipline, interdisciplinary research, global research agendas, data science, computational social science.
    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?

Learning outcomes

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

• Understand of some of the main conceptual ideas at the forefront of big data research; Develop an appreciation of some of the main ways in which big data problematize core methodological issues in big data research; Have an appreciation of the methodological and epistemological challenges involved in conducting research with big data; Engage critically with big data for research purposes; Evaluate and apply knowledge gained about big data to a specific thematic area of study; Appreciate the extent to which big data may or may not transform research practice in a particular thematic area of study; Advance innovative, original and independent thinking in relation to conducting big data research;

Indicative reading list

Alvarez, M (2013) 'Is big data a big deal in political science?' Open University Press Blog, Available at: http://blog.oup.com/2013/11/is-big-data-a-big-deal-in-political-science/.
Berry, D (2011) 'The computational turn: Thinking about the digital humanities'. Culture Machine 12. Part of Open Humanities Press. Available at:
http://www.culturemachine.net/index.php/cm/article/view/440/470.
Bollier, D (2010) 'The Promise and Peril of Big Data'. The Aspen Institute. Available at: http://www.aspeninstitute.org/sites/default/files/content/docs/pubs/The_Promise_and_Peril_of Big Data.pdf.
boyd, D and Crawford, K (2012) 'Critical questions for big data: Provocations '. Information, Communication and Society. 15(5): 662-679. Available at: http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878.
Crawford, K (2013) 'The hidden biases of big data'. Harvard Business Review Blog.
Cukier, C and Mayer-Schoenberger, V (2013) 'Rise of Big Data: How it's Changing the Way We Think about the World '. Foreign Affairs, 98. Available at:
http://heinonline.org/HOL/LandingPage?handle=hein.journals/fora92&div=46&id=&page.
Crawford, K (2013) The hidden biases of big data. Harvard Business Review Blog. Available at: http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/.
Crawford, C and Schultz, J (2014) 'Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms', Boston College Law Review, 55(1): article 4. Available at: http://lawdigitalcommons.bc.edu/bclr/vol55/iss1/4.

Kitchin, D (ed) (2014 – in press) The Data Revolution, London: Sage.
Lohr, S (2012) 'The Age of Big Data'. New York Times, Sunday Review. Available at: http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?pagewanted=all&_r=0.
Manovich, L (2011) 'Trending: The promises and the challenges of big social data'. Debates in the Digital Humanities, MK Gold (ed). Available at:
http://www.manovich.net/DOCS/Manovich_trending_paper.pdf.
Pentland, A (2012) 'Reinventing society in the wake of big data'. Edge. Available at: http://www.edge.org/conversation/reinventing-society-in-the-wake-of-big-data.
Sacasas, M (2014) 'The Political Perils of 'Big Data''. Blog: The Frailest Thing. Available at: http://thefrailestthing.com/2014/05/19/the-political-perils-of-big-data/ .
Uprichard, E (2012) 'Being stuck in (live) time: the sticky sociological imagination'. The Sociological Review, 60: 124–138. Available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-954X.2012.002120.x/pdf.
Uprichard, E (2013) ‘Big Data: Little Questions’, Discover Society. Issue 1; 'Focus' section. Available at: http://www.discoversociety.org/2013/10/01/focus-big-data-little-questions/.
Uprichard, E (forthcoming) 'Big data doubts and pixelated policy'. The Chronicle of Higher Education.
Key journals
Big data & Society (Sage)
Big data (Marie Ann Liebert)
Big Data Research (Elsevier)
Journal of Big Data (Springer)
International Journal of Big Data Intelligence (InderScience Publishers)
Political Analysis – Special issue: Big Data in Political Science

Research element

tbc

Interdisciplinary

The module requires students to demonstrate an appreciation of multi-disciplinary approaches to big data with a view to understanding how it impacts on traditional disciplinary -aligned social reserach practice.

Subject specific skills

• Demonstrate an appreciation of multi-disciplinary approaches to big data;
• Demonstrate an understanding of some of the ways in which big data may or may not transformtraditional social research practices and processes;
• Understand and appreciate future professional challenges relating to big data research;
• Discover and share new material in a particular area of study;
• Develop imaginative and forward-facing big data project ideas;
• Extend general and current knowledge in big data to specific thematic areas of expertise;
• Problematize and reconceptualise old problems and challenges to new areas of research.

Transferable skills

 Evaluate, interpret and judge core subject related material critically;
 Develop and demonstrate independent thinking;
 Apply concepts gained in one area to another area;
 Take initiative in their own learning;
 Design and develop a structured essay plan;
 Think critically about a particular area of study;
 Systematically evaluate and synthesise research in a particular area of study;
 Demonstrate their learning through a written and structured essay.