20 CATS (core for MSc Big Data & Digital Futures)

30 CATs (available as an optional module)

Module Outline

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? How are research practices transformed with big data? To what extent are core concepts relating to research practice - such as comparison, description, explanation and prediction - transformed? Ho can we critically engage with big data, and consider data justice and decolonial perspectives?

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 Michael Castelle

Assessment

• Research Essay (4,000 - 4,500 words; Weighting 100%) - 20 CATS
• Research Essay (5,000 - 5,500 words; Weighting 100%) - 30 CATS

Indicative Topics

• Histories of Big Data
• Big Data Epistemologies
• Researching with Big Data
• Access and Sampling
• Ethics & Privacy
• Data Justice & Decolonising Big Data
• Big Data Futures

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.);
• Identify how big data impacts on marginalized populations
• Understand key methodological and epistemological challenges involved in conducting social research with big data.

Indicative Reading List

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

Balazka, D & D Rodighiero (2020) Big Data and the Little Big Bang: An Epistemological (R)Evolution. Frontiers in Big Data 3 https://www.frontiersin.org/article/10.3389/fdata.2020.00031

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. Available at: http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/

Couldry, N & U A Mejias (2019) The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism.Stanford: Stanford University Press.

D’Ignazio, C & L. Klein (2020) Data Feminism: From Data Ethics to Data Justice. Cambridge, MA: MIT Press. https://data-feminism.mitpress.mit.edu/

Kitchin, R (2021) The Data Revolution, London: Sage.

Milan, S & E Treré (2019) Big Data from the South(s): Beyond Data Universalism, Television & New Media, 20(4):319–335.

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 (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/