Big Data Research: Hype or Revolution?
Interdisciplinary Postgraduate Modules
20 CATS (core for MSc Big Data & Digital Futures)
30 CATs (available as an optional module)
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.
- Research Essay (4,000 - 4,500 words; Weighting 100%) - 20 CATS
- Research Essay (5,000 - 5,500 words; Weighting 100%) - 30 CATS
- Histories of Big Data
- Big Data Epistemologies
- Researching with Big Data
- Access and Sampling
- Ethics & Privacy
- Data Justice & Decolonising Big Data
- Big Data Futures
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/
Important Registration Information:
You will need to make your optional module choices using the degree specific CIM module webform available in the CIM welcome page. All further instructions will be available to you on the webform.
The webform opens on Monday 12th September at 12:00 noon BST and closes on Monday 19th September at 12:00 noon BST
Gheerdhardhini (CIM PG Coordinator) will register you for your chosen modules, confirming your place in the module by 30th September, Friday.
If there are any queries, please get in touch with Gheerdhardhini via firstname.lastname@example.org
Computer Science – Please register your interest in the CIM module with the PG Administrator in your home department - DCS.PGT.Support@warwick.ac.uk
Psychology - Your PG Administrator will be in touch before Term 1 about registering interest for CIM modules
All other external students - Please contact the CIM PG Coordinator (Gheerdhardhini) via email (email@example.com), to request your optional module choice at the latest by Week 1 : Wednesday, 5th October, 17.00 BST.
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.
Please be advised that some modules may have restricted numbers and places are allocated according to availability and inter-departmental arrangements.
Please note that a request does NOT guarantee a place on the module and is subject to availability.
Gaining permission of a member of CIM teaching staff or a member of staff from your home department or filling in the eVision Module Registration (eMR) system with the desired module does NOT guarantee a place on that module.
Requests after the specified deadline will not be considered.
For external students - Only after confirmation of a place from CIM PG Coordinator can students’ or their home departments confirm their registration on eVision/MRM. Registrations by students who have not received confirmation of a place from CIM will be rejected via the system.
NOTE – The above-mentioned registration deadline also applies to the CIM optional modules running in Term 2. We will consider registrations again in the first week of Term 2, but only in relation to modules where there is availability.
We are normally unable to allow students (registered or auditing) to join/leave the module after the second week of it commencing.