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level7

7AAVDM16 Data Visualisation

Module convenor: Dr Ashwin Mathew 
Credits: 
20
Teaching pattern: Ten one-hour lectures and ten one-hour seminars
Module description:

In the digital age, data visualisations are becoming an increasingly prominent way to represent and organise collective life – from digital analytics dashboards to interactive news graphics. Data visualisations are also becoming more popular as tools for exploration and analysis in research contexts, including for social and cultural inquiry. This module will provide a hands-on introduction to creating, using and studying data visualisations. It will cultivate a “critical data practice” for working with data visualisations in research as both instruments and objects of study. As well as facilitating creative experimentation with tools, methods, approaches and best practices for creating data visualisations, it will also draw on leading research from media studies, science and technology studies and associated fields in order to support critical reflection about their evolving role in the digital age.

Module aims

  • This module aims to familiarise students with data analysis and visualisation
  • The module will provide a brief introduction to the history of data analysis and visualisation
  • The module will set the foundations of modern data processing within the context of the media industry
  • The module will provide students with the mindset and processes required to analyse from beginning to end using open source technology (e.g. Rshiny, Plotly, ggplot2, etc.)

Learning outcomes

At the end of the module the student will be able to:

  • Explore small datasets using basic coding skills
  • Use open-source technology to visualise data insights
  • How to publish results online and share with stakeholders

Core reading

The following list is illustrative, and some further readings associated with this course can be found at: https://www.zotero.org/groups/data_visualisation

Berger, J. (1972). Ways of Seeing. London: Penguin Classics.

Bertin, J. (1983). Semiology of Graphics: Diagrams, Networks, Maps. (W. J. Berg, Trans.). Madison, WI: University of Wisconsin Press.

Bounegru, L., Venturini, T., Gray, J., & Jacomy, M. (2017). Narrating Networks: Exploring the Affordances of Networks as Storytelling Devices in Journalism. Digital Journalism, 5, 699–730.

Drucker, J. (2014). Graphesis: Visual Forms of Knowledge Production. Cambridge, MA: Harvard University Press.

Galison, P. (1990). Aufbau/Bauhaus: Logical Positivism and Architectural Modernism. Critical Inquiry, 16, 709–752.

Gray, J., Bounegru, L., Milan, S., & Ciuccarelli, P. (2016). Ways of Seeing Data: Toward a Critical Literacy for Data Visualizations as Research Objects and Research Devices. In Kubitschko, Sebastian & Kaun, Anne (Eds.), Innovative Methods in Media and Communication Research (pp. 227–251). London: Palgrave Macmillan.

Halpern, O. (2015). Beautiful Data: A History of Vision and Reason Since 1945. Durham: Duke University Press Books.

Kennedy, H., & Engebretsen, M. (Eds.). (2019). Data Visualization in Society. Amsterdam: Amsterdam University Press.

Lima, M. (2011). Visual Complexity: Mapping Patterns of Information. New York: Princeton Architectural Press.

Segel, E., & Heer, J. (2010). Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics, 16, 1139–1148.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd edition edition). Cheshire, CT: Graphics Press.

Assessment

Data visualisation project with 1000 word report. (100%)

The modules run in each academic year are subject to change in line with staff availability and student demand so there is no guarantee every module will run. Module descriptions and information may vary depending between years. 

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