Postgraduate Course: Understanding Data Visualisation (PGSP11442)
|School||School of Social and Political Science
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Available to all students
|Summary||Internetworked digital technologies generate, store and elaborate vast quantities of data. This raises questions about how best to make sense of such voluminous and potentially fast-changing information, especially when data is used to take real-time decisions (about investments, policies, trades and new markets) or it needs to be presented to wider audiences. The use of data visualization is increasing in the digital age where much information is consumed via full color displays. Consequently, we are experiencing a period of rapid innovation in new data visualization techniques intended to serve this purpose. In this course, we examine the visual aspects of data analytics and the emerging professional practices of turning 'numbers into pictures' or, more specifically, into 'screen realities'. Hosting contributions from key experts in the field, the course will provide students will skills to critically interpret the most popular data visualization techniques used by major information provider firms such as Bloomberg, Gartner, Reuter or Telerate. Questions addressed in this course include: What are the benefits and limitations of popular data visualization formats (e.g. lists, 2x2 matrixes, pie charts, bar charts)? What are the most advanced digital data visualization techniques and software tools? What are the different steps through which raw data become amenable to be represented in graphics? What is lost and what is gained in the process of preparing data for visual display?
Week 1 - Production and use of Data Visualisation
Whenever the pictorial aspect of number is considered the main reference is Tufte's (2001) Visual Display of Quantitative Information. However, as reminded by Stark & Paravel (2009), visual displays are increasingly becoming part of oral presentations (or embedded in video recordings of oral presentations). Furthermore, visual display of quantitative information are increasingly produced in a digital format that allow them to circulate independently from the oral presentation and be widely distributed over the internet, generating specific "geographies of persuasion" (Stark & Paravel, 2009: 33). This first week introduces students to the different steps of the process of data visualization (production, engagement and deployment, see Burri & Dumit, 2008) necessary to fill the gap in studies of visual numbers by focusing on the context of consumption, or, as Cleveland (1994) puts it, on the "decoding skills" that are necessary to make sense of visual numbers.
Week 2 - Popular graphical formats: lists, matrices and double entry books
Whilst scholars have linked the issue of how a figuration might facilitate and mediate a decision (Miller & O'Leary, 2007), they have not yet considered how calculations might be shaped by and result from the specific socio-material features of a graph. For example it has been noted that, by bringing different actors in the same space, listing as well as other visual devices they enact them as direct rivals (Kornberger & Carter, 2010). Discussed will also be the role of 2x2 matrixes such as the famous Gartner's Magic Quadrant in limiting the number of technology providers in a market (Pollock & D'Adderio, 2012) and the way double-entry book (Quattrone, 2009) shaped accounting practices.
Week 3 - "Please make me a visualization" : procuring advanced data visualization services
Investors, trading houses and market development people rely upon information provider firms such as Reuter, Bloomberg and Telerate keeping up with the new, their clients may not be able to interpret information in a more nuanced way. Therefore, information provider firms increasingly rely on advanced digital data visualization solutions. In this week we will discuss with Hermann Zschiegner (TWO-N), designer of the software currently in use for NYSE data wall, what datavis is about. We will address in particular, type of briefs his studio receives from clients.
Reflective Activity: A transcription of an interview with Hermann Zschiegner will be made available to students before the guest lecture. Students are invited to prepare one question to be submitted to the guest lecturer based on their analysis of the interview.
Week 4 - Tutorial: Analyzing Information Graphics
Information graphics, distinct from photography and video, use visual means (charts, graphs, network webs, diagrams, etc) to concisely convey and enliven both simple and complex relationships drawn from data. Information graphics have long been a part of making sense of social science data, especially when the data is being presented to wider audiences. The use of information graphics is increasing in the digital age where much information is consumed via full color displays. Creating coherent, compelling information graphics is left largely to individual practitioners. As Lara Noren reminds in her Graphic Sociology blog (see below), there is very little training available for people who would like to have basic graphic design skills in their repertoire. In this week we will discuss analysis of the visual presentation of social data from the perspective of social science practice, focussing on different approaches to identify the successes and weaknesses of graphics.
Reflective Discussion Topic: Take one article from one of the following blogs (http://thesocietypages.org/graphicsociology or http://thewhyaxis.info/ and try to extract the criteria they use for analysing visual images). Apply the same criteria to another data visualization of your choice.
Week 5 - Who are data visualizers? Professional Practices in data visualization
A growing number of specialists collaborate with the aim to progress and innovate visualization techniques. Among them are data journalists (Gray, Bounegru & Chambers, 2014), 'computational architecture groups' (Pryke, 2010), Graphical User Interface (GUI) designers as well as artists (Viegas & Wattenberg, 2011). In this week we will provide an overview of the different professions in the field of data visualisation, with the aim of understanding their different approaches and positioning in the field.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Enrolment restricted to students on the Online Distance Learning Data Science Programme only.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
||Block 5 (sem 2)
|Course Start Date
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||29 May ┐ 30 Jun/2017
Assessment 1 30% Individual Presentation - W5
Assessment 2 70% 2000 words Essay - 7th Aug 2017
|No Exam Information
On completion of this course, the student will be able to:
- - understand the work-practices of information professionals in digital research;
- - make best use of the results of digital data analytics for service design, marketing and institutional reputation management;
- - appreciate the practical benefits and limitations of digital data for organizational decision-making;
- - identify, access and commission on-line data analytics tools and services appropriate to their needs;
- - understand when and how to procure social media data analytics services and how to combine them with existing knowledge practice.
|Burri, RV and Dumit, J. (2008). Social studies of Scientific imagining and visualization. In: Hackett E et al. (eds) The Handbook of Science and Technology Studies, 3rd Edn. Cambridge, MA: MIT Press, 297-317.|
Cleveland, W.S. (1994). The Elements of Graphing Data (Rev.ed.) Summit, NJ: Hobart Press. (CHAPTHER 4).
Gray, J., Bounegru, L., Chambers, L. (2014) The Data Journalism Handbook. 1.0 BETA Available at: datahournalismhandbook.org.
Kornberger, M., & Carter, C. (2010). Manufacturing competition: How accounting practices shape strategy making in cities. Accounting, Auditing & Accountability Journal, 23(3), 325-349.
Lynch, M. & Woolgar, S. (1990) Representation in Scientific Practice. (Cambridge, Mass.: MIT Press).
Miller, P., & O'Leary, T. (2007). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organisations and Society, 32, 701-734.
Pollock, Neil & D'Adderio, Luciana (2012) Give me a two-by-two matrix and I will create the market: Rankings, graphic visualisations and sociomateriality In: Accounting, Organizations and Society Issue 8 37 pp. 565 - 586.
Pryke, M. (2010) Money┐s eyes: the visual preparation of financial markets. Economy and Society, 39(4): 427-459.
Quattrone, P. (2009). Books to be practiced: Memory, the power of the visual, and the success of accounting. Accounting, Organizations & Society, 34(1), 85-118.
Stark, D., Paravel, V. (2009) PowerPoint in Public: Digital Technologies and the New Morphology of Demonstration." Theory, Culture & Society 2008, 25(5):31-56.
Tufte, Edward R (2001) , The Visual Display of Quantitative Information (2nd ed.), Cheshire, CT: Graphics Press.
Viegas, F., Wattenberg, M. (2001). How to make data look sexy. Cnn.com editorial, April 19, 2011, available at: http://edition.cnn.com/2011/OPINION/04/19/sexy.data/index.html?_s=PM:OPINION
|Graduate Attributes and Skills
||Enrolment is restricted to students on the Online Learning Data Science Programme only.
|Course organiser||Dr Gian Campagnolo
Tel: (0131 6)51 4273
|Course secretary||Ms Nicole Develing-Bogdan
Tel: (0131 6)51 5067
© Copyright 2016 The University of Edinburgh - 3 February 2017 5:02 am