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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2016/2017

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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Understanding Data Visualisation (PGSP11392)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryInternetworked 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?
Course description 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 colour 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?

Outline content

1. Production and Use of Data Visualisation
This first week introduces students to the key aspects of data visualization necessary to fill the gap in studies of visual numbers by focusing on the context of consumption, or, as Cleveland (1985) puts it, on the "decoding skills" that are necessary to make sense of visual numbers.
2. Popular graphical formats: lists, matrices and double entry books
Visual displays are increasingly becoming part of oral presentations (or embedded in video recordings of oral presentations). And oral presentations are not written documents. They are real time demonstrations, given to an audience, using a device and a demonstrator providing a voice-over about the object. 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. 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.
3. "Please make me a visualization": procuring advanced data visualization services
In this week we will discuss most advanced data visualisation solutions for dynamic datasets with Hermann Zschiegner (TWO-N), designer of the software currently in use for NYSE data wall.
4. Tutorial: Analyzing Information Graphics
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
5. Student Presentations
Students will draw on on criteria discussed in Week 4 & Week 3 Reflective Activity to discuss one graph of their choice. Presentation should be 20' long and should be supported with Power Point.

This is a 10 credits course taught online using Moodle over five weeks through a series of weekly a-synchronous study sessions and synchronous virtual classrooms (delivered using Collaborate). When taken by on campus students, online teaching will be complemented by individual contact hours, face-to-face tutorials and (if applicable) reading group activities to make the teaching experience fully equivalent to on-campus teaching.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2016/17, Available to all students (SV1) Quota:  None
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Knowledge and Understanding of Digital Research - understand the work-practices of information professionals in digital research
  2. Applied Knowledge and Understanding of Digital Research - make best use of the results of digital data analytics for service design, marketing and institutional reputation management
  3. identify, access and commission on-line data analytics tools and services appropriate to their needs
  4. Cognitive Skills in Digital Research - - appreciate the practical benefits and limitations of digital data for organizational decision-making
  5. understand when and how to procure social media data analytics services and how to combine them with existing knowledge practice
Reading List
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) [1983], 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

Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Gian Campagnolo
Tel: (0131 6)51 4273
Email: g.campagnolo@ed.ac.uk
Course secretaryMs Nicole Develing-Bogdan
Tel: (0131 6)51 5067
Email: v1ndeve2@exseed.ed.ac.uk
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