Postgraduate Course: Understanding Data Visualisation (PGSP11484)
|School||School of Social and Political Science
||College||College of Arts, Humanities and Social Sciences
|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. 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 Foundations: Defining Data Visualisation
Week 2 Working with Data
Week 3 Research Design
Week 4 Tutorial
Week 5 Student Presentations
Week 6 Use of Data Visualisation
Week 7 Procuring advanced data visualization services
Week 8 The non-subject neutral nature of data visualisation
Week 9 Popular graphical formats: lists, matrices and barchart
Week 10 Analyzing Social Data Graphs
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Assessment 1 30%: (Group) Presentation.
Assessment 2 70%: 2.500 words essay.
||Assessment 1 by Week 5
Assessment 2 two weeks after the end of the course
|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
- identify, access and commission on-line data analytics tools and services appropriate to their needs
- appreciate the practical benefits and limitations of digital data for organizational decision-making
- understand when and how to procure social media data analytics services and how to combine them with existing knowledge practice
|Cleveland, W.S. (1994). The Elements of Graphing Data (Rev.ed.) Summit, NJ: Hobart Press.|
Kirk, A. (2016) Data Visualisation: A Handbook for Data Driven Design, SAGE.
Pollock, N., Campagnolo, G.M. (2015) Subitizing Practices and Market Decisions: The Role of Simple Graphs in Business Valuations, in: M. Kornberger, L. Justesen, J. Mouritsen, and A. Koed-Madsen, Making Things Valuable, Oxford University Press (89-108).
|Graduate Attributes and Skills
|Course organiser||Dr Gian Campagnolo
Tel: (0131 6)51 4273
|Course secretary||Ms Maria Brichs
Tel: (0131 6)51 3205