Undergraduate Course: Visualizing the past - data, narratives and visual explanations (ARCA10087)
Course Outline
| School | School of History, Classics and Archaeology |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course introduces students to the exploration of the human past through Information Visualization methods. It will provide a global perspective of current applications covering the visual display of social dynamics over time and space. Students will become proficient users of the open source package R while developing critical skills on the use of
visualization within archaeological and historical projects. |
| Course description |
The development of digital technologies is generating a vast amount of data about our current and past societies. How can we make use of these new methods to understand of the past?
This course will explore how Information Visualization can improve the interpretation of historical and archaeological case studies through the creation of innovative graphics. Through a mixture of lectures, practicals, in-class discussions, and presentations the students will learn to identify and interpret patterns inferred from archaeological and historical evidence. They will also become aware of the potentials and limitations of Data Science specifically linked to the study of the past, including topics such as time and uncertainty.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Exploring the Past with Data Science (PGHC11461)
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Other requirements | Archaeology 2A and 2B, or Honours entry to degrees in Classics, or equivalent. |
Information for Visiting Students
| Pre-requisites | Visiting students should have at least 3 Archaeology courses at grade B or above (or be predicted to obtain this). We will only consider University/College level courses. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate the ability to design an implement meaningful visualizations of archaeological and historical data;
- demonstrate the ability to understand and critically analyse current practices of information visualization;
- demonstrate the ability to apply Exploratory Data Analysis methods to identify patterns in archaeological and historical data;
- demonstrate critical understanding of the issues surrounding the identification, quantification and display of spatiotemporal social dynamics;
- demonstrate knowledge on the uses of visualization tools beyond archaeology;
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Reading List
Fekete, J.-D., van Wijk, J. J., Stasko, J. T., & North, C. (2008). The Value of Information Visualization. In A. Kerren, J. T. Stasko, J.-D. Fekete, & C. North (Eds.), Information Visualization (Vol. 4950, pp. 1-18). Berlin, Heidelberg: Springer Berlin Heidelberg.
Fletcher, M., & Lock, G. R. (2005). Digging numbers: elementary statistics for archaeologists (Vol. 33). Oxford Univ School of Archaeology.
Gupta, N., & Devillers, R. (2016). Geographic Visualization in Archaeology. Journal of Archaeological Method and Theory.
Llobera, M. (2011). Archaeological Visualization: Towards an Archaeological Information Science (AISc). Journal of Archaeological Method and Theory, 18 (3), 193-223.
Spence, R. (2014). Information visualization: an introduction (Third Edition). Cham Heidelberg New York Dordrecht London: Springer.
Tufte, E. R. (1997). Visual explanations: images and quantities, evidence and narrative. Cheshire, Conn: Graphics Press.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed). Cheshire, Conn: Graphics Press.
Wickham, H. (2009). Ggplot2: elegant graphics for data analysis. New York: Springer. |
Additional Information
| Graduate Attributes and Skills |
On successful completion of the course, students should be able to:
- gather, integrate and critically assess relevant information
- extract key elements and meanings from complex data sets
- answer a research question by developing a reasoned argument based on quantitative analysis
- present their ideas and analyses in a coherent fashion |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Xavier Rubio-Campillo
Tel: (0131 6)50 3592
Email: xavier.rubio@ed.ac.uk |
Course secretary | Miss Lorna Berridge
Tel:
Email: Lorna.Berridge@ed.ac.uk |
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