Postgraduate Course: Exploring the Past with Data Science (PGHC11461)
Course Outline
| School | School of History, Classics and Archaeology |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
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 |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2017/18, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 11,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 11,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
169 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
60 %,
Practical Exam
40 %
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| Additional Information (Assessment) |
1. Short report on selected visualization (20%)
Students will choose one visualization within a list of classic examples. They will need to perform a short analysis of the visualization. This will be complemented by a lightning presentation of its contents and methods.
2. Essay (40%)
Students will need to choose and critically assess 2 visualizations from Archaeology or History journals (a good one and a bad one).
3. Poster presentation (40%)
Four case studies will be offered including a set of research questions, a dataset and some guidelines. The student will apply the methods learned to design and implement a visualization of the dataset. They posters themselves will also be submitted and assessed. |
| Feedback |
Students will receive verbal feedback during each practical and written feedback for the assessments following standard Learn procedure. They will also have the opportunity to discuss that feedback further with the Course Organiser during his published office hours or by appointment. |
| No Exam Information |
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)51 7112
Email: |
Course secretary | Mr Gordon Littlejohn
Tel: (0131 6)50 3782
Email: |
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