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Practical Skills: Computational Archaeology - ARC00065I

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  • Department: Archaeology
  • Module co-ordinator: Dr. Peter Schauer
  • Credit value: 20 credits
  • Credit level: I
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

Our understanding of the past and present is mediated through data. This module provides a practical and theoretical foundation for the use of data-driven, computational methods in archaeology. You will learn methods for exploring, describing and comparing samples, managing data, and producing attractive maps and plots using the R statistical environment. No prior experience of maths or coding is needed, and all concepts will be introduced using case studies and examples from the Beazley Archive Pottery Database. This module will provide a useful foundation for all students, and will be especially useful for those going on to study topics in data-driven fields.

Related modules

A directed option - students must pick a Practical Skills module and have a choice of which to take (one in Semester 2)

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

The Practical Skills modules seek to introduce you to a range of skills in various diverse areas of archaeological practice and are designed to allow you to gain experience in a 'hands-on' manner.

This specific module aims:

  • To provide students with the ability to generate testable hypotheses from archaeological data.
  • To provide students with a good understanding of methods for managing archaeological data.
  • To introduce students to the skills of managing data and producing useful outputs using the R statistical programming language.

Module learning outcomes

By the end of the module the students should be able to:

  • Demonstrate an awareness of how to formulate a data-driven hypothesis, test it with an appropriate statistical method and accurately describe the results.
  • Demonstrate an understanding of the methods used in a wide range of scientific publications and interpret their results.
  • Apply the methods and techniques they have learned to use the R statistical programming language for a variety of tasks.

Module content

The module will provide students with a solid understanding of computational approaches to archaeology, and how to apply these using the R environment. Lectures will complement practicals, and students will learn by working through guided problems that illustrate key concepts. Case studies will be provided from the Beazley Archive Pottery Database, which contains a large list of Athenian figure-painted pottery. Using this data set, we will examine key concepts such as data import and export, map making, and the production of descriptive graphs and plots.

The first several weeks will introduce the case study and the role of pottery in the Athenian world, as well as the R statistical environment. We will cover file management, data import and export, and methods for describing data. We will then go on to create data-driven hypotheses using examples, and test these using appropriate statistical methods. We will then go on to produce maps, including KML outputs for other applications, such as Google Earth. Finally, we will bring all of the methods covered in the course together by creating simple models to test complex hypotheses. By the end of the module students will be able to apply methods from the course to their own case studies.

Assessment

Task Length % of module mark
Essay/coursework
Portfolio		 N/A 100

Special assessment rules

None

Additional assessment information

Students will work week by week towards their summative assessment during their activities in class.

Reassessment

Task Length % of module mark
Essay/coursework
Portfolio		 N/A 100

Module feedback

Formative: oral feedback from module leaders in class

Summative: written feedback within the University's turnaround policy

Indicative reading

  • Osborne, R. 2004 "Workships and the iconography and distribution of Athenian red-figure pottery: a case study," in Greek art in view: essays in honour of Brian Sparkes. Edited by S. Keay and S. Moser, pp. 78-94. Oxford: Oxbow.
  • Carlson, D. L. (2017). Quantitative Methods in Archaeology Using R. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781139628730
  • Shennan, S. (2014). Quantifying Archaeology. Academic Press.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.