- Department: Archaeology
- Module co-ordinator: Dr. Peter Schauer
- Credit value: 20 credits
- Credit level: I
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
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.
A directed option - students must pick a Practical Skills module and have a choice of which to take (one in Semester 2)
|Semester 1 2024-25
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:
By the end of the module the students should be able to:
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.
|% of module mark
Students will work week by week towards their summative assessment during their activities in class.
|% of module mark
Formative: oral feedback from module leaders in class
Summative: written feedback within the University's turnaround policy