Studying at York
Data Science for Archaeology - ARC00123M
- Department: Archaeology
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2023-24
- See module specification for other years: 2024-25
Module summary
Archaeology by definition produces data which are a partial record of the past. In this module, you will learn techniques to explore, describe and compare such data. You will also learn how to communicate your findings effectively using plots and other outputs using the R statistical programming language. No prior experience of mathematics or coding is needed, and all concepts will be introduced using real-world case studies and examples. This module will provide a useful foundation for all students going on to work or study in data-driven fields.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2023-24 |
Module aims
This module aims:
- To equip students with the tools to differentiate meaningful patterns in data and test these using appropriate methods.
- To provide students with a broad understanding of data management techniques.
- To provide students with a solid foundation in the use of the R statistical programming language for a variety of tasks.
Module learning outcomes
By the end of the module the students should be able to:
- Demonstrate the ability to recognise meaningful patterns in new or old data using appropriate techniques
- Demonstrate a comprehensive understanding of how to Organise and manage data for analysis using best practice methods.
- Demonstrate a practical understanding of how established techniques of research are used to create and interpret knowledge through independent analysis of plots, tables and other outputs using R.
- Evaluate methodologies used in a wide range of scientific publications and develop critiques of them and, where appropriate, to propose new hypotheses
Module content
This module is designed to give you an understanding of the capabilities and common techniques used in R for data science. You will learn by doing: the lectures provide context while the practicals will provide you with hands-on experience working through problems and finding solutions in R. Each two-hour session will be half lecture and discussion, and half hands-on guided work. There are no required readings, instead students will be encouraged to work through a set of questions at the end of each practical in their own time.
The first set of lectures describe the necessity and utility of statistical methods in archaeology and related fields, while the first few practicals will familiarise you with the R environment, file management and associated workflows. We will then move on to describe methods for data management, including importing and exporting data in the R environment, before moving on to methods for describing data. The next set of lectures and practicals will cover tests for comparing parametric and non-parametric samples. These sessions will be followed by multivariate analysis, including methods for identifying the effects of interacting variables and methods for reducing dimensions in data sets such as PCA (principal component analysis). The module will conclude with an introduction to Bayesian inference and its growing application to archaeological problems.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Formative: oral feedback from module leaders
Summative: written feedback within the University's turnaround policy
Indicative reading
-
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.