Master of Data Science (Heritage)

Entry requirements

We require a 2:1 Bachelor (Honours) degree or international equivalent in any degree subject.

Applicants with a strong data science or mathematical background may wish to consider our MSc Scientific Computing and Data Analysis programmes.

Months of entry

September

Course content

Cultural heritage offers a sense of identity, helps maintain social diversity, cohesion, and intercultural dialogue, and forms part of our basic right to participate in cultural life. Data Science techniques are playing an increasing role in this sector, helping practitioners to monitor and protect heritage assets such as archaeological sites, present information to the public and critically assess the role of heritage in contemporary debates.

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their natural and cultural environments. This has led to a significant increase in demand for skilled data scientists.

Drawing on this, we have created the Master of Data Science (Heritage), a conversion course that equips you with the skills to generate, access, clean, analyse and visualise data, opening a future in data science even if your first degree is in a non-quantitative subject. It is likely to appeal to archaeologists, anthropologists, curators, and historians who want to learn how to use the data produced in modern research, industry and government contexts to manage heritage resources and spatio-temporal information flows.

The course provides training in contemporary data science. You will be based in a supportive environment, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses will equip you with wider statistical and machine learning skills, while subject-specific modules develop your quantitative skills in the field.

The programme culminates with a research project, an in-depth investigation in which you apply the skills learned during the course to a research problem working alongside an expert in the area.

The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as neural networks, analysis of spatial and temporal datasets and deep learning. Optional modules allow you to focus on an area of interest.

Course structure

Year 1 modules

Core modules:

The Data Science Research Project

is a substantial piece of self directed research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.

Critical Perspectives in Data Science and AI

develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically utilising AI applications. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project.

Data Analysis in Space and Time

provides an understanding of data methods and tools used in the field of earth and environmental sciences, with a particular focus on those used for analysing spatial and temporal datasets. You will also learn about the physical modelling of complex real-world systems and use popular software packages currently used in industry settings.

Data Science Applications in Heritage and Archaeology

gain hands-on experience handling and analysing diverse heritage datasets across time and space. Learn key techniques including GIS, Google Earth Engine, geochemical and petrographic image analysis, and 3D modelling. This module focuses on digital tools for prospection, heritage management, and the interpretation of archaeological and cultural data.

Programming for Data Science

uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.

Introduction to Statistics for Data Science

focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.

Optional modules:

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

• Introduction to Computer Science
• Introduction to Mathematics for Data Science
• Machine Learning
• Text Mining and Language Analytics
• Data Exploration, Visualisation, and Unsupervised Learning
• Strategic Leadership
• Ethics of Artificial Intelligence and Data Science

Information for international students

Fees and funding