The Master of Data Science (Heritage) stream equips you with cutting-edge data science skills to tackle real-world challenges in heritage preservation, from digitising artefacts to analysing architectural datasets preparing you for impactful careers in cultural heritage, museums, conservation, and beyond.
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
This interdisciplinary course is made up of modules that span across the University. It incorporates a wide range of learning and teaching methods , these include lectures, seminars, workshops and computer/practical classes. The taught elements are further reinforced through independent study, research and analysis, case studies and structured reading.
All modules are underpinned by research and embed elements of research training in both delivery and assessment. Throughout the course you will be encouraged to develop research methods, skills and ethics reflecting the methods used by the research-active staff. Overall, you will be encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for use in future work or research.
Assessment
The Master of Data Science is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups.
The course culminates with a major research project, which is conducted and written up as a self-directed piece of work with support from your appointed supervisor.
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.
International students who do not meet direct entry requirements for this degree might have the option to complete an International Foundation Year.
Home students who do not meet our direct entry requirements, may be eligible for our Foundation Programme which offers multidisciplinary programmes to prepare you for a range of specified degree programmes.
The fees for this academic year have not been confirmed yet.
The tuition fees shown are for one complete academic year of study and are set according to the academic year of entry. Fees will be subject to an annual inflationary increase and are expected to rise throughout the programme of study. The fee listed above is for the first year of the course only.
In today’s data-driven world, the ability to capture, analyse, and communicate insights from complex information is one of the most sought-after skills by employers globally.
Graduates are equipped for exciting and meaningful careers across a wide range of sectors from science, health, and the environment to the humanities and social policy where data is driving innovation and transforming how we live and work. Whether you're influencing public health strategies, advancing climate research, or unlocking cultural insights, your data science expertise will open doors to impactful opportunities.
Department information
Natural Sciences
Our Master of Data Science programmes give you the tools to capture, analyse, and unlock insights from the vast streams of data shaping our world. These interdisciplinary degrees empower smarter decision-making across sectors from health and environment to culture and society.
The Master of Data Science programmes at Durham University equip you with the skills to capture, process, and analyse the vast and complex data shaping every aspect of modern life. From healthcare and environmental science to social policy and cultural heritage, data science is transforming how decisions are made and problems are solved.
The suite of programmes are designed as conversion degrees, ideal for students from non-data science backgrounds who want to transition into this high-impact field. You'll gain a strong foundation in data science while exploring its applications across a range of disciplines.
Choose from seven pathways tailored to your interests and career goals: the Master of Data Science or Master of Data Science with AI Applications, or specialist routes in Bioinformatics and Biological Modelling, Digital Humanities, Earth and Environment, Health, Social Analytics, and Heritage.
As a student, you’ll benefit from Durham’s interdisciplinary approach and the expertise of the Institute for Data Science, a hub for innovation that supports research and teaching across the University. Join a vibrant academic community working to transform nature, society, and culture through data.
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