Master of Data Science (Heritage)
MDS
1 year full-time
Durham City
G5P523
Course details
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, and this demand is predicted to further grow.
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 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.
The MDS culminates in the 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 of application of your choice. There may be an option to carry out the project in conjunction with an industry partner.
Course structure
Core modules:
The Data Science Research Project is a substantial piece of research into an area of data science unfamiliar to you, 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 develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. 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 provides students with experience of handling, amalgamating and analysing diverse datasets from a range of sources and across spatial and temporal scales. The module covers the most commonly used data science techniques in the Heritage and Archaeology sectors, giving the student experience in Geographical Information Systems and Google Earth Engine for prospection and heritage management and protection. The module also touches on the classification and interpretation of geochemical and petrographic digital imagery as well as 3D modelling of artefacts and standing features.
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.
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 and Bias in Data Science.
Learning
This interdisciplinary course is made up of modules that span departments across the University. It incorporates a wide range of learning and teaching methods which vary according to the modules studied. These include lectures, seminars, workshops and computer/practical classes. The taught elements are further reinforced through independent study, group work, 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 (Heritage) is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups.
The course comes together with a major research project, which is conducted and written up as an independent piece of work with support from your appointed supervisor.
Entry requirements
A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences.
Candidates with a degree in Archaeology, History, Classics, Anthropology, or heritage studies in the humanities more broadly are strongly encouraged to apply.
Evidence of competence in written and spoken English if the applicant’s first language is not English:
- Minimum TOEFL requirement is 102 IBT (no element under 23)
- Minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent
Fees and funding
Full Time Fees
Home students | £14,500 per year |
---|---|
EU students | £34,000 per year |
Island students | £14,500 per year |
International students | £34,000 per year |
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. More information is available here: Tuition fees - how much are they - Durham University
Please also check costs for colleges and accommodation.
Scholarships and Bursaries
We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities.
Find out more about Scholarships and BursariesCareer opportunities
Natural Sciences
The skills and knowledge that constitute a Masters qualification in Data Science are widely sought by employers around the globe. In today’s data-driven society, the ability to capture, analyse and communicate information and trends from the data generated by business, governments and their agencies, communities and organisations is highly prized.
It follows that Data Science is a rapidly expanding career sector with opportunities for stimulating and rewarding work in many sectors including the areas of science, humanities, health, environmental and social where understanding and expertise in data is leading to transformations in the way people live and work.
Department information
Natural Sciences
The Masters of Data Science programmes developing expertise on the capture and processing information derived from the vast volumes of complex data being generated across the globe that affects all our lives.
A wide range of groups such as businesses, researchers, governments, communities, families and individuals can all use that data to make more informed decisions and therefore increase the chances of better outcomes for society, in fields such as health, environmental sciences, and social analytics.
In an academic context, data science has a key role in underpinning research activity around many subject specialisms. Our Master of Data Science degrees are offered as conversion courses for those who hold a first degree that doesn’t have a strong component in data science.
Seven qualifications are available including the broad Master of Data Science as well as specialist routes in Bioinformatics and Biological Modelling, Digital Humanities, Earth and Environment, Health, Social Analytics, and Heritage.
Durham University is also home to the specialist Institute for Data Science, which acts as a hub for new ideas and works to realise its vision to help transform nature, society and culture. The Institute has many years of supporting taught degrees from Departments across the University.
Facilities
Data Science is a conversion course that incorporates content from many Departments across the University. This provides access to a selection of related state-of-the-art facilities from across the University, in particular Computer Science and Mathematics.
Facilities will depend on the subject specialism but include laboratories, libraries, project spaces, lecture theatres, study and networking spaces as well as shared social spaces. Most departments are close to the historic centre of Durham which is a UNESCO World Heritage site.
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