Explore how quantitative and computational methods can be used to explore and analyse large-scale social data to make predictions and inform future practice.
From security cameras and satellite transmission to our mobile devices, homes, cars, and in the workplace, we are surrounded by data. 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 environment. 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 (Digital Humanities), a conversion course that equips you with the skills to access, clean, analyse and visualise data, opening a future in data science even if your first degree is in a non-quantitative subject such as humanities.
The MDS provides training in contemporary data science, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses build wider skills in statistical and machine learning, while additional subject-based modules in digital humanities give you the opportunity to explore the application of quantitative and computational methods to cultural data: languages, literary, philosophical and theological texts, historical data, artefacts and material culture, visual art, video and music.
The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks. Optional modules allow you to focus on an area of interest.
The MDS culminates in the research project, an in-depth investigation into an area of specific interest in which you apply the skills you’ve learned during the course to a research problem in a humanities domain of your choice.
Course Structure
The Data Science Research Project is a substantial piece of 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 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.
Digital Humanities: Practice and Theory introduces you to contemporary debates on the future of the humanities in an increasingly digital world. You will learn about the most important technical tools for representing and manipulating cultural artefacts in digital form, and how to apply cutting-edge theoretical frameworks and technical tools to practical problems in Digital Humanities.
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.
Machine Learning introduces the essential knowledge and skills required in machine learning for data science. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, neural networks and deep learning.
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
Text Mining and Language Analytics
Data Exploration, Visualisation, and Unsupervised Learning
Strategic Leadership
Data Science Applications in Archaeology and Heritage
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, 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 (Digital Humanities) is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups.
The course culminates in 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 Arts and Humanities 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
The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).
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
Postgraduate Natural Sciences provision at Durham is focused data science and its expertise in capturing 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 as diverse as health, the environment and social analytics.
In an academic context, data science has a key role in underpinning research activity around many subject specialisms in many disciplines. Our Master of Data Science degrees are offered as conversion courses for those who hold a first degree that is not highly quantitative.
Six 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 and Social Analytics.
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.
Rankings:
2nd in The Times and Sunday Times Good University Guide 2024
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.
Apply
Find out more:
Apply for a postgraduate course (including PGCE International) via our online portal.
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