Taught course

Master of Data Science (Digital Humanities)

Durham University · Faculty of Science

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

Months of entry


Course content

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
  • Qualitative Approaches to Digital Humanities
  • Computer Music
  • Ethics and Bias in Data Analytics

Information for international students

If you are an international student who does not meet the requirements for direct entry to this degree, you may be eligible to take a pre-Masters pathway programme at the Durham University International Study Centre.

Fees and funding

UK students
International students

For further information see the course listing.

Qualification, course duration and attendance options

  • MDS
    full time
    12 months
    • Campus-based learningis available for this qualification

Course contact details