The Master of Data Science (Health) shares a common core with the other Master of Data Science programmes and will provide graduates with quantitative skills required for the practical analysis of health data. Health is a large and growing sector in the economy, and effective use of data is essential for improving clinical care for individual patients as well as overall public health. While generic data science skills are useful for Health Data Scientists, health data often have special characteristics (for instance, patient data or clinical metrics) which also require specialised techniques for collection, analysis, and management. This programme includes modules about using data for clinical and public health decisions, relevant modelling techniques such as survival and epidemiological methods, and also addresses questions such as governance and privacy. Shared core modules with the suite of Data Science Master courses will ensure that you get equipped with the wider statistical and machine learning skills required for your career. You will be carrying out team building activities, presenting case studies and carrying out both formative and summative assessments with students from all four faculties of Durham University, ensuring that you learn how to represent not just your own discipline but to also listen and integrate views and skills from other disciplines. An additional contribution to the academic environment will be provided by the Durham Research Methods Centre which will also help with the allocation of project topics through partnerships with neighbouring NHS Trusts and other collaborators in the health sector. All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas. Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage. The Master of Data Science suite of programmes is a course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow students to focus on an area of interest. The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment.
Core modules: The Master of Data Science (Health) programme is comprised of the following core modules: Introduction to Computer Science (optional under certain conditions) Introduction to Statistics for Data Science Introduction to Mathematics for Data Science (optional under certain conditions) Models and Methods for Health Data Science Health Informatics and Clinical Intelligence Research Project Examples of optional modules: Computational Social Science Society, Health and Wellbeing Machine Learning Data Exploration and Visualization Strategic Leadership Programming for Data Science
The Master of Data Science suite of programmes is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science. Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments. They are also developed through innovative teaching practices such as simulations. Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future. All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.
How to apply
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.
UK first or upper second class honours degree or equivalent. Applicants with a first degree (or equivalent experience) involving significant content in Mathematics or Computer Science will be considered on a case-by-case basis.
Fees and funding
|Northern Ireland||£12900||Year 1|
|Channel Islands||£12900||Year 1|
|Republic of Ireland||£29500||Year 1|
Additional fee information
For further information see the course listing.
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