Healthcare Data Science (EPSRC CDT) (Taught)

The information provided on this page was correct at the time of publication (November 2025). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.

The EPSRC CDT in Healthcare Data Science is a four-year cohort-based training programme offering doctoral study in computational statistics, machine learning, data engineering and infectious disease analytics within the context of ethically-responsible health research.

This course is administered by the Medical Sciences Doctoral Training Centre and jointly run by a range of Oxford departments including the departments of Computer Science, Statistics, Engineering Science, the Nuffield Department of Medicine, and the Nuffield Department of Population Health.

The Oxford EPSRC CDT in Healthcare Data Science is based in the Oxford Big Data Institute (BDI) a purpose-built research institute at the heart of the University's biomedical campus.

The Institute combines researchers from genomics, epidemiology, population health, and infectious disease alongside those from computer science, statistics and engineering to develop the field of big data as applied to biomedical research. Scientists working in the Institute form an analytical hub, deeply connected to the wider experimental and clinical community in Oxford and beyond, working to solve some of the major challenges in medical research. The BDI aims to develop, evaluate and deploy efficient methods for acquiring and analysing information at scale and for exploiting the opportunities presented by large-scale studies. Its activity includes, the analysis population scale data, derived from health records, genetics and biomarkers, the analysis of images and application of machine learning, and the analysis of single cells and molecular proteomic and transcriptomic data.

Course structure
The course begins with a training year, which consists of two terms of intensive training in core data science principles and techniques. Your day will typically comprise of lectures each morning with practical computational exercises each afternoon. The taught courses cover core subjects such as computational statistics, machine learning, data engineering, ethics and governance, and health research methodology.

You will develop an understanding of relevant concepts and techniques that is not only enough to enable their application and integration but will also serve as a solid foundation should you choose to pursue research in that area.

Each term of taught modules concludes with an extended, team-based two-week data challenge where you will work in small groups with clinicians and domain experts to address questions using large healthcare datasets.

In the third term of the first year, you will usually undertake two ten-week research projects in two of your chosen research areas. These are usually selected from a pool of projects at the start of your second term.

These projects are proposed by Oxford faculty members but you may also contact faculty members to jointly propose projects. There are always more projects than students, and students are typically matched to, at least, their first choice, but it is not possible to guarantee that you will be able to work with a particular member of staff.

The projects will provide you with experience of working as part of an active research group and the opportunity to explore specific areas before writing a proposal for your doctoral research.

At the end of the summer of the first year, you will normally select one of these two projects to become the basis of your DPhil research, carried out in the following three years.

While working on your research project, you will have the opportunity to participate in a range of activities including an ethics placement, four-week external data challenge, seminar series and annual CDT retreats. Skills training will also continue alongside your project.