Healthcare Data Science (EPSRC CDT) (Taught)

The information provided on this page was correct at the time of publication (November 2024). 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 Oxford EPSRC Centre for Doctoral Training in Healthcare Data Science is a four-year doctoral cohort-based training programme offering opportunities for doctoral study in computational statistics, machine learning, data engineering and infectious disease analytics within the context of ethically-responsible health research.

This course is 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 followed by a third term where you will usually undertake two ten-week research projects in two of your chosen research areas. One of these projects will usually become the basis of your doctoral research, carried out in the following three years.

During the first year, your day will typically comprise of lectures each morning with practical computational exercises each afternoon.

The taught courses covering core subjects such as computational statistics, machine learning, data engineering, ethics and governance, and health research methodology include the following:

• Ethics

• Software Engineering

• Statistical Methods

• Research Methods

• Machine Learning

• Bayesian Statistics

• Medical Imaging

• Biomedical Image Analysis

• Biomedical Time Series Analysis

• Device and Sensor Data

• Genetics

• Infectious Diseases

• Modelling for Policy Making

• Data Governance

• Data Engineering

• Health Data Quality

• Health Data Standards

• Data-driven Innovation.