The MSc in Health Data Science programme provides specialist training in the area of Health Data Science, aiming to educate current and future health data scientists, at all career stages, including those in the public and private sectors. Training is grounded in the team science approach to addressing important health research questions, including theory alongside practical application, and introducing you to new forms of health data. Team science communication skills development will be an essential component of each module of the programme.
Developed by internationally excellent research groups in biostatistics, public health, and computer science, combined with a number of world-leading centres in strategic areas of health, the programme offers a rigorous curriculum with research-focused teaching, training, and development in health data science.
The programme will expose you to core health research, statistics and computer science modules, with the opportunity to obtain specialised knowledge along a statistics track, computer science track, or a combination.
By completing the first semester you qualify for the PG certificate. By completing the second, you qualify for the PG Diploma. Then, by completing your dissertation, you qualify for the MSc.
The programme is in a blended format using both online and campus based delivery. It is available on both a full-time and part-time basis.
Please note this programme is suitable for intercalating medical students.
Watch the following short video to hear Professor Iain Buchan talk about this programme and the work of health data scientists at the University of Liverpool during the COVID-19 pandemic:
Why Department of Health Data Science?
The Department of Health Data Science (previously known as Department of Biostatistics) is committed to high quality applied research and teaching. The Department has a number of methodological research interests and our members are engaged in a broad range of collaborative projects. The main research areas are:
- Clinical trials methodology
- Statistical modelling
- Health informatics
- Statistical pharmacogenetics
- Evidence synthesis.