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As part of this programme, you will gain an understanding of techniques that are transforming medical research and creating exciting new commercial opportunities.
Today, some of the most exciting, stimulating and productive research is carried out using large collections of data acquired in big collaborative endeavours or major public or private initiatives. This programme covers computational and statistical methods as applied to problems in data-intensive medical research. The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science.
To find out more information about this degree, such as entry requirements, programme length and cost, visit the UCL prospectus webpage. To hear from our current and former students, visit the study page.
Applications for 2022/23 will remain open from 18th October 2021 to 31st March 2022.
About the course
The programme is designed to meet a need, identified by the funders of health research and by a number of industrial organisations and healthcare agencies, for training in the creation, management and analysis of large datasets. This programme is practical, cross-disciplinary and closely linked to cutting-edge research and practice at UCL and UCL’s partner organisations.
Why study with us?
- The programme is delivered through a collaboration between UCL and the University of Manchester, the two largest teams in this field in the UK.
- The programme is supported by the NHS graduate management training scheme and a wide range of other employers. You will join a cohort with a diverse mix of educational and technical backgrounds, and this shared experience is one of the strengths of the programme.
- The programme is delivered through a mix of face to face teaching and online learning, designed to fit into your working life. Assignments are often relevant to students' working lives and many students complete a dissertation in their place of work
Careers
Data science is a rapidly growing field of employment at the moment and employers recruiting in health data science include government agencies, technology companies, consulting and research firms as well as scientific organisations. A number of employers - IQVIA, Roche, AstraZeneca and Public Health England - are supporting the programme in different ways, including providing paid internships to selected students.
Course Content
Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.
Compulsory modules
- Basic Statistics for Medical Sciences (IEHC0046)
- Data Methods for Health Research (CHME0013)
- Principles of Health Data Science (CHME0012)
- Regression Modelling (IEHC0050)
- Software Development with Python for Health Data Science (CHME0031)
- Dissertation in Health Data Science(CHME0021)
Optional modules
- Advanced Statistics for Records Research
- Essentials of Informatics for Healthcare Systems (CHME0025)
- Machine Learning in Healthcare and Biomedicine (CHME0016)
- Public Health Data Science (CHME0017)
- Computational Genetics of Healthcare (CHME0034)
- Advanced Machine Learning for Healthcare (CHME0035)
Dissertation/report
All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).
Examples of past projects:
- Generating and Evaluating Synthetic Mixed-type Structured Electronic Health Records Based on State-of-the-art Generative Adversarial Networks
- Prediction of Alzheimer’s Disease (AD) from MRI using a Convolutional Neural Network
- Predicting Patients with Diabetes at Risk of 30-day Emergency Readmission Using Supervised Machine Learning
How is the programme delivered?
The programme is delivered via face-to-face classes. Modules usually follow a lecture plus practical format.
How is the programme structured?
| Full-time Y1 | Part-time Y1 | PT Y2 | Modular Flexible (up to 5 years) Y1 | MF Y2 | MF Y3 |
|---|---|---|---|---|---|
| 8 Modules + DISS | 5 or 6 Modules | 2 or 3 optional modules + DISS | 4 Modules | 4 Modules | DISS |
| PHDS (C) | PHDS (C) | + 1 or 2 optional modules | PHDS (C) | RM (C) | DISS (C) |
| BSMS (C) | BSMS (C) | DISS (C) | BSMS (C) | + 3 optional modules | |
| SSDPHS (C) | SSDPHS (C) | SSDPHS (C) | |||
| DMHR (C) | DMHR (C) | DMHR (C) | |||
| RM (C) | RM (C) | OR: | |||
| + 3 optional modules | + 0 or 1 optional modules | Programming Modules | Statistics modules | ||
| DISS (C) | PHDS (C) | BSMS (C) | DISS | ||
| SDPHDS (C) | RM (C) | ||||
| MLHB (C) | DMHR (C) | ||||
| AMLH (optional) | ASA (optional) |
