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Health Data Analytics MSc

Key information

Qualification type

MSc - Master of Science

Subject areas

Health Studies

Course type


Course Summary


Our Health Data Analytics course has been created to train a new generation of leaders in Health Data Science, Biostatistics, Medical Statistics, and Epidemiology. It will prepare you with the specialist skills needed for analysing and interpreting ‘big data’ in the settings of population health and healthcare delivery.

The course is delivered by expert staff at the Leeds Institute for Data Analytics (LIDA) and is unique in the UK for specialising in the analysis of real-world health data for causal inference. LIDA is an internationally recognised centre for data science and recently partnered with the Alan Turing Institute, the UK's national institute for data science and artificial intelligence. Module lecturers Prof Mark Gilthorpe and Dr Peter Tennant are themselves Fellows of the Alan Turing Institute, in recognition of their world-leading contributions to health data science.

This unique MSc offers an exciting blend of core and optional content to provide a cutting-edge grounding in modern health data science while allowing you to specialise in a range of areas, such as clinical trials, machine learning, spatial analytics, and genetic epidemiology.

Our innovative Professional Skills for Health Data Analysts module will equip you with the skills and experience to work effectively in research, public health or health services research. It covers; ethics, academic writing for publication, consultancy, management and leadership skills.

The course will also provide you with strong foundations in the skills and knowledge of data analytics with relevance to health. We stretch you to acquire and implement advanced techniques through optional modules. These modules allow your learning to be tailored towards discipline-specific paths appropriate to your future career.

Full time MSc students will study modules totalling 180 credits over 12 months. If you study part time you will study fewer modules in each year.

Learning and teaching

You’ll have access to the very best learning resources and academic support during your studies. We’ve been awarded a Gold rating in the Teaching Excellence Framework (TEF, 2017), demonstrating our commitment to delivering consistently outstanding teaching, learning and outcomes for our students.

We mix face-to-face teaching with technology to enhance your learning experience. Self-directed online learning lets you study at a pace that suits you, whilst face-to-face support allows you to explore individual areas of difficulty and extend your understanding.

Career opportunities

Our course is designed for recent graduates with an interest in health data science including those seeking a career in quantitative health research, either within industry, the public sector, or academia, or advanced data analytics within a healthcare or health intelligence setting. Upon graduating you will be at the forefront of the discipline of health data science and have advanced knowledge and skills appropriate to a range of careers involving the analysis and interpretation of ‘real world’ health data.

Different course options

Full time | University of Leeds | 12 months | SEP-20

Study mode

Full time


12 months

Start date



Professional Skills for Health Data Scientists (15 Credits) - Core
Statistical Theory and Methods (15 Credits) - Core
Modelling Strategies for Causal Inference with Observational Data (15 Credits) - Core
Further techniques in Health Data Analytics (15 Credits) - Core
Modelling Prediction and Causality with Observational Data (15 Credits) - Core
Introduction to Health Data Science (15 Credits) - Core
Research Project (60 Credits) - Core

Tuition fees

UK fees
Course fees for UK / EU students

Please contact university and ask about this fee

Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

Please contact university and ask about this fee

Average for all Postgrad courses (per year)


Entry requirements

A bachelor degree with a 2:1 (hons) or equivalent qualification in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components. We also consider working experience (two years or more) of research in a quantitative subject area. English language requirements IELTS 7.0 overall, with no less than 6.0 in writing and 6.5 in all other components.