2019/20 Taught Postgraduate Programme Catalogue

MSc Health Data Analytics (Part-Time)

Total credits: 180

Entry requirements:

A 1st degree in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components (at least 2:1). We also consider working experience (two years or more) of research in a quantitative subject area. Non-graduates who: have successfully completed three years of a UK medical degree; are normally ranked in the top 50% of the year 3 cohort; and wish to take the Health Data Analytics MSc as an intercalated programme, will also be accepted.

An overall score of 7.0 on IELTS (International English Language Testing System) with at least 6.0 in writing and no other skill below 6.5; from a TOEFL paper-based test the requirement is a minimum score of 600, with 4.5 in the Test of Written English (TWE); from a TOEFL computer-based test the requirement is a minimum score of 250, with 4.5 TWE; from a TOEFL Internet-based test the requirement is a minimum score of 100, with 25 in the "Writing Skills" score.

School/Unit responsible for the parenting of students and programme:

Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine

Examination board through which the programme will be considered:

School of Medicine, Postgraduate Programmes Assessment Board.

Programme specification:

The programme will:

Train scientists in the cutting-edge quantitative skills needed for health research; with the proficient expertise required to be able to work in a variety of fields related to health, inculcated with in-depth knowledge and nurtured in thinking that yields the ability to undertake robust scientific enquiry using health data of various kinds.
The programme will provide strong foundations in the skills and knowledge of data analytics with relevance to health; we will stretch students to acquire and implement advanced techniques through optional modules that will allow their learning to be tailored towards discipline-specific paths appropriate to their future planned career. At graduation, students will find themselves at the forefront of the discipline of health data analytics, with advanced knowledge and skills appropriate to all and any careers involving observational health data.

Distinctive features include:

- A focus on statistical methods for observational health and health services research;
- State-of-the-art training in predictive modelling;
- Cutting-edge training in causal inference modelling (unique for UK MSc programmes);
- Leading expert training in the pitfalls and malpractices of observational data analysis (unique for PGT programmes world-wide);
- Extensive access to practice and practice-derived datasets maintained within LIDA;
- Substantial scope for student choice across a range of optional modules to accommodate different interests and needs, including potential engagement with the health-orientated non-medical aspects of computing and geography (via modules and research projects);
- A compulsory generic and transferable skills module to prepare graduates for professional careers as independent researchers;
- Research projects using clinically-relevant data, supervised by research-active academics, leading to the production of journal papers suitable for publication;
- The use of blended learning to meet the differing learning styles of individual students, and to provide student paced-learning for those with different aptitudes for quantitative skills training.

Year1 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates will be required to study the following compulsory module:

Optional modules:

Candidates must study 45 credits from the following optional modules:

Year2 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates must study the following compulsory module:

Optional modules:

Candidates must study 45 credits from the following optional modules:

Last updated: 16/08/2019

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