MSc Health Data Analytics
Start date September 2018
Duration/mode 12 months full time, 24 months part time
UK/EU fees £9,250 (total)
International fees £21,500 (total)
Entry requirements A bachelor degree with a 2:1 (hons) in a relevant subject.
IELTS 7.0 overall, with no less than 6.0 in writing and 6.5 in all other components
There is an established need for advanced analytical training in population health research, as explicitly stated by most funding bodies in medicine and in other disciplines. The emergence of ‘Big Data’ has focussed the minds of many on appropriate data analytic skills training. This programme which is a development from the MSc Epidemiology and Biostatistics is the only taught postgraduate programme in the UK that specialises in the analysis of observational studies, routine healthcare data, and that adopts a focus on causal inference.
The programme offers intensive training in data analytic techniques tailored to the needs of career enhancers and career changers with a focus on health. It can be studied full time over 12 months or part time over 24 months.
For more details contact:
- Our admissions team at firstname.lastname@example.org or +44 (0)113 343 7646
- Programme Leader: Dr Richard Feltbower, R.G.Feltbower@leeds.ac.uk
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.
You will take compulsory modules, including our innovative Professional Skills for Health Data Analysts module, designed to give you the skills and experience to work effectively in research, public health or health services research. It includes, for example, ethics, academic writing for publication, consultancy, management and leadership skills.
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, together 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 Leeds Institute of Data Analytics (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.
These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
Learning and teaching
We blend 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.
You’re likely to experience:
- small-group teaching with an expert in the field, including some modules with the opportunity to mix with students from other disciplines;
- teaching in computer clusters to help you rapidly gain the skills required with statistical packages;
- online workbooks with relevant links for further research;
- online audio-visual presentations (vodcasts);
- online help files and sample data sets with worked examples, which support all the statistical packages;
- experiential learning as part of the research team for your research project;
- continuous formative and summative assessment, and feedback.
We understand the importance of assessment and feedback in your learning. We provide assessment in as many modules as possible so that you can gauge your understanding of the key concepts.
You’ll get feedback in a variety of ways: through informal discussion with tutors, written feedback from formative assessments, marks obtained in both formative and summative assessments and peer-review from presenting projects and data.
Each module contains a summative assessment component (a more formal evaluation). Some of these will be done via continuous in-course assessment, and some as end-of-module assessment.
Our assessment and feedback will use a number of methods:
- Online assessment which allows a flexible set of responses, marks the assessment immediately and provides both results and more structured feedback;
- Short answer questions to test understanding of more complex methods and scenarios;
- Project reports that allow deeper exploration of a topic;
- Other methods to fit the skills and knowledge under test, eg presentation of data;
- For the overall research project, regular meetings with your supervisor to monitor your progress and give feedback.
Applying, fees and funding
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.
English language requirements
IELTS 7.0 overall, with no less than 6.0 in writing and 6.5 in all other components. For other English qualifications, read English language equivalent qualifications.
International students who do not meet the English language requirements for the programme may be able to study an English for Academic Purposes pre-sessional course with a progression route to the degree programme. For information and entry requirements, read Pre-sessional programmes.
How to applyApply (Full time)
Apply (Part time)
This link takes you to information on applying for taught programmes and to the University's online application system.
If you're unsure about the application process, contact the admissions team for help.
UK/EU fees: £9,250 (total)
International fees: £21,500 (total)
Read more about paying fees and charges.
For fees information for international taught postgraduate students, read Masters fees.
Part-time fees are normally calculated based on the number of credits you study in a year compared to the equivalent full-time course. For example, if you study half the course credits in a year, you will pay half the full-time course fees for that year.
Scholarships and financial support
If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government. Find out more at Masters funding overview.Back to top
There is demand for students and academic staff with excellent statistical skills, an enquiring attitude, and a broader understanding of the research environment. Our graduates are attractive to employers in academic research, health and social care and industry. Prospective employers value their technical expertise and research understanding and the professional skills gained, including writing a grant application and a paper of submission quality.
We encourage you to prepare for your career from day one. Thats one of the reasons Leeds graduates are so sought after by employers.
The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.Back to top