Health Data Analytics

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.

Course structure

These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.

Modules Year 1

Compulsory modules

• Research Project 60 credits
• Introduction to Health Data Science 15 credits
• Statistical Inference for Health Data 15 credits
• Modelling Prediction and Causality with Observational Data 15 credits
• Further techniques in Health Data Analytics 15 credits
• Professional Skills for Health Data Analysts 15 credits
• Modelling Strategies for Causal Inference with Observational Data 15 credits

Optional modules

You will need to study 30 credits from the following optional modules.

• Introduction to Clinical Trials 15 credits
• Introduction to Genetic Epidemiology 15 credits
• Latent Variable Methods 15 credits
• Independent Skills in Health Data Analytics 15 credits
• The Legal, Ethical and Professional Considerations in Healthcare Data Research 15 credits
• Machine Learning for Health Data 15 credits
• Spatial Analytics and Visualisation for Health 15 credits

For more information on typical modules, read Health Data Analytics MSc Full Time in the course catalogue

For more information on typical modules, read Health Data Analytics MSc Part Time in the course catalogue

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.

Assessment

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.