Health Data Analytics

The MSc in Health Data Analytics at the University of Leeds is a comprehensive postgraduate programme designed to equip students with the essential skills and knowledge required to analyze complex health data sets effectively. This course aims to develop expertise in data management, statistical analysis, and the application of digital health technologies to improve patient outcomes and healthcare systems. Students will gain a deep understanding of the principles of data collection, processing, and interpretation within various health contexts, including clinical settings, public health, and health policy. The programme combines theoretical foundations with practical applications, offering hands-on experience with industry-standard analytics tools and software, such as R, Python, and SQL.

Throughout the course, students explore topics like epidemiology, data visualization, machine learning, and health informatics, enabling them to extract meaningful insights from large datasets. The MSc emphasises the importance of ethical considerations, data privacy, and security in health data research. Collaboration and interdisciplinary working are central to the programme, preparing graduates to work effectively with healthcare professionals, policymakers, and researchers. The curriculum is designed to respond to the rapidly evolving landscape of digital health, ensuring that students are well-versed in current trends and innovations.

The programme benefits from the University of Leeds' strong links with the NHS, healthcare providers, and industry partners, offering students opportunities for real-world experience, networking, and potential employment after graduation. Assessment methods include coursework, projects, and examinations, with a final dissertation that consolidates learning and demonstrates applied research skills. Graduates of this MSc will be equipped to pursue careers in health data analysis, health informatics, healthcare consultancy, and research positions across public and private sectors. The programme prepares students not only for immediate employment but also for further academic study or research in health data sciences.

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.

Compulsory modules

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

Optional modules

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

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.
• TOEFL (Test of English as a Foreign Language) of 92 with no less than 21 in listening, 21 in reading, 23 in speaking and 22 in writing
• Pearson (Academic) of 64 overall with no less than 60 in any component
• Cambridge Advanced English (CAE), or C1 Advanced, of 176 overall with no less than 169 in any component
• Trinity College Integrated Skills in English of a Pass in ISE II or above (if taken in the UK)
• GCSE English Language or Cambridge IGCSE English as a First or Second Language at grade C

Want to improve your English level for admission?

Prepare for the program requirements with English Online by the British Council.

• ✔️ Flexible study schedule
• ✔️ Experienced teachers
• ✔️ Certificate upon completion

📘 Recommended for students with an IELTS level of 6.0 or below.

Enroll in the course

The University of Leeds offers a range of funding opportunities for students enrolled in the Health Data Analytics postgraduate programme. Prospective students are encouraged to explore various options including government loans, scholarships, bursaries, and employer sponsorships. For UK students, tuition fee loans are available through the Student Loans Company, which can cover the full cost of tuition fees. These loans typically have favorable repayment terms, with repayments commencing only when the graduate's income surpasses a certain threshold. International students are advised to seek scholarships or financial aid specific to their status, as funding options vary by domicile. The university provides several merit-based scholarships for high-achieving applicants, which are awarded automatically based on application data or through a competitive process. Additionally, the university’s scholarship schemes may include tuition fee discounts or partial fee waivers for eligible students. Students are also encouraged to explore external funding opportunities from governmental or private organizations in their home countries, which may provide grants or fellowships for postgraduate study. Some students may also secure funding through employer sponsorship, especially if the programme enhances their professional qualifications or aligns with organizational training initiatives. The university offers advice and support services to assist students in identifying suitable funding sources and completing application procedures. Furthermore, students enrolled in the programme can sometimes access part-time work opportunities on or near campus to help offset living expenses. It is important for applicants to review the specific eligibility criteria, application deadlines, and funding amounts for each scholarship or loan scheme, as these can vary from year to year. While specific financing details for the Health Data Analytics course are not publicly detailed on the university website, the institution’s overall postgraduate funding policies generally adhere to the above framework. Students are advised to regularly consult the university’s official financial aid page and contact the university’s admissions or student finance offices for personalized guidance. Up-to-date information on funding opportunities, deadlines, and application procedures can typically be obtained prior to the start of the academic cycle to ensure timely submission of applications.

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.

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. These include:

• informal discussion with tutors
• written feedback from formative assessments
• marks obtained in both formative and summative assessments
• peer-review from presenting projects and data.

Each module contains a summative assessment component. 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.