Postgraduate Studentships - Search for funding opportunities.
Postgraduate Studentships - Search for funding opportunities.
Study Details: 1-2 Years
The field of health data science is evolving, from AI-assisted diagnosis to large-scale epidemiological modelling. At Sussex, you’ll develop your analytical, computational and problem-solving skills so you can address pressing challenges in public health.
The NHS and global health organisations face rising demand for expertise in health analytics. They rely on data-driven decision-making. On this MSc you’ll learn:
the core principles of data science and machine learning methods with applications to health
about clinical research, epidemiology and health systems
about managing, analysing and interpreting health data, including electronic health records and clinical imaging
to translate data into actionable insights for healthcare professionals and policymakers
to use data to help improve patient care, optimise services and manage public health risks.
This innovative course is for you if you want to specialise in data science for clinical and health applications.
Sussex has a strong tradition in interdisciplinary research. Our Departments of Mathematics, Life Sciences and Informatics and the Brighton and Sussex Medical School have been at the forefront of innovative research in statistical modelling.
We’ll connect you with our NHS and public health partners through guest lectures, case studies and project opportunities. You’ll also receive guidance on employment pathways, networking and skills development.
You’ll graduate ready for roles across healthcare, biomedicine, public health and health technology.
You should have an upper second-class (2.1) undergraduate honours degree or above. Your qualification should be in physics, engineering, science, computing, mathematics or life sciences. You may also be considered for the course if you have other professional qualifications or experience of equivalent standing.
To support career readiness, dedicated career sessions form part of your course. These sessions offer you advice on employment pathways, health-sector job trends and strategies for effective professional development.
An industry advisory board, composed of professionals from the NHS, public health and health technology sectors will:
share updates on developments in health data science
recommend enhancements to ensure course content remains relevant
facilitate networking opportunities with employers and mentors.
Flexible learning pathways, our research excellence and embedded career development will prepare you to make meaningful contributions to healthcare and public health.
We’ll help you engage with NHS professionals, clinical researchers and public health experts. This experience will help you appreciate the realities of working with health data and translating research into practice.
Core modules are taken by all students on the course. They give you a solid grounding in your chosen subject and prepare you to explore the topics that interest you most.
Autumn teaching
Clinical Trials Management
Data Science Research Methods Autumn
Linear Statistical Models
Spring teaching
Data Science Masters Research Proposal
Evidence-Based Practice
Machine Learning and Statistics for Health
Wider Topics in Data Science
Spring and Summer teaching
Dissertation in Data Science for Health
Options
Alongside your core modules, you can choose options to broaden your horizons and tailor your course to your interests. This list gives you a flavour of our options, which are kept under review and may change, for example in response to student feedback or the latest research.
While it’s our aim for students to take their preferred combinations of options, this can’t be guaranteed and will be subject to timetabling. Options may be grouped and if so, students will be able to choose a set number of options from the selection available in any particular group.
Autumn teaching
Algorithmic Approaches to Mathematics
Algorithmic Data Science
Imaging in Brain Diseases
Introduction to Mathematical Biology
Spring teaching
Machine Learning
Monte Carlo Simulations
Statistical Inference
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