Data Science and Analytics for Health

Our Data Science and Analytics for Health MRes degree provides a comprehensive training in the management, modelling and interpretation of health and healthcare data used by clinical, behavioural and organisational sources. These skills will enable you to extract valuable empirical evidence to better understand the causes of disease, and more accurately predict and evaluate health outcomes and health service needs.

The course draws on recent advances in information technology, data management, statistical modelling (for description/classification, causal inference and prediction), machine learning and artificial intelligence. It intends to equip health data scientists and health data analysts with the skills required to: harness the empirical insights available within large and varied data sources; and apply these to pressing clinical, social and organisational questions within the broad and varied context of health and healthcare services.

The programme is designed to enable you to develop both the technical and applied skills required for addressing real‐world challenges in real‐world health and healthcare contexts.

This course draws together:

  • established expertise in applied data science relevant to the statistical modelling of complex data and the use of machine learning and artificial intelligence to accelerate the application of modelling for insight and discovery through causal inference and prediction
  • key public and private sector partners with extensive experience of managing a range of complex health and healthcare data sources, and harnessing these to inform professional practice, service delivery, public policy and commercialization.

Course content

A distinctive feature of this course is the inclusion of extended periods of hands‐on data science practice working on applied and collaborative workplace‐based projects across a range of health and healthcare services. You will be under the supervision of service‐specific specialists and academic experts in the management, analysis and interpretation of health and healthcare data.

These projects will offer you the opportunities to:

  • apply, test and further refine your skills in data science and analytics
  • experience working within established data science teams addressing pressing and pertinent health and healthcare problems
  • develop invaluable transferable skills relevant to interdisciplinary team science
  • generate analytical tools, empirical findings, and evidence‐based insights with the potential to have tangible impacts on health and healthcare policy and practice.

Want to find out more about your modules?

Year 1

Compulsory modules

  • Data Science & Analytics for Causal Inference and Prediction15 credits
  • Principles of Data Science & Analytics15 credits
  • Machine Learning15 credits
  • Artificial Intelligence15 credits

Optional modules (selection of typical options shown below)

  • Workplace-based Data Science & Analytics Research and Development Project (Long Form)120 credits
  • Workplace-based Data Science & Analytics Research and Development Project (Short Form)105 credits
  • Programming for Data Science15 credits

Learning and teaching

Campus‐based blended learning with workplace‐based research project supervision.

On this course you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.

Entry requirements

A bachelor degree with a 2:1 (hons) in computer science, science, technology, engineering, mathematics or a quantitative health discipline; or equivalent first‐hand work‐related experience in one or more quantitative science or healthcare settings assessed through APEL.

Fees

  • UK: £11,250
  • International: £25,500

Career opportunities

On completion of this course, you will be strongly positioned to enter an exciting and rewarding career path in one of at least three main areas:

  • as skilled data science researchers in research‐intensive settings (including academia) – with good research funding prospects and substantial potential for societal and economic impacts arising out of the outputs from your applied, workplace‐based health data science projects;
  • as health and healthcare data science entrepreneurs – developing business ideas based on the application of your advanced data science skills in extended workplace‐based research projects within the health domain; and
  • as key research and development staff within public, private/commercial or voluntary sector organisations – generating and capitalising upon the novel insights and discoveries accessed through the application of advanced data science techniques to rapidly expanding clinical, behavioural and operational data sets.

 

Course Website  
( https://courses.leeds.ac.uk/i927/data-science-and-analytics-for-health-mres )

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