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MRes Data Science and Analytics for Health

Year 1

(Award available for year: Master of Research)

Learning outcomes

On completion of the year/programme students should have provided evidence of being able to:
- demonstrate in-depth, specialist knowledge and mastery of data science modelling techniques, and their
application using machine learning and artificial intelligence, for description/classification, causal inference and
prediction relevant to health and healthcare services;
- demonstrate a sophisticated understanding of the scientific concepts, data source contexts and analytical
techniques required to harness discovery and insight from complex multimodal data;
- demonstrate mastery of generic and subject-specific intellectual abilities and transferable skills, particularly as
these relate to identifying, explicating, managing and communicating the risk of error, bias and inaccuracy in data
science analytics;
- demonstrate comprehensive understanding of, aptitude towards, and commitment to the continuing development
of their knowledge, skills and expertise based on advanced scholarship and research-derived evidence;
- demonstrate a proactive and self-reflective role in working with others and developing professional
interdisciplinary team science relationships and related practices with others;
- demonstrate capability in the proactive formulation of ideas and related hypotheses, and in the skills required to
design, implement and manage effective plans to evaluate and learn from these; and
- demonstrate a commitment to the continual, critical and creative evaluation of current issues, research and
scholarship, and to emerging opportunities and developments in health and healthcare data science.

Transferable (key) skills

Masters students will have had the opportunity to acquire the following abilities, as defined in the modules
specified for the programme:
- the skills necessary to undertake a higher research degree or for employment in a higher, independent capacity in
an area of applied data science practice within public, private and voluntary sector organisations where health and
healthcare data analytics have the capacity to strengthen understanding, identify innovative practices and improve
- the skills necessary to reflect upon, set objectives for, learn lessons from, and identify achievement in, their own
knowledge, skills, practices and performance, and that of others, and thereby undertake and support continuing
professional development;
- the skills necessary to independently focus their attention, effort and decisions in an effective and efficient fashion
to address existing and novel challenges in situations that are complex, unpredictable, or both; and
- the skills necessary to pro-actively and critically engage in the development of emerging inter-disciplinary, interprofessional and trans-sectoral opportunities, organisations, boundaries and practices.


Achievement for the degree of Master will be assessed by a variety of methods in accordance with the
learning outcomes of the modules specified for the year/programme and will include:
- demonstrating and evidencing the knowledge, ability and aptitude required to conduct independent, in-depth
enquiries into the knowledge base, methodological practices and translational activities of data science and
analytics as applied to health and healthcare services;
- demonstrating and evidencing the knowledge, ability and aptitude required to harness a breadth and depth of
expertise - and a range of alternative perspectives and theories - to both established and novel issues of variable
complexity within data science and analytics for health and healthcare services;
- demonstrating and evidencing the ability, aptitude and willingness required to critically evaluate, sensitively
challenge and robustly expose the strengths and weaknesses of received opinion; and
- demonstrating and evidencing the knowledge, ability, expertise and confidence to make reasoned judgements
that include explicit reference, and carefully balance, any potential limitations or risks associated with identifiable
errors, biases, inaccuracy and imprecision, including in the absence of definitive evidence or knowledge.


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