MRes Data Science and Analytics for Health (Part time)
Year 2
(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 theirapplication using machine learning and artificial intelligence, for description/classification, causal inference andprediction relevant to health and healthcare services;- demonstrate a sophisticated understanding of the scientific concepts, data source contexts and analyticaltechniques required to harness discovery and insight from complex multimodal data;- demonstrate mastery of generic and subject-specific intellectual abilities and transferable skills, particularly asthese relate to identifying, explicating, managing and communicating the risk of error, bias and inaccuracy in datascience analytics;- demonstrate comprehensive understanding of, aptitude towards, and commitment to the continuing developmentof 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 professionalinterdisciplinary team science relationships and related practices with others;- demonstrate capability in the proactive formulation of ideas and related hypotheses, and in the skills required todesign, 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 andscholarship, and to emerging opportunities and developments in health and healthcare data science.
Transferable (key) skills
- the skills necessary to undertake a higher research degree or for employment in a higher, independent capacity inan area of applied data science practice within public, private and voluntary sector organisations where health andhealthcare data analytics have the capacity to strengthen understanding, identify innovative practices and improvepolicy;- the skills necessary to reflect upon, set objectives for, learn lessons from, and identify achievement in, their ownknowledge, skills, practices and performance, and that of others, and thereby undertake and support continuingprofessional development;- the skills necessary to independently focus their attention, effort and decisions in an effective and efficient fashionto 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.
Assessment
- demonstrating and evidencing the knowledge, ability and aptitude required to conduct independent, in-depthenquiries into the knowledge base, methodological practices and translational activities of data science andanalytics as applied to health and healthcare services;- demonstrating and evidencing the knowledge, ability and aptitude required to harness a breadth and depth ofexpertise – and a range of alternative perspectives and theories – to both established and novel issues of variablecomplexity within data science and analytics for health and healthcare services;- demonstrating and evidencing the ability, aptitude and willingness required to critically evaluate, sensitivelychallenge and robustly expose the strengths and weaknesses of received opinion; and- demonstrating and evidencing the knowledge, ability, expertise and confidence to make reasoned judgementsthat include explicit reference, and carefully balance, any potential limitations or risks associated with identifiableerrors, biases, inaccuracy and imprecision, including in the absence of definitive evidence or knowledge.