MRes Data Science and Analytics for Health (Part time)

Year 1

(Award available for year: Postgraduate Certificate)

Learning outcomes

On completion of the year/programme students should have provided evidence of being able to:
- demonstrate extensive knowledge and knowledge of data science modelling techniques for prediction and causal
inference relevant to health and healthcare services;
- demonstrate substantial understanding of the scientific concepts, data source contexts and analytical techniques
required to harness discovery and insight from complex multimodal data;
- demonstrate competence across a range 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 understanding of the role of ongoing developments in their knowledge, skills and expertise based on
advanced scholarship and research-derived evidence;
- demonstrate reflective practice when working with others, and developing professional interdisciplinary team
science relationships and related practices;
- demonstrate capability in the application and (re)formulation of existing and novel ideas, and related hypotheses,
and in the skills required to design, implement and manage effective plans to evaluate and learn from these; and
- demonstrate an appreciation of the importance of continually, critically and creatively evaluating current issues,
research and advanced scholarship, and emerging opportunities and developments in health and healthcare data
science.

Transferable (key) skills

- the skills necessary to plan and contribute to the delivery of applied research projects, including those undertaken
during employment as a team member within 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 policy;
- the skills necessary to reflect upon, set objectives for, learn lessons from and identify achievement in their own
knowledge, skills, practices and performance;
- the skills necessary to contribute their attention, effort and decisions in an effective and efficient fashion as a
member of a team tasked with addressing existing and new challenges in situations that are complex; and
- the skills necessary to critically engage with emerging inter-disciplinary, inter-professional and trans-sectoral
opportunities, organisations, boundaries and practices.

Assessment

- demonstrating and evidencing the knowledge, ability and aptitude required to act as a key member of teams
undertaking 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 identify both a breadth and depth of
expertise – and a range of alternative perspectives and theories – to examine 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 and sensitively
explore the potential strengths and weaknesses of received opinion; and
- demonstrating and evidencing the knowledge, ability, expertise and confidence to make reasoned judgements
that include explicit reference to, and sensitively balance, potential limitations or risks associated with identifiable
errors, biases, inaccuracy and imprecision including in the absence of definitive evidence or knowledge.