2024/25 Taught Postgraduate Programme Catalogue
MRes Data Science and Analytics for Health (Part time)
Programme code: | MOR-DS&AH-PT | UCAS code: | |
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Duration: | 24 Months | Method of Attendance: | Part Time |
Programme manager: | Owen Johnson | Contact address: | O.A.Johnson@leeds.ac.uk |
Total credits: 180
Entry requirements:
Either a 1st class degree at bachelor or Masters level Or 2:1 (hons) plus (minimum 3 years) first‐hand work‐related experience in one or more quantitative science or healthcare settings.
A pass at GCSE level English Language (grade B or above) or equivalent.
For students whose first language is not English, an English language qualification at a suitable level: IELTS 6.5 or equivalent with no lower than 6.5 in each category.
School/Unit responsible for the parenting of students and programme:
School of Computing
Examination board through which the programme will be considered:
School of Computing
Programme specification:
The programme provides a comprehensive training in the management, modelling and interpretation of the
increasing amounts of health and healthcare data that are becoming available from a diverse range of clinical,
behavioural and organisational sources – skills that will enable students 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 programme 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 students to develop both the technical and applied skills required for
addressing real‐world challenges in real‐world health and healthcare contexts.
A distinctive feature of the programme 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 under the co‐supervision of service‐specific specialists and academic experts in the management,
analysis and interpretation of health and healthcare data. These projects offer students opportunities to:
apply, test and further refine the skills the MRes will provide 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; and generate analytical tools,
empirical findings, and evidence‐based insights with the potential to have tangible impacts on health and
healthcare policy and practice.
The programme draws together: (i) 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; and (ii) 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
commercialisation.
Year1 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
Compulsory modules:
COMP5122M | Data Science | 15 credits | Semester 1 (Sep to Jan) | |
COMP5513M | Workplace-based Data Science & Analytics Research and Development Project (Short Form) | 105 credits | 1 Feb to 31 Aug (19mth) | |
COMP5611M | Machine Learning | 15 credits | Semester 2 (Jan to Jun) | |
COMP5712M | Programming for Data Science | 15 credits | Semester 1 (Sep to Jan) |
Optional modules:
Students must study the following compulsory modules:
Year2 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
Compulsory modules:
Optional modules:
Students to continue with Project module chosen in Year 1:
Candidates will be required to study 30 credits from the following optional modules:
COMP5625M | Deep Learning | 15 credits | Semester 2 (Jan to Jun) | |
COMP5840M | Data Mining and Text Analytics | 15 credits | Semester 2 (Jan to Jun) | |
LUBS5308M | Business Analytics and Decision Science | 15 credits | Semester 1 (Sep to Jan) | |
LUBS5980M | Innovation Management in Practice | 15 credits | Semester 2 (Jan to Jun) | |
MATH5743M | Statistical Learning | 15 credits | Semester 2 (Jan to Jun) | |
MATH5820M | Bayesian Statistics and Causality | 15 credits | Not running in 202425 | |
YCHI5075M | Spatial Analytics and Visualisation for Health | 15 credits | Not running in 202425 | |
YCHI5082M | Foundations of Health Data | 15 credits | 01 Oct to 31 Dec | |
YCHI5083M | Human Factors in Health Data Science | 15 credits | 1 Mar to 31 May | |
YCHI5084M | Visualisation for Health Data | 15 credits | 1 Dec to 31 Jan | |
YCHI5087M | Artificial Intelligence and Machine Learning in Health | 15 credits | Semester 2 (Jan to Jun) |
Last updated: 04/09/2024 16:53:52
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