Module and Programme Catalogue

Search site

Find information on

2021/22 Taught Postgraduate Programme Catalogue

MRes Data Science and Analytics for Health (Part time)

Programme code:MOR-DS&AH-PTUCAS code:
Duration:24 Months Method of Attendance: Part Time

Total credits:

Entry requirements:

An honours degree equivalent to a UK first/high upper second class, in a Science, Technology, Engineering, Mathematics or quantitative Health discipline; or equivalent first‐hand work‐related experience in one or more quantitative science or health setting assessed through APEL.
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:

COMP5611MMachine Learning15 creditsSemester 2 (Jan to Jun), 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)
COMP5623MArtificial Intelligence15 creditsSemester 2 (Jan to Jun)

Optional modules:

COMP5512MWorkplace-based Data Science & Analytics Research and Development Project (Long Form)120 credits1 Feb to 31 Aug (19mth)
COMP5513MWorkplace-based Data Science & Analytics Research and Development Project (Short Form)105 credits1 Feb to 31 Aug (19mth)
COMP5712MProgramming for Data Science15 credits1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)


Year2 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

COMP5510MData Science & Analytics for Causal Inference and Prediction15 credits1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan)
COMP5511MPrinciples of Data Science & Analytics15 creditsNot running in 202122

Optional modules:

Students to continue with Project module chosen in Year 1,either:

COMP5512MWorkplace-based Data Science & Analytics Research and Development Project (Long Form)120 credits1 Feb to 31 Aug (19mth)
COMP5513MWorkplace-based Data Science & Analytics Research and Development Project (Short Form)105 credits1 Feb to 31 Aug (19mth)

Last updated: 26/10/2021 08:52:23

Disclaimer

Browse Other Catalogues

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD

© Copyright Leeds 2019