MRes Data Science and Analytics for Health (Part time)

Programme overview

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.

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.

Year 1

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

Compulsory Modules

Code	Title	Credits	Semester	Pass for Progression
COMP5122M	Data Science	15	Semester 1 (Sep to Jan)
COMP5513M	Workplace-based Data Science & Analytics Research and Development Project (Short Form)	105	1 Feb to 31 Aug (19mth)
COMP5611M	Machine Learning	15	Semester 2 (Jan to Jun)
COMP5712M	Programming for Data Science	15	Semester 1 (Sep to Jan)

Optional Modules

Students must study the following compulsory modules:

Year 2

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

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:

Code	Title	Credits	Semester	Pass for Progression
COMP5625M	Deep Learning	15	Semester 2 (Jan to Jun)
COMP5840M	Data Mining and Text Analytics	15	Semester 2 (Jan to Jun)
LUBS5308M	Business Analytics and Decision Science	15	Semester 1 (Sep to Jan)
LUBS5980M	Innovation Management in Practice	15	Semester 2 (Jan to Jun)
MATH5743M	Statistical Learning	15	Semester 2 (Jan to Jun)
MATH5820M	Bayesian Statistics and Causality	15	Not running in 202425
YCHI5075M	Spatial Analytics and Visualisation for Health	15	Not running in 202425
YCHI5082M	Foundations of Health Data	15	01 Oct to 31 Dec
YCHI5083M	Human Factors in Health Data Science	15	1 Mar to 31 May
YCHI5084M	Visualisation for Health Data	15	1 Dec to 31 Jan
YCHI5087M	Artificial Intelligence and Machine Learning in Health	15	Semester 2 (Jan to Jun)

Last updated: 19/09/2024 16:36:14

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