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2023/24 Taught Postgraduate Module Catalogue

COMP5513M Workplace-based Data Science & Analytics Research and Development Project (Short Form)

105 creditsClass Size: 45

Module manager: Owen Johnson

Taught: 1 Feb to 31 Aug (19mth) View Timetable

Year running 2023/24

Pre-requisite qualifications

Academic entry requirements
A 1st degree in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components (at least 2:1). We also consider working experience (two years or more) of research in a quantitative subject area. Non-graduates who: have successfully completed three years of a UK medical degree; are normally ranked in the top 50% of the year 3 cohort; and wish to take the Health Data Analytics MSc as an intercalated programme, will also be accepted.
English language requirements
An overall score of 7.0 on IELTS (International English Language Testing System) with at least 6.0 in writing and no other skill below 6.5; from a TOEFL paper-based test the requirement is a minimum score of 600, with 4.5 in the Test of Written English (TWE); from a TOEFL computer-based test the requirement is a minimum score of 250, with 4.5 TWE; from a TOEFL Internet-based test the requirement is a minimum score of 100, with 25 in the "Writing Skills" score.


COMP5400MBio-Inspired Computing
COMP5510MData Science & Analytics for Causal Inference and Prediction
COMP5623MArtificial Intelligence
COMP5712MProgramming for Data Science

This module is not approved as an Elective

Module summary

The module is designed to support the development of independent and team science practical competencies in applied health data science research and innovation, within the context of real-world systems, challenges and opportunities. The short-form module will build upon the opportunities provided within the Programming for Data Science Module (COMP5712M – a pre-requisite for students attending the short-form of the workplace-based research and development project module) to develop the programming competencies relevant for research software engineering within applied data science contexts.


The aim of this module will be to support the development of independent and team science practical competencies in applied health data science research and innovation, within the context of real-world systems, challenges and opportunities.

Learning outcomes
Upon successful completion of the module the students will have demonstrated:
1. in-depth specialist knowledge related to: the topic, context, inherent (and applied) methods, and potential impact of a workplace-based research and innovation project – by completion of assessment (i), see below;
2. a sophisticated understanding of concepts at the cutting edge of health data science, including: mastery of appropriate computing, statistical and analytical techniques – by completion of assessments (ii), see below;
3. proficient intellectual and practical analytical skills relevant for: planning, management, design, implementation, delivery and dissemination of workplace-based projects – by completion of assessments (ii) and (iii), see below;
4. independent ideas and hypotheses, particularly concerning: potential lines of enquiry, alternative explanations and hypotheses, counterfactuals, and plausible biases and errors – by completion of assessment (iii), see below;
5. self-reflection with regard to the development of collegial, participative and professional relationships with peers, colleagues, trainers and hosts, including: a willingness to seek alternative perspectives, receive and deliver encouragement and constructive criticism, and uphold the principles of inclusive, open and engaged science – by completion of independent and supervised learning in a workplace-based context and of assessment (iv), see below; and
6. the ability to keep abreast of current issues, new developments and professional advances as these impact upon: technology, methods and health-related breakthroughs, and the additional learning needs involved – by completion of assessments (i), (ii) and (iv), see below.

Skills outcomes
Transferable professional skills related to team science and independent working, including: project planning; time management; and creative problem solving.


The focus and scope of each health data research project will be determined by students, supervisors and workplace hosts in order to match the capabilities, aptitudes, aspirations and interests of individual students to: existing and novel datasets; and challenges offering genuine value to the host department/service. A list of project opportunities available across host departments/services within health/healthcare partners will be compiled at the beginning of each academic year, with an outline describing potential inputs, outputs and outcomes.
In consultation with project supervisors and workplace hosts, students will be able to identify preferred projects (and, wherever possible, design and propose projects of their own design), with the allocation of projects undertaken by an interdisciplinary panel of supervisors.
Such projects will make use of existing sources of data (from statutory reports and indicators; surveys and audits; and management information systems) and/or novel sources of data (such as text-based notes and records; patient scans and images; audio/visual recordings; wearable technologies; and web-based interfaces).

Attainment of intended learning outcomes will be assessed using five separate assignments:
(i) a 2,000-word scoping review focussing on previous research, examining the topic/methods of the allocated research project(s) – 10%;
(ii) a 3,000-word protocol outlining the data sources, sampling, analytical design and interpretational approach for the allocated project – 10%;
(iii) a web-enabled summary of computer code/modelling/data extraction and analysis tools developed for application within the allocated project – 10%;
(iv) a 4,000-word dissertation, in the format of a publishable article, summarising the aims, conduct, findings and implications of the allocated project – 60%; and
(v) a 1,000-word reflective log of transferable skills development, focussing on professional learning and the benefits/challenges of team science – 10%.
The rationale for this strategy is to emphasise the importance of planning, evaluation, critical reflection and open science practices, and the limitations of advanced specialist skills in real-world health data science contexts.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Work Based Learning407.50300.00
Private study hours900.00
Total Contact hours300.00
Total hours (100hr per 10 credits)1,200.00

Private study

The module will run in the (extended – 12 month MRes) Semester 3 of both the first and second year part-time; in which it is expected that students will devote 0.2FTE during work and work-related private time on workplace-based research and development projects managed by project hosts at one of the programme’s health service partners (which includes NHS Digital) and under regular (weekly) academic supervision.

Opportunities for Formative Feedback

Weekly feedback from workplace hosts and academic supervisors; and feedback following the completion and assessment of successive formative assignments.

Methods of assessment

Assessment typeNotes% of formal assessment
Report2000-word scoping review10.00
Report3000-word project protocol10.00
Report4000-word journal article manuscript60.00
Reflective log1000-word reflection on transferable skills development10.00
PortfolioWeb-enabled computer code deposition10.00
Total percentage (Assessment Coursework)100.00

his module will be reassessed by the resubmission of the substantive component of work.

Reading list

There is no reading list for this module

Last updated: 26/09/2023


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