Module code: COMM070

Module Overview

The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.

Module provider

Computer Science and Electronic Eng

Module Leader


Number of Credits: 60

ECTS Credits: 30

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 588

Lecture Hours: 2

Tutorial Hours: 10

Module Availability

Crosses academic years

Prerequisites / Co-requisites

Some project titles may require the student to have taken specific modules from the MSc programme.

Module content

The dissertation is the result of an expected 600 hrs of work. Most of this is done individually by the student, in locating and reading relevant sources, working on the technical contribution that is the main part of the dissertation, and writing up the final report. Some time is also spent in regular discussions with the supervisor. Further details are given in the module handbook.

Assessment pattern

Assessment type Unit of assessment Weighting
Project (Group/Individual/Dissertation) INDIVIDUAL REPORT 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.

Thus, the summative assessment for this module consists of:

  • Grades for the final report against previously published assessment criteria.

Final submission is tentatively due at the end of Summer.

Formative assessment and feedback

Project Synopsis: Late in Semester 2 the project Synopsis will be submitted for feedback. This document should contain: Main report

Introduction to problem, aims / objectives (half page)

Literature review / background (1 page)

Technical overview (1 page)

Workplan, including risks and timeline (half page)

References (doesn't count towards the page limit) - as many as you need Draft Report: In the middle of the Summer term the current state of the written report including planned table of contents will be submitted for feedback.

Formative feedback is also given by the supervisor during regular meetings.

Module aims

  • Provide an opportunity for students to pursue a single topic in depth and to demonstrate evidence of research ability at a Masters level. The topic is typically a current problem in the broad area of the MSc Data Science. Students are encouraged to either research a new concept or apply existing technology to a new field

Learning outcomes

Attributes Developed
001 Be able to approach an open-ended topic, to research new ideas and experiment with new technologies. CK
002 Be able to locate, select, and interpret sources relevant to their topic. KT
003 Integrate and build upon the concepts, theories, and knowledge gained in the taught component of their MSc programme. CK
004 Demonstrate their command of the subject matter of their dissertation in a written report. CK
005 Demonstrate independent, critical and analytical skills, and an ability to evaluate evidencThe exact nature of this depends on the topic of their dissertation, and is typically demonstrated by: CKP
006 For software development projects: a justification of the software design, algorithms, and development methodology chosen, and a critical evaluation of the final solution and its comprehensive testing CP
007 For experimental research: a justification of the experiments to be performed and the experimental methodology used, and a critical evaluation of the results obtained CPT
008 For empirical analysis: a justification of the data to be collected and the methodology used, and a critical evaluation of the results obtained CPT
009 For theoretical research of a mathematical / technical nature: complete proofs and derivations, as necessary, for a novel problem. CK

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.

The learning and teaching methods include: - Regular meetings with the allocated supervisor to discuss progress. 

Lecture on research methods 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: COMM070

Other information

Digital Capabilities On this module, students learn to take a large-scale technical or research project from conception through to implementation and evaluation. This requires excellent technical skills that bring in aspects from the other modules on the programme to incorporate either security (Information Security programme) or data science and AI (Data Science programme) into their work. Students use their knowledge from previous modules in their topic area to engineer a solution to a complex problem or solve a technical research question. Employability This module provides students with technical skills alongside a range of transferrable skills by developing their own solution to a complex problem. A crucial key to success in this module is good project management skills and organisation, and the ability to develop a project management plan and follow this through. This large-scale project module provides students with experience of working on a large scale, complex piece of software or complex research problem. The resulting solution can be used as a portfolio piece to advertise a student¿s development experience to employers. . Global and Cultural Skills Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to develop applications with global reach and collaborate with their peers around the world. Resourcefulness and Resilience This module requires that a student take an idea from conception, through to specification and design, implementation and then critical evaluation. This large-scale project requires excellent technical skills, but also excellent project management and planning. Students will inevitably encounter obstacles during the development of their project, requiring them to be resourceful, in order to find solutions or alternative approaches to circumvent the obstacle. Students also require resilience to persist in the face of failure, until a viable path forward for the project is developed. The experience gained in the module will be immensely valuable when planning and implementing future large-scale projects. Sustainability The project is directed by the student primarily, and there are many staff with suitable expertise to supervise projects related to sustainability. Students therefore have the option of working on a project directly related to sustainability. For example, some projects in data science work on more efficient machine learning models that can be trained with fewer computations and a smaller memory requirement, thereby reducing power consumption. Some students work with optimisation techniques that can directly be applied to problems of reducing energy consumption, or improving the efficiency of a process with a fixed energy budget. Some students work directly on the problem of understanding ecological networks and food chains, or more efficient management of traffic flow.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Data Science MSc Cross Year Core A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.