• Inactive for Year: 2026/27

• Module Leader(s): Dr Stephen McGough
• Co-Module Leader: Dr Pete Philipson
• Other Staff: Mr Graham Cole, Professor Barry Hodgson

• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

This module develops your ability to apply knowledge, methods, and skills from across the programme within a substantial collaborative project in data science and artificial intelligence. Working in small groups, you will investigate a challenging real-world, industrially informed, or research-informed problem or dataset using appropriate analytical, computational, statistical, and AI-based approaches.

You will contribute to collaborative project scoping, planning, design, and development activities while also undertaking an independent element demonstrating individual analysis, technical implementation, evaluation, and critical reflection in relation to wider project objectives.

The emphasis is on teamwork, problem solving, project management, technical communication, and the critical and responsible application of data science and artificial intelligence methods within realistic and complex environments.

Outline Of Syllabus

Projects will address challenging real-world, industrially informed, or research-informed problems or datasets that can be investigated using methods and techniques drawn from across the degree programme in artificial intelligence, data science, statistics, and computing.

Each project will include a practical component and a substantial design, programming, analytical, modelling, or evaluation element, requiring students to integrate and apply knowledge and skills developed throughout their studies.

Topics covered by this module include:

Project scoping and problem definition
Collaborative investigation and project planning
Application and integration of AI, data science, statistical, and computational techniques
User, client, or stakeholder requirements and engagement
Data analysis, modelling, software development, and technical implementation
Evaluation, testing, validation, and interpretation of results
Ethical, legal, professional, and responsible innovation considerations
Communication and dissemination of technical outcomes to specialist and non-specialist audiences
Independent investigation and development within the context of a collaborative group project

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

This module includes an encounter with the leading edge of research and industry.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams

Other Assessment

Assessment Rationale And Relationship

The assessment structure comprises a combination of collaborative and independent assessment designed to evaluate students’ ability to apply knowledge and skills developed across the degree programme to a substantial AI and data science project.

The group report provides an opportunity for students to collaboratively scope, analyse, and plan an approach to a challenging real-world or realistic problem, demonstrating the selection and application of appropriate analytical, computational, statistical, data science, and AI-based methods. The report also assesses students’ ability to communicate technical work in a structured written format. (MLO1, MLO2, MLO3, MLO4)

The group presentation enables students to present and justify their project aims, proposed approaches, technical decisions, and planned outcomes to an audience. The presentation assesses collaborative working, oral communication, and the ability to explain technical concepts, decisions, and limitations clearly and professionally. (MLO2, MLO3, MLO4, MLO5)

The individual report assesses each student’s independent contribution to the project through the development, implementation, evaluation, or analysis of a specific aspect of the wider project. Students are required to demonstrate technical understanding, critical evaluation, professional practice, and the ability to communicate findings and recommendations effectively. Students are required to reflect on which modules have contributed to their capstone project and the skills and techniques they have developed in their methodology. (MLO1, MLO2, MLO3, MLO4, MLO5)

This assessment structure supports both collaborative and independent learning, ensuring students demonstrate technical, analytical, organisational, ethical, and communication skills appropriate to a Stage 3 capstone project in artificial intelligence and data science.

Alternative assessment:

For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.

The oral presentation may present a barrier to some students. Where appropriate, alternative arrangements may include presenting to a reduced audience or directly to the examiners. If presentation is not possible, students will be asked to provide a short reflective statement on the group-based project element, which will be marked on an individual basis.

Students requiring an alternative to group-based assessment activities may select the alternative capstone module that does not include collaborative project work.

Reading Lists

• DSC3010's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC3010's Timetable