• Inactive for Year: 2026/27

• Module Leader(s): Dr Stephen McGough
• Co-Module Leader: Dr Pete Philipson

• 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 independent project in data science and artificial intelligence. 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 undertake an extended independent project involving project scoping, research, technical implementation, analysis, evaluation, and critical reflection in relation to defined project objectives, supported through academic supervision and, where appropriate, engagement with external or industrial contexts.

The emphasis is on independent investigation, 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 substantial research, design, programming, analytical, modelling, implementation, or evaluation element, requiring students to integrate and apply knowledge and skills developed throughout their studies within an extended independent project.

Topics covered by this module include:
Project definition, scoping, and planning
Independent investigation and research
Application and integration of AI, data science, statistical, and computational techniques      
Literature review and evaluation of relevant research and technical sources
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
Project management, reflective practice, and independent development within a substantial individual 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 range of assessment designed to evaluate students’ ability to apply knowledge and skills developed across the degree programme to a substantial independent AI and data science project.

The formative assessment provides an opportunity for students to define and justify their proposed project aims, methods, and approach, enabling them to receive feedback on project scope, technical direction, planning, and feasibility prior to undertaking the main project work. (MLO1, MLO2, MLO3)

The oral presentation enables students to present and justify their project aims, technical approaches, implementation decisions, findings, and recommendations to an audience. The presentation assesses oral communication skills and the ability to explain technical concepts, decisions, limitations, and outcomes clearly and professionally. (MLO2, MLO3, MLO4, MLO5)

The individual report assesses the student’s ability to undertake and communicate a substantial independent investigation involving research, technical implementation, analysis, evaluation, and critical reflection. Students are required to demonstrate technical understanding, appropriate methodological selection, professional practice, critical evaluation, and effective communication of findings and recommendations. 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 independent learning and investigation, 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 may be asked to provide an alternative recorded presentation or equivalent reflective commentary together with an academic poster presenting the outcome of the work (appropriate to the learning outcomes being assessed).[

Reading Lists

• DSC3009's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC3009's Timetable