Course Structure

This course has a January start which is unique for Queen’s University Belfast. It allows us to take a modern, applied approach to assessment. Instead of traditional written exams or a single large dissertation, students complete research-based coursework or project(s), each aligned with key areas of AI: Ethics, Engineering, and Analytics.

The advantage?
• Real-world relevance: You’ll work on practical, real-world problems that mirror the challenges AI professionals encounter in industry, going beyond theory into applied practice.
• Depth without burnout: Each project allows you to dive deep into a specific area without the pressure of a high-stakes final exam or months-long thesis.
• Stronger portfolio: You’ll graduate with multiple applied projects to showcase to employers or use in interviews.
• Skill-building across domains: With each project, you get to demonstrate a broader set of skills, from technical implementation to critical thinking and communication.
• Continuous feedback: You’ll be assessed through coursework and presentations, giving you regular feedback and the chance to improve throughout the course.

This structure supports different learning styles and prepares you for the collaborative, project-based work that defines most careers in AI and data science today.

AI for Health

This module will serve as a case study of AI applications. It will cover contemporary digital health topics such as precision medicine, diagnostics, medical imaging and drug discovery. It will develop the ability to utilise AI principles and techniques to solve some health challenges, the ability to obtain relevant data from recognised repositories, the ability to utilise existing libraries and packages for analysing and visualising health data, and the transferable skills to apply AI to solve practical challenges.

Computer Vision (Optional)

This module will cover deep neural networks (DNNs) and modern approaches to computer vision including DNN models for various computer vision tasks and current topics of computer vision. It will develop the ability to utilise DNN models to solve real-world computer vision challenges, the ability to obtain image/video data from recognised repositories, the ability to utilise existing libraries and packages for implementing appropriate DNN models for a given computer vision task.

Data Mining

This module will introduce the basics of data mining and present the need for data mining approaches and how they can handle big data. Data mining is the study of data to identify new and interesting characteristics generating new information from pre-existing data sets. The following techniques are covered along with their implementation in R including the definition of data mining, data reduction methods such as Principal Component Analysis, Linear Models and Generalised Linear Models, classification methods e.g. simple linear, nearest neighbour, decision tree models, Bayes classifying, clustering methods: k means and nearest neighbour and Association rules and their application on real datasets.

Foundations in Data Analytics and AI

This module will introduce the basic approaches to data science and AI for collecting and investigating data in a meaningful way. The module will provide the basics of how to manage and manipulate both large and small datasets. Statistical models and the concept of predictive analytics will be introduced and examples given through the introduction of regression analysis. Fundamentals of programming using both R and Python will introduce data importation, data management, procedural programming concepts and object-oriented programming concepts.

Knowledge Engineering

This module will cover classical and modern knowledge engineering techniques including logic, ontology, knowledge graph, and uncertainty reasoning. It will provide you with a systematic understanding of knowledge, principles and procedures of knowledge engineering, develop your ability to utilise suitable knowledge-based methods to solve real-world problems, and ability to evaluate and compare the performance of knowledge-based solutions for a given problem.

Machine Learning

This module will introduce the basics of machine learning algorithms and how to achieve practical implementations of the core methods. This will include a study of classical machine learning algorithms; supervised and unsupervised methods; applications and tools. It will also cover modern methods including deep learning and convolutional neural networks. The module will provide a basic understanding of application areas including implementation in Python. Topics covered will include Unsupervised methods: Self-organising maps, EM, dimensionality reduction: PCA, LDA, Supervised methods: K-NN, decision trees, boosting, ensemble methods, random forests, neural networks, deep learning, CNNs and Applications: text mining and information retrieval of active learning, vision: face recognition, genetic algorithms.

Mini Project Module - AI Engineering

This module equips students in Data Science and AI with the skills to design, build, and deploy end-to-end AI systems. It covers system architecture, ML Ops practices, and front-end integration, with hands-on experience in tools like LangChain, Docker, and FastAPI. Students will learn to manage unstructured and domain-specific data, automate workflows, and evaluate system performance. The module also addresses security, robustness, and ethical considerations in real-world AI deployment.

Mini Project Module - AI Ethics

This module equips students in Data Science and AI with the conceptual understandings, thought frameworks and practical strategies to address the ethical and societal implications of their work. It emphasises the role of issues around data, algorithms, and deployment contexts in shaping outcomes, and promotes the integration of ethical reasoning into the design, development, and governance of AI systems.

Mini Project Module - Applied Analytics

This module will begin with an introduction of the concept of Visual Analytics, the science of analytical reasoning that is facilitated by interactive visual interfaces. The module will focus on the introduction of the design and development of interactive dashboards, along with practical implementation. This module will provide experience of working with real-life data to gain knowledge of how to engage with both experts and non-experts of data science and AI. A real-world dataset will be provided to students with an open-ended problem to solve to act as training for life as a data scientist.

Natural Language Processing (Optional)

This module will mainly cover modern approaches to natural language processing (NLP), including various deep neural networks (DNNs) for NLP, current topics of NLP. It will develop the ability to utilise DNN models to solve real-world NLP challenges, the ability to obtain text/speech data from recognised repositories, the ability to utilise existing libraries and packages for developing NLP models, and an awareness of current developments, methods and applications of NLP.

Teaching Times

The MSc is taught over three days each week during the semester. You will study one module per day, with 6–7 hours of teaching for that module. This timetable stays the same throughout the semester.

Learning and Teaching

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Assessment

All of the modules on the PGCert/MSc programme are assessed by coursework, in common with practice across the sector in similar courses and aligning with teaching and learning standards across the university.

• Coursework typically involves problem analysis within a real-world or simulated data context, application of theoretical knowledge from data science, statistics, and machine learning, design and implementation of data-driven or AI-based solutions, and the evaluation of these approaches using appropriate metrics. Students are expected to present their findings through technical reports and, in some cases, oral presentations, demonstrating both analytical and communication skills. Specific types of coursework may be:
  
  • Individual/group projects,
  • Class tests,
  • Lab work and written reports,
  • Presentations – group and individual,
  • Peer marking – formative feedback,
  • Case studies,
  • Peer assessment,
  • Experiment design – carrying out and reflecting on process,
  • Reflective report on skill development,
  • Reflective report on meeting learning outcomes,
  • Problem based learning,
  • Public events.

Modules

Career Prospects

Introduction

Graduates will be prepared for roles such as:

• Data Scientist
• Machine Learning Engineer
• AI Developer
• Data Analyst
• Data Engineer
• Research Scientist (AI or Data Science)
• Business Intelligence Analyst
• AI Consultant
• Health Data Specialist
• Computer Vision or NLP Engineer
• Ethical AI Advisor

Employment after the Course

Employers who are interested in people like you:

BT, BBC, PwC, Kainos, Datactics, Allstate, Citibank
Data Intellect, Ocula Technologies, Natwest, FP McCann, EY, Celerion, Tiktok, Microsoft, Google, Facebook, Oosto (formerly Anyvision).

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

Tuition Fees

Northern Ireland (NI) 1	£7,300
Republic of Ireland (ROI) 2	£7,300
England, Scotland or Wales (GB) 1	£9,250
EU Other 3	£25,800
International	£25,800

¹EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

² EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

³ EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

How to Apply

Apply using our online Queen's Portal and follow the step-by-step instructions on how to apply.

Apply now

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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