Computer Science - MSc
2024/25 Part-time Postgraduate course
Award:
Master of Science
Faculty:
Faculty of Computing, Engineering and the Built Environment
School:
School of Computing
Campus:
Belfast campus
Start dates:
September 2024
January 2025
We are passionate about sharing with our students the vital role they each have now and as future professionals in promoting a sustainable future for all. We believe that sustainability is not the domain of one discipline or profession. It is the responsibility of all disciplines, professions, organisations and individuals.
That is why on each of our courses within the School of Computing you will learn about the UN Sustainable Development Goals and the contribution you can make now, and as a graduate in Computing.
Read the course details below to find out more.
Creating the next generation of high-quality professionals for the Computer Science industry.
“Computer scientists understand the underlying principles of programming and algorithms and use them to design software, systems and networks to meet the needs of clients and the public. It is a fast-moving, highly specialised field and there is a constant, high demand for talented computer science graduates.” —TimesHigher
The MSc Computer Science is a specialist programme that has the core aim of preparing students for both an industrial career, equipped with a comprehensive understanding of the advanced concepts, paradigms, algorithms, theories and techniques underpinning advanced computing systems, in addition to providing a relevant platform to embark on further research studies. The course covers leading-edge subjects in areas of Advanced Computer Science, Artificial Intelligence and Internet of Things.
Further motivated by evidence of demand from industry and business for upskilling of staff in the areas of Computer Science, The new MSc in Computer Science will strive to address the growing demands in the sector by training a new kind of Computing specialist who is able to both manage data, understand business process and implement solutions subsequently interconnecting them as part of a larger system.
The delivery of the course is supported by multi-million pound infrastructure of a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities. The course content has been informed by internationally leading research being conducted in the School and by our strong industry partnerships, most notably with BT through the BT Ireland Innovation Centre (BTIIC) and with PwC through the Advanced Research and Engineering Centre (ARC).
The course is accredited by BCS, The Chartered Institute for IT, for Partial CITP (Chartered IT Professional) and Partial CEng (Chartered Engineer).
The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. W particularly welcome female applicants, as they are under-represented within the School at postgraduate taught courses.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
In this section
The MSc award consists of two compulsory taught modules (totaling 40 credits), four optional taught modules (totaling 80 credits) from a wide range of topics, in addition to a substantial piece of independent Masters Project (60 credits).
The two compulsory modules are:
Scalable Advanced Software Solutions
This module aims to explore a range of modern development and deployment concepts in the context of scalable and high-performance computing services. Within this module concepts such as containerisation, Continuous Integration, Continuous Delivery, cloud architectures, scalable solutions and infrastructure will be explored. Additionally, advanced programming/development concepts facilitating high performance solution development will be examined.
Data Science and Machine Learning
This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.
Optional modules are
Cyber Security
Cyber security, which has an impact on national security, infrastructure, and the global economy, is one of today's most pressing issues. Due to the enormous digital threat, cyber security knowledge is among the most in-demand globally. This course examines recent advancements in cyber security theory and practice. To enable critical cyber security decision-making, the students will develop the fundamental and advanced aspects of cyber security in terms of theory, practice, policy, and security standards. They will also learn about the threats to current and emerging systems and networks and how to effectively counter them in accordance with information security management standards. The students will learn about the social, legal, and ethical issues surrounding cyber security.
Deep Learning and Its Application
The module will introduce the fundamentals of deep learning, construction of neural networks and theory of developing successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimisation process along with development tools, and apply them to develop solutions for applications of computer vision and natural language processing.
Digital Transformation
This module aims to provide students with an understanding of digital transformation in a range of organisational contexts. On successful completion of the module, a student will be able to: assess how digital technologies can disrupt industries by transforming industry value chains, patterns of demand and competitive pressures; understand how digital technologies and frameworks can be applied in a digital transformation strategy; understand the organisational and people capabilities required to support and implement a digital transformation strategy; and critically evaluate current practice and theory on digital transformation.
Big Data and Infrastructure
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources. The core concepts of distributed computing will be examined in the context of a data lake. Students will be taught, practically and theoretically, about the components of Data lakes, workflows, functional programming concepts, use of MapReduce, Spark, Pig, and Hive.
IoT Networks and Protocols
The Internet of Things (IoT) describes the interconnectivity of uniquely identifiable devices embedded in the environment through internet protocols and infrastructure. The module will evaluate and critically appraise IoT networking concepts, models, standards, protocols and practical skills. It will address Sustainability Development Goals, inform on the evolving IoT use cases, and appraise related issues such as the impact of IoT on a citizen’s privacy.
Software Product Management
A software product manager is responsible for the market success of a software product by controlling the development of business strategy, coordinating with developers, marketers, and customers, and managing analytics and continuous improvement. This module identifies the stages in the product management lifecycle and equips students from a technical background with the skills to enter this increasingly important field.
Robotics & AI
This module provides an overview of smart robotics and AI. It is designed to provide students with a strong foundation through the core topics and the key technologies of robotics and AI while providing hands-on experience on programming smart robots in the labs. The module will explore practically coding AI techniques for Robotics and the focus is given to design and implement smart robots exhibiting AI behaviours.
Pervasive Computing
The focus of this module is to provide an opportunity for students to gain an in-depth understanding of pervasive computing and to apply this understanding to a range of application domains through developing specific solutions for selected application case studies. The module surveys emerging hardware and software components associated with Pervasive Computing Systems, examining the technical and societal issues concerned with a pervasive infrastructure, wireless networks, protocols and emergent algorithms. In doing so a number of examples of innovative systems and applications are reviewed. The module includes a strong practical element where students will be asked to develop services providing support for wearable and smart home context-aware solutions.
Knowledge Engineering
This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.
Intelligence Engineering and Infrastructure
The aim of this module is to educate students on best practices for engineering, deploying, testing and orchestration intelligence across modern computing. This will include aspects of Machine learning, federated operation of activities, data engineering, production of tailored computational artefacts (such as models which are tailored for a range of device type), production pipelines, automated testing and automated deployment.
Emerging and Advanced Topics in AI
This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature.
Embedded Systems and Sensors
An embedded system is an electronic or computer system which performs dedicated control and data access functions in electronics-based systems and applications. Embedded systems play crucial role in modern communications, automotive systems, consumer electronics and medical devices and will provide the foundation for the next generation of inclusive and sustainable, smart and connected Internet-of-Things (IoT) solutions. This module covers the most important aspects of the embedded systems and will provide a successful student with theoretical and practical knowledge on the feasibility, reliability, and security of electronic systems, especially those important for existing and future IoT applications.
Human Computer Interaction and UX research
This module allows students to gain knowledge about HCI and UX practices as well as the theory that supports these practices. This includes gaining experience in analysing UX related data and undertaking a literature review of a user interface technology whilst also considering a novel application for this technology.
Note: the running of optional modules are subject to minimal viable number of students taken this modules each Academic Year
Typically 5-10 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Research Project takes place in the final semester(s) seperately.
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
The course is assessed by 100% coursework.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10, 20, or 40 credit modules (more usually 20) and postgraduate courses typically 15 or 30 credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Teaching and learning activities will be in-person and/or online depending on the nature of the course. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessments. This feedback may be issued individually and/or issued to the group and you will be encouraged to act on this feedback for your own development.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification, the assessment timetable and the assessment brief. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised. The module pass mark for undergraduate courses is 40%. The module pass mark for postgraduate courses is 50%.
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study.
In Masters degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Figures from the academic year 2022-2023.
Academic staff in the School of Computing are qualified to teach in higher education with most of them holding at least a Postgraduate Certificate in Higher Education Practice. The majority of academic staff in the School (89%) are accredited fellows of the Higher Education Academy (HEA) or above. Within the School of Computing courses are taught by staff who are Professors (22%), Readers/Senior Lecturers (28%) and Lecturers (50%)
The University employs over 1,000 suitably qualified and experienced academic staff - 60% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (19%), Readers, Senior Lecturers (22%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic and learning support staff (85%) are recognised as fellows of the Higher Education Academy (HEA) by Advance HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures from the academic year 2022-2023.
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Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
In this section
Year: 2
Status: C
This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.
Year: 2
Status: C
This module aims to explore a range of modern development and deployment concepts in the context of scalable and high performance computing services.
Within this module concepts such as containerisation, Continuous Integration, Continuous Delivery, cloud architectures, scalable solutions and infrastructure will be explored. Additionally, advanced programming/development concepts facilitating high performance solution development will be examined.
Status: O
Year: 2
This module is optional
This module aims to provide students with an understanding of digital transformation in a range of organisational contexts. On successful completion of the module, a student will be able to: assess how digital technologies can disrupt industries by transforming industry value chains, patterns of demand and competitive pressures; understand how digital technologies and frameworks can be applied in a digital transformation strategy; understand the organisational and people capabilities required to support and implement a digital transformation strategy; and critically evaluate current practice and theory on digital transformation.
Status: O
Year: 2
This module is optional
The focus of this module is to provide an opportunity for students to gain an in-depth understanding of pervasive computing and to apply this understanding to a range of application domains through developing specific solutions for selected application case studies. The module surveys emerging hardware and software components associated with Pervasive Computing Systems, examining the technical and societal issues concerned with a pervasive infrastructure, wireless networks, protocols and emergent algorithms. In doing so a number of examples of innovative systems and applications are reviewed. The module includes a strong practical element where students will be asked to develop services providing support for wearable and smart home context-aware solutions.
Status: O
Year: 2
This module is optional
This module provides an overview of smart robotics and AI. It is designed to provide students with a strong foundation through the core topics and the key technologies of robotics and AI while providing hands-on experience on programming smart robots in the labs. The module will explore practically coding AI techniques for Robotics and the focus is given to design and implement smart robots exhibiting AI behaviours.
Status: O
Year: 2
This module is optional
The module introduces the fundamental concepts of deep learning, neural networks as well as the theory associated with the development of successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimization process along with development tools, and apply them to the development of solutions for deep learning application domains (i.e. Sequential data analysis, Computer Vision, Natural Language Processing, etc.)
Status: O
Year: 2
This module is optional
Cyber security, which has an impact on national security, infrastructure, and the global economy, is one of today's most pressing issues. Due to the enormous digital threat, cyber security knowledge is among the most in-demand globally. This course examines recent advancements in cyber security theory and practice. To enable critical cyber security decision-making, the students will develop the fundamental and advanced aspects of cyber security in terms of theory, practice, policy, and security standards. They will also learn about the threats to current and emerging systems and networks and how to effectively counter them in accordance with information security management standards. The students will learn about the social, legal, and ethical issues surrounding cyber security.
Status: O
Year: 2
This module is optional
This module will cover emerging topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, as well as practical skills.
Status: O
Year: 2
This module is optional
This module allows students to gain knowledge about HCI and UX practices as well as the theory that supports these practices. This includes gaining experience in analysing UX related data and undertaking a literature review of a user interface technology whilst also considering a novel application for this technology.
Year: 3
Status: C
The aim of the project is to allow the student to demonstrate their ability in undertaking an independent research project for developing theoretical perspectives, addressing research questions using data, or analysing and developing real-world solutions. They will be expected to utilise appropriate methodologies and demonstrate the skills gained earlier in the course when implementing the project.
As part of the project development activity, they will be required to extract and demonstrate knowledge from the literature in an analytic manner and develop ideas and appropriate hardware and software implementations. This may involve the development of a hardware sensor component or may access existing hardware to develop new/ novel software processing or data analytics. This will typically be followed by a structured analysis of needs for a realistic application or actual organisation and identification and application of tools/techniques required to deliver a well-formed solution. Through the project, the student will develop capabilities to analyse case studies related to IoT / Artificial Intelligence / Advanced Computer Science and its application in a range of domains including transport, environment, health and commerce. The project may further create improvement plans and recommendations for future implementation based on the tools/technologies experienced during the programme of study.
In summary, the Masters Project represents a piece of work performed by the student under suitable staff supervision which draws both from the practical and creative nature of a problem-solving project and the traditional, scholarly exposition of an area of study. The content of the work must be original and contain a critical appraisal of the subject area.
Status: O
Year: 3
This module is optional
A software product manager is responsible for the market success of a software product by controlling the development of business strategy, coordinating with developers, marketers, and customers, and managing analytics and continuous improvement. This module identifies the stages in the product management lifecycle and equips students from a technical background with the skills to enter this increasingly important field.
Status: O
Year: 3
This module is optional
The Internet of Things (IoT) describes the interconnectivity of uniquely identifiable devices embedded in the environment through internet protocols and infrastructure. The module will evaluate and critically appraise IoT networking concepts, models, standards, protocols and practical skills. It will address Sustainability Development Goals, inform on the evolving IoT use cases, and appraise related issues such as the impact of IoT on a citizen's privacy.
Status: O
Year: 3
This module is optional
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores.
Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources.
The core concepts of distributed computing will be examined in the context of a data lake. Students will be taught, practically and theoretically, about the components of Data lakes, workflows, functional programming concepts, use of MapReduce, Spark, Pig, and Hive
Status: O
Year: 3
This module is optional
This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.
Status: O
Year: 3
This module is optional
The aim of this module is to educate students on best practices for engineering, deploying, testing and orchestration intelligence across modern computing.
This will include aspects of Machine learning, federated operation of activities, data engineering, production of tailored computational artefacts (such as models which are tailored for a range of device type), production pipelines, automated testing and automated deployment.
Status: O
Year: 3
This module is optional
This module enables the student to understand, design, apply, and critically evaluate embedded systems and their applications as enabling technology for the IoT.
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
Applicants must:
(a) have gained
(i) a second class honours degree or better, in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification excluding Conversion courses; and the qualification must be in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline.
and
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent). For applicants whose first language is not English the minimum English language requirement is an Academic IELTS 6.0 with no band score less than 5.5, Trinity ISE: Pass at level III or equivalent English language tests comparable to IELTS equivalent score.
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme.
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
The entry requirements facilitate accreditation of prior learning.
In this section
Recent predictions from the US Department of Labor Bureau of Labor Statistics have indicated that the Computer and IT field will grow by 13% between the period 2016-2026. This is faster than the average rate of growth of all occupations. The MSc Computer Science specialist programme aims to provide postgraduate education and training in the area of Computer Science and its application to the needs of the industrial community. The course is designed to meet the demand for a new kind of Computing specialist who is able to both manage data, understand business process and implement solutions subsequently interconnecting them as part of a larger system. Graduates from the MSc Computer Science will be well placed to progress into a wide variety of careers, across a range of industrial settings and application domains. There are also opportunities for graduates from the MSc Computer Science to embark on further research by enrolling for PhD study affiliated with the research centres within the School of Computing. Computing related PhD studies in the areas of Pervasive Computing and Artificial Intelligence can be perused within the School of Computing.
There is no placement as part of the course, however, there are opportunities in the course for you to participate in research and industry related projects through our two Innovation centres BTIIC, CHIC and ARC.
BTIIC is the BT Ireland Innovation Centre (BTIIC) in collaboration with Ulster University and BT. The centre aims to invent new ways of using data analytics, artificial intelligence and the IoT, through two work streams of Intelligent System and IoT.
CHIC is the Connected Health Innovation Centre is funded by Invest NI to support business led research in the area of connected health, with focus on data analytics and IoT. The centre currently has over 30 national and international member companies with both technical expertise and clinical experience.
Advanced Research Centre (ARC) is a joint centre with PWC, Ulster and Queens University Belfast. ARC brings together researchers, engineers and business executives, combining expertise from academia and industry within one R&D centre and aims to downstream innovative research into commercial applications. Three technical work streams are established at Ulster on Digital Transparency, Digital Transformation and Digital Empowerment.
The price of your overall programme will be determined by the number of credit points that you initiate in the relevant academic year.
For modules commenced in the academic year 2024/25, the following fees apply:
| Credit Points | NI/ROI/GB Cost | International Cost* |
|---|---|---|
| 5 | £194.45 | £474.70 |
| 10 | £388.90 | £949.40 |
| 15 | £583.35 | £1,424.10 |
| 20 | £777.80 | £1,898.80 |
| 30 | £1,166.70 | £2,848.20 |
| 60 | £2,333.40 | £5,696.40 |
| 120 | £4,666.80 | £11,392.80 |
| 180 | £7000.20 | £17,089.20 |
NB: A standard full-time PGCert is equivalent to 60 credit points per year. A standard full-time PGDip is equivalent to 120 credit points per year.
*International student access to courses is subject to meeting visa requirements. More information can be found in the Visas and Immigration section.
It is important to remember that costs associated with accommodation, travel (including car parking charges) and normal living will need to be covered in addition to tuition fees.
Where a course has additional mandatory expenses (in addition to tuition fees) we make every effort to highlight them above. We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals, as well as first-class facilities and IT equipment. Computer suites and free Wi-Fi are also available on each of the campuses.
There are additional fees for graduation ceremonies, examination resits and library fines.
Students choosing a period of paid work placement or study abroad as a part of their course should be aware that there may be additional travel and living costs, as well as tuition fees.
See the tuition fees on our student guide for most up to date costs.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
