MSc Computer Science
About
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 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.
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. We particularly welcome female applicants, as they are under-represented within the School at postgraduate taught courses.
Start Date
September 2025 (Full-Time and Part-Time)
January 2026 (Full-Time and Part-Time)
Study Mode and Duration
Full-Time: Three semesters (12 months study)
Part-Time: Six semesters over 3 academic years
Campus
Belfast
Attendance
Each module is timetabled 5 hours per week, typically starting mid-afternoon on a weekday including lectures, tutorials and practicals for the taught components of the course. Research Project takes place in the final semesters separately. Student can select 1 or 2 taught module(s) to study per semester.
Teaching, learning and assessment
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
Masters project is conducted with a dedicated supervisor for each student.
The course is assessed by 100% coursework.
Career options
The UK economy is being stifled by a well-documented digital skills gap, costing an estimated £63 billion a year in potential GDP, prompting the Government to launch various initiatives to address this issue, including designated Institutes of Coding and the announcement of a Digital Skills Strategy to make the UK a global tech superpower. Recent estimates predict that by 2025 there will be 3 million new tech vacancies in the UK and 149 million world-wide – this is an issue affecting the global labour market.
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.
Contact
Dr. Shuai Zhang - Course Director
Email: s.zhang@ulster.ac.uk
Modules
The MSc award consists of two compulsory taught modules, four optional taught modules* from a wide range of topics, in addition to a substantial piece of independent Masters Project.
Compulsory modules
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
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.
*The provision of the optional modules each Academic year is conditioned on meeting the minimum viable student number.
Entry Requirements
Academic qualifications
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.
Eligibility
Places are limited and open to applicants who:
- are over 18 years of age;
- are eligible to work in Northern Ireland;
- are ‘settled’ in Northern Ireland, and has been ordinarily resident in the UK for at least three years; or
- are a person who has indefinite leave to enter or remain in the UK.
- meet the course specific entry requirements. See course pages for requirements.
- meet the Ulster University general entry requirements