Creating the next generation of high-quality professionals for the Computer Science industry.
The University regularly ‘refreshes’ courses to make sure they are as up-to-date as possible.
In addition it undertakes formal periodic review of courses in a process called 'revalidation’ to ensure that they continue to meet standards and are current and relevant.
This course will be revalidated in the near future and it is possible that there will be some changes to the course as described in this prospectus.
“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 new MSc in Computer Science is a specialist programme that prepares you for an industrial career with a comprehensive understanding and practical experience of the advanced concepts, paradigms, algorithms, theories and techniques underpinning advanced computing systems. The course covers leading-edge subjects of programming, networks, sensor technologies, machine learning and statistical modelling—the most up to date topics in areas of 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 jointly established £28.6 million BT Ireland Innovation Centre.
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) in addition to a substantial piece of independent Masters Project (60 credits).
The two compulsory modules are:
Scalable Advanced Software Solutions
In modern development/deployment approaches, source code is stored in a controlled and collaborative manner. It is automatically tested, monitored, packaged and deployed across test and production environments. Traditional server infrastructure is supplanted by dynamic hosting provided by cloud providers which can react, shrink and grow at scale and on demand. These modern operational practices have shown to increase solution quality with a greater responsiveness to customer needs. In addition to technical aspects of such modern solutions, there is an impact on the administrative, operational and accounting practices when compared with traditional development and deployment 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.
Statistical Modelling & Data Mining
This module first provides a systematic understanding of probability and statistics. It then provides an in-depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as Python, R and Weka. Online tools, such as Blackboard will be used to facilitate blended learning approach. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real-world contexts.
Optional modules are
Machine Learning
Machine learning is the branch of artificial intelligence concerned with algorithms and statistical models that use data for various tasks. This module will provide students with the mathematical and statistical knowledge to understand the foundations of common supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world data-sets. State-of-the art methods including probabilistic programming and explainable AI will also be introduced.
Deep Learning and Its Application
Deep learning is an emerging and important focus area of Artificial Intelligence (AI). It aims to learn models and patterns as in conventional machine learning approaches, but it has the ability to discover more accurate representations without manual intervention for new types of domains. More recent advances on deep learning have led to very successful applications in computer vision, natural language processing, autonomous computing, etc. 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. Computer Vision, Natural Language Processing, etc.)
Big Data & 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 Hadoop. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
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 working with wireless sensor networks. 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.
IoT Networks & Security
IOT has emerged as a significant technology that can be used for automation and empowerment. The module covers the life cycle of IoT security mechanisms, including the design, development, management and, most importantly, how they are sustained. The module provides an understanding of the IoT architecture, protocols and security considerations; and the ensuing computing challenges of managing big data in a secure way.
Knowledge Engineering & Computational Creativity
Knowledge Engineering is a branch of artificial intelligence concerned with the process to build, maintain, use and extend knowledge-based systems that use (symbolic) AI methods. Computational creativity can be defined as the use of an algorithmic approach/perspective to gain a better understanding of human creativity or AI to replicate human-level creativity using computing. This module introduces students to the fundamentals of knowledge engineering and computational creativity, including terminology and concepts, core models and algorithms, technologies, and application scenarios. Students will be introduced to data science toolkit that can be applied to build knowledge-based applications. Material covered will be contextualised by providing examples of the latest research within the area. Through completion of the module, students will demonstrate qualities including subject-specific knowledge and skills and the ability to use flexible and creative approaches to problem solving and communication.
Autonomic Computing & Robotics
Autonomic computing is a self-managing computing model (self*) named after, and inspired by, the human body's autonomic nervous system. The goal of autonomic computing is to enable systems to run themselves, providing the capability of high-level functioning (autonomy) while keeping the system's complexity invisible to the user. An autonomous robot is a robot that performs behaviours or tasks with a high degree of autonomy. Autonomous robotics is usually considered to be a sub-field of artificial intelligence, robotics, and information engineering. Our proposition is that to enable a truly autonomous robot, it requires autonomicity. This module will introduce students to the self-managing systems area of Autonomic Computing and its cross-over into AI & (Autonomic) Robotics. These self* principles will be able to be applied to the advanced automation of any system not just AI & Robotics.
Masters Project
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 cases 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.
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.
We have a highly experienced and energetic course team, in terms of both teaching and research. All members of the course team are research active and will be included in the forthcoming REF2021 submission. The course team have been grantholders of multi-million pound research projects, they have produced world-leading and internationally excellent research outputs in the area and have demonstrated research impact from their endeavours. We also fully embrace the importance of innovative teaching and assessment methods and are all Fellows of the Higher Education Academy in the UK.
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (20%) or Lecturers (55%).
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 staff (81%) are accredited fellows of the Higher Education Academy (HEA) by Advanced 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 correct for academic year 2021-2022.
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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.
In this section
Applicants must:
(a) have gained
(i) a second class honours degree or better, in the subject areas of computing, engineering, mathematics or related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institute of another country which had 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; and the qualification must be in the subject areas of computing, engineering, mathematics or 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.
The course doesn’t require placement experience.
There are opportunities in the course for you to participate in research and industry related projects through our two Innovation centres BTIIC and CHIC.
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.
We also have strong links with IAESTE (International Association for the Exchange of Students for Technical Experience). It provides students industry placement opportunities from six weeks to 1 year in one of 80 countries linked with the Association.
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 2023/24, the following fees apply:
| Credit Points | NI/ROI/GB Cost | International Cost |
|---|---|---|
| 5 | £186.65 | £440 |
| 10 | £373.30 | £880 |
| 15 | £559.95 | £1,320 |
| 20 | £746.60 | £1,760 |
| 30 | £1,119.90 | £2,640 |
| 60 | £2,239.80 | £5,280 |
| 120 | £4,479.60 | £10,560 |
| 180 | £6,719.40 | £15,840 |
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.
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.
