2020/21 Part-time Postgraduate course
Master of Science
Faculty of Computing, Engineering and the Built Environment
School of Computing
Creating the next generation of high-quality professionals for the Computer Science industry.
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“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.
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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- or 20-credit modules (more usually 20) and postgraduate course 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. 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 assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
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 and the assessment timetable. 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.
Calculation of the Final Award
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 Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.
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 (18%) 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 staff (81%) are accredited fellows of the Higher Education Academy (HEA) - 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 2019-2020.
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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.
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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.
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.
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.
This module is optional
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
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 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.
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 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.
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, semantic web and computational creativity. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.
This module is optional
This module focuses on the development of self-managing systems, inspired by the human body's autonomic nervous system. The ANS is that part of the nervous system that manages body functions such as blood circulation, intestinal activity, and hormonal secretion and production, all without conscious effort. The desire of Autonomic Computing and Robotics is to bring a similar self-managing level of capability to systems, including robots, and thus free up the human user for higher-level concerns.
This module is optional
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.
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. Computer Vision, Natural Language Processing, etc.)
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.
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(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;
(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.
Typically we require applicant for taught programmes to hold the equivalent of a UK first degree (usually in a relevant subject area). Please refer to the specific entry requirements for your chosen course of study as outlined in the online prospectus. We consider students who have good grades in the following:
Typically, we require applicants for taught programmes to hold the equivalent of a UK first degree.
Please refer to the specific entry requirements for your chosen course of study as outlined in the online prospectus.
The comparable US qualifications are as follows:
UK 2:1 Degree - Bachelor degree with a cumulative GPA of 3.0 out of 4
UK 2:2 Degree - Bachelor degree with a cumulative GPA of 2.6 out of 4
|Level 12 English Lang in HSD|
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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 Systems 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.