Without software engineering, you wouldn’t be reading this page. Software innovation has transformed the way we think about, and interact with, technology. In fact, all the forms of communication, interaction and access that we take for granted today are made possible by software in some shape or form. Which is why the demand for software engineers has never been greater
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From improving the efficiency of the apps we use in order to make them more sustainable, to developing scalable and secure app infrastructure, Software Engineers have skills that drive innovation. In essence, they are problem solvers with the ability to take an existing software system and creatively refine, enhance and transform it into something even better.
This degree programme is designed to equip you with the analytical, reasoning and practical skills you will need to design, develop and deploy large software systems. Plus, our project based learning approach enables you to craft a degree pathway that fits your own areas of interest. You could find yourself working on anything from apps to virtual and augmented reality; lifesaving smart medical implants to consumer space travel or automated intelligence.
Employer experience is embedded into this course and allows students to engage and learn from prospective employers from day one. Through placements, sponsored projects and hackathons, you will engage with employers and begin making a difference to real world problems.
Software Engineering highlights
Career Development
Students taking this programme may spend a year gaining professional experience in industry in a paid full-time post. Students are helped to obtain suitable places and the School has excellent links with over 500 local, national and international employers such as Citi, BT, Liberty IT, and Kainos in Belfast, IBM in England, Microsoft and Sun Microsystems in Dublin, Fujitsu in Japan and Siemens in Germany.
Industry Links
Our students are constantly given the opportunity to put theory into practice. We regularly consult a large number of employers including, for example, Civica and Sensata Technologies, who provide sponsorship for our students as well as Kainos and Liberty IT who are members of the employer liaison panel for the course.
Student Testimonials
Rebekah Kane (Computer Science/Software Engineering)
Attraction to QUB
I went to secondary school in Belfast and we had an open day at QUB in sixth year, which really opened my eyes to how many opportunities are available to you when you study at Queen’s, especially for students like myself who have an interest in studying abroad.
I was also really impressed with the development plans for the Computer Science Building, which opened at the start of my second year of uni. The resources available within the building are fantastic and events run weekly which allow students to connect with future employers.
Positive experience during studies
Personally, my placement year stands out as the most positive experience I have had throughout my degree. I worked with the Renault Formula One Team in Oxfordshire for nine months within their IT department, and it was a fantastic opportunity to network within an industry I would like to work in once I graduate. Through studying at QUB I also had the opportunity to partake in a two month placement in Zhuhai, China, which was an amazing experience and makes me stand out to perspective employers.
Placement
My placement was with the Renault F1 Team in Enstone, Oxfordshire. I spent nine months there working as an IT analyst, helping users with various tech problems they were experiencing. My placement year was probably the highlight of my time at Queen’s, as it has opened doors for me within an industry that many believe to be out of reach. I was lucky enough to work alongside some of the best minds in the business, as well as the F1 drivers and the media.
Engaging in extracurricular activities
I studied French at A-Level but had forgotten much of it by the time I finished my first year of university, so this year I signed up for post-GCSE and A-Level French classes. Through this I have met so many people who have the same interests as me and I feel not only much more confident at the language but also more involved in the wider QUB community.
One piece of advice for EEECS applicant
I wasn’t entirely sure whether or not I should study Computer Science at university, but it has opened more doors for me than I could have imagined. If you are the kind of person who is good with numbers and logic but also likes to be creative, then this is the course for you. I didn’t study IT based subjects at GCSE or A-Level – my A-Level subjects were Maths, Chemistry, Biology and French – but by approaching concepts like programming with an open mind and using the support systems within the EEECS community that are available, I am on course to graduate with a 2.1 in a highly desirable degree.
Going forward
Once I graduate in June, my plan is to travel for a few months before returning to the world of F1 for the 2021 Season.
If you had a time machine, and could go back to your first day at Queen’s, what would you do differently? (if anything!)
The best bit of advice that I can give to freshers is to talk to people at your induction – making friends on the first day is the easiest way to do it as everyone is in the same boat. I didn’t know anyone on my course on that first day, but it’s been four years and I still sit with the same girls I befriended on day one.
These degrees teach the fundamental principles of Software Engineering, together with the necessary skills, tools and techniques to enable our graduates to embark on careers as professional software engineers. Special emphasis is placed on individual and team-based software development projects in preparation for industry.
The programme contains the following themes which may change due to the nature of the IT Industry and keeping up with industrial trends:
Stage 1
Reasoning for Problem Solving
Introduction to Software Engineering
Foundation of Computing Systems
Stage 2
Stage 2 themes build on Stage 1 to include core aspects of Software Engineering such as database design and networking.
Students taking the Single Honours BEng spend a year on a paid full-time placement. The School has links with over 500 local, national and international employers eg BT, Liberty, Asidua, Kainos (Belfast), IBM (England), Microsoft, Sun Microsystems (Dublin), Fujitsu (Japan) and Siemens (Germany), and students are assisted in obtaining placements. It is expected that students would gain a Licentiateship of the City & Guilds from this period of work experience
Topics may include:-
Professional Computing Practice
Architecture and Networks
Data Structures Algorithms and Programming Languages
Information Modelling
Software Development
Software Engineering
Stage 3
Along with working on a software engineering project these advanced topics permit each student to tailor their skills towards one or more areas of software engineering specialism.
Topics may include:
Concurrent Programming
Agile & Component Based Development using NET
Information System Security
Formal Methods
Software Design Principles and Patterns
Advanced Computer Architecture
Stage 4
This four-year extended degree has been established to provide a supply of particularly well-qualified graduates who will become industry leaders. It has a blend of Software Engineering knowledge and skills and business practice and management. Students can undertake a year of professional experience.
Transferring from BEng to the MEng is possible for selected students at the end of Stage 2, subject to satisfactory performance.
Topics may include:
Research and Development Project
Advanced Software Engineering
HPC Principle Parallel Program
Advanced Intelligent Information Systems
Algorithms: Analysis and Application
People teaching you
EEECS T: +44 (0)28 9097 4669
E: eeecs@qub.ac.uk
Contact Teaching Hours
Small Group Teaching/Personal Tutorial
6 (hours maximum) 6 hours of practical classes, workshops or tutorials each week
Large Group Teaching
9 (hours maximum) 9 hours of lectures
Personal Study
24 (hours maximum) 22-24 hours where we expect students to spend time on completing assignments, working on projects (individual or group), preparing for practical classes, alongside studying and reviewing taught material.
Learning and Teaching
The School has a world class reputation for research and provides excellent facilities, including access to major new research centres in Secure Information Technologies, Electronics, Communications and Information Technology and Sonic Arts. A number of modules on the course are closely linked to the research expertise of these centres and evolve and change rapidly to reflect some of the current, emerging and exciting developments in the field.
At Queen’s, we aim to deliver a high quality learning environment that embeds intellectual curiosity, innovation and best practice in learning, teaching and student support to enable student to achieve their full academic potential.
On the Software Engineering degree we do this by providing a range of learning experiences which enable our students to engage with subject experts, develop attributes and perspectives that will equip them for life and work in a global society and make use of innovative technologies and a world class library that enhances their development as independent, lifelong learners. Examples of the opportunities provided for learning on this course are:
Additional Information
Students have access to a wide range of computers in world class laboratories (equipped with several hundred PCs) and specialised software packages. Networks link the School and university computers to powerful machines in Great Britain.
The course emphasises the professional and practical application of software engineering. As such, students are encouraged to build strong links with local software engineering companies through employer involvement within projects, lectures and other forms of teaching contact.
E-Learning technologies
IThe Virtual Learning Environment (VLE) is called CANVAS and may be associated with communication relating to lectures and assignments. A range of e-learning experiences are also embedded in the degree through, for example: interactive group workshops in a flexible learning space; IT modules; podcasts and interactive web-based learning activities; opportunities to use IT programmes associated with design in practicals and project- based work etc.
Lectures
Introduce information about new topics as a starting point for further self-directed private study/reading. Lectures also provide opportunities to ask questions, gain some feedback and advice on assessments (normally delivered in large groups to all year group peers).
Peer Mentoring
Queen’s runs a peer mentoring scheme for Computing students – a group of students from all year groups (except first year) are trained to provide support for the 1st year students, in terms of offering advice and guidance, organising social events etc. The School has an active body of EEECS Student Mental Health Ambassadors. The School also has a Computing Society (QCS – Queen’s Computing Society) who organise a range of activities, including social events and more formal activities such as industry lectures, for all Computing students. Charity games evenings are open to all computing students in the School.
Personal Tutor
Undergraduates are allocated a Personal Tutor who meets with them on several occasions during the year to support their academic development.
Practicals
Where you will have significant opportunities to develop technical skills and apply theoretical principles to real-life or practical contexts. Comprehensive demonstrator support is provided.
Projects and teamwork
A number of modules throughout the degree will use supervised projects as a means of enabling you to put your technical understanding into practice. The extensive use of team based projects will provide you with the opportunity to develop skills widely used by employers. In final year, you will be expected to carry out a significant piece of research on a topic or practical methodology. You will receive support from a supervisor who will guide you in terms of how to carry out your research and will provide feedback to you.
Self-directed study
This is an important part of life as a Queen’s student when important private reading, engagement with e-learning resources, reflection on feedback to date and assignment research and preparation work is carried out.
Work placements
A student taking this programme can spend a year gaining professional experience in industry in a paid full-time post. Students are helped to obtain suitable places and the School has excellent links with over 500 local, national and international employers such as Microsoft, Liberty IT, Kainos, IBM,Deloitte, BT, Citi Group, SAP and Dell.
Assessment
Details of assessments associated with this course are outlined below:
The way in which you are assessed will vary according to the Learning objectives of each module. Some modules are assessed solely through project work or written assignments. Others are assessed through a combination of coursework and end of semester examinations. Details of how each module is assessed are shown in the Student Handbook which is provided to all students during their first year induction.
Feedback
As students progress through their course at Queen’s they will receive general and specific feedback about their work from a variety of sources including lecturers, module co-ordinators, placement supervisors, personal tutors, advisers of study and peers. University students are expected to engage with reflective practice and to use this approach to improve the quality of their work. Feedback may be provided in a variety of forms including:
Feedback provided via formal written comments and marks relating to work that you, as an individual or as part of a group, have submitted
Face to face comment. This may include occasions when you make use of the lecturers’ advertised “office hours” to help you to address a specific query.
Placement employer comments or references.
Online or emailed comment
General comments or question and answer opportunities at the end of a lecture, seminar or tutorial.
Pre-submission advice regarding the standards you should aim for and common pitfalls to avoid. In some instances, this may be provided in the form of model answers or exemplars which you can review in your own time.
Feedback and outcomes from practical classes.
Comment and guidance provided by staff from specialist support services such as, Careers, Employability and Skills or the Learning Development Service.
Once you have reviewed your feedback, you will be encouraged to identify and implement further improvements to the quality of your work.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
• Computer Abstractions and Technology
• Basic computer organisation
• Digital Design Basics
• Number Representation
• Arithmetic for Computers
• Microarchitecture Basics – Pipelining
• Instructions: Language of the CPU
• Instruction Set Architectures
• Basic Assembly Programming
• Compilation Flow (how high-level languages are operated)
• The role of the operating system
Learning Outcomes
• Describe how information (e.g. numbers, characters etc.) is represented in computers.
• Describe the internal hardware organisations that form a computer.
• Describe how a high level program is executed in a computer, including the role of the operating system
• Implement basic assembly language programs
• Describe some of the fundamental differences between instruction set architectures
Skills
Application of Number, ICT, Improving Own Learning and Performance, Problem Solving, Design and Implementation of solutions, Programming
This module introduces essential concepts and skills for developing data-driven web applications, covering relational and NoSQL databases, and client-side technologies (HTML, CSS, JAVAScript). Emphasizing best practice software design principles, development activities are underpinned by industry standard approaches to software modelling, designing methodologies, software testing principles and key security considerations. Additionally, this module fosters transversal skills such as communication, teamwork, problem-solving and agility in a team environment. Aimed at providing a strong technical and theoretical foundation, this module equips students for the dynamic field of web development and software design, blending technical competencies with essential soft skills for the software industry.
Learning Outcomes
Upon successful completion of this module, students will be able to:
1. Design and implement relational and NoSQL databases, understanding their respective use-cases and benefits,
2. Develop interactive web pages using HTML, CSS and JavaScript, adhering to modern web standards .
3. Employ fundamental software design and testing principles in the development process, integrating modelling techniques, design methodologies, and ensuring code reliability and functionality.
4. Understand and apply fundamental principles of security in web development and database management, recognising common vulnerabilities and learning to implement basic protective measures.
5. Apply and evaluate the transversal skills associated with software development including effective communication, teamwork, problem-solving, and adaptability in a team environment.
Skills
Design and implementation of modern data driven systems considering technical and environmental aspects.
This module will introduce the fundamentals of maths for students studying a computing degree. As you progress through your nominated degree you will need to understand the concepts of algorithms design, logical reasoning and programming. Therefore, it is necessary to understand how to apply mathematical arguments and knowledge to model real world problems. This module will also cover key mathematical concepts for problem solving and analysis including: number theory, algebra, logic, set theory, vectors and matrices, statistics and graph theory. This will allow you to apply mathematical reasoning about problems and programs and strategies for problem solving.
Learning Outcomes
Students must be able to:
• Demonstrate knowledge, understanding and the application of the principles of number theory to include:
o Number systems, arithmetic operations, prime numbers, fundamental theorem of arithmetic.
• Demonstrate knowledge, understanding and the application of the principles of algebra to include:
o Algebraic expressions and notation for the product and summation of algebraic terms.
• Demonstrate knowledge, understanding and the application of the principles of logic to include:
o Propositional logic, predicate logic and proofs.
• Demonstrate knowledge, understanding and the application of the principles of set theory to include:
o Sets, set operations, set equality, subsets, sequences and functions.
• Demonstrate knowledge, understanding and the application of the principles of vectors & matrices to include:
o Addition, multiplication, distributive and associativity, and identity matrix.
• Demonstrate knowledge, understanding and the application of the principles of statistics to include:
o Probability theory and introductory methods for data analysis.
• Demonstrate knowledge, understanding and the application of the principles of graph theory to include:
o Graph models, trees, paths, cycles, Euler's theorem.
Introduction and Cybersecurity concepts
Access control and authentication
Risk Assessment/Management
Social Engineering
Basic Crypto Systems
Weaknesses of cryptosystems
Cryptosystems in GSM technology
Learning Outcomes
Understand the core principles of secure information system design.
Identify and analyse the current threats and challenges to the security of information systems, data and services.
Evaluate system protection technologies and methods.
Apply knowledge of cryptographic algorithms to provide data confidentiality.
Skills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
This module introduces the fundamentals of procedural programming. Using a problem-solving approach, real-world examples are explored to promote code literacy and good algorithm design. Students are introduced to the representation and management of primitive data, structures for program control and refinement techniques, which guide the development process from problem specification to code solution.
Learning Outcomes
Students must be able to:
• Demonstrate knowledge, understanding and the application of the principles of procedural programming, including:
o Primitive data types (including storage requirements)
o Program control structures: Sequencing, selection and iteration
o Functions/methods and data scope
o Simple abstract data structures, i.e. strings and arrays
o File I/O and error handling
o Pseudocode and algorithm definition/refinement
• Apply good programming standards in compliance with the relevant codes of practice e.g. naming conventions, comments and indentation
• Analyse real-world challenges in combination with programming concepts to write code in an effective way to solve the problem.
Skills
KNOWLEDGE & UNDERSTANDING: Understand fundamental theories of procedural programming
INTELLECTUAL AND PRACTICAL:
• Be able to design and develop small programs, which meet simple functional requirements expressed in English.
• Programs designed, developed and tested will contain a combination of some or all of the features as within the Knowledge and Understanding learning outcomes.
This module introduces the fundamentals of object-oriented programming. Real-world problems and exemplar code solutions are examined to encourage effective data modelling, code reuse and good algorithm design. Fundamental OO programming concepts including abstraction, encapsulation, inheritance and polymorphism are practically reviewed through case studies, with an emphasis on testing and the use of code repositories for better management of code version control.
Learning Outcomes
Students must be able to:
• Demonstrate knowledge, understanding and the application of the principles and application of object-oriented design, to include:
o Abstraction, encapsulation, inheritance and polymorphism
• Demonstrate knowledge of static data modelling techniques (through UML)
• Demonstrate knowledge, understanding and the application of the principles and application of object extensibility and object reuse.
• Demonstrate knowledge, understanding and the application of more advanced programming concepts, to include:
o Recursion
o Searching and sorting
o Basic data structures
• Demonstrate knowledge, understanding and the application of testing, in particular, unit and integration testing.
• Apply good programming standards in compliance with the relevant codes of practice and versioning tools being employed e.g. naming conventions, comments and indentation
• Analyse real-world challenges in combination with OO programming concepts to write code in an effective way to solve the problem.
Skills
KNOWLEDGE & UNDERSTANDING: Understand fundamental theories of object-oriented programming
INTELLECTUAL AND PRACTICAL:
• Be able to design, develop and test programs, which meet functional requirements expressed in English.
• Programs designed, developed and tested will contain a combination of some or all of the features as within the Knowledge and Understanding learning outcomes.
This module introduces the fundamentals of object-oriented programming. Real-world problems and exemplar code solutions are examined to encourage effective data modelling, code reuse and good algorithm design. Fundamental OO programming concepts including abstraction, encapsulation, inheritance and polymorphism are practically reviewed through case studies, with an emphasis on testing and the use of code repositories for better management of code version control.
Learning Outcomes
Students must be able to:
• Demonstrate knowledge, understanding and the application of the principles and application of object-oriented design, to include:
o Abstraction, encapsulation, inheritance and polymorphism
• Demonstrate knowledge of static data modelling techniques (through UML)
• Demonstrate knowledge, understanding and the application of the principles and application of object extensibility and object reuse.
• Demonstrate knowledge, understanding and the application of more advanced programming concepts, to include:
o Recursion
o Searching and sorting
o Basic data structures
• Demonstrate knowledge, understanding and the application of testing, in particular, unit and integration testing.
• Apply good programming standards in compliance with the relevant codes of practice and versioning tools being employed e.g. naming conventions, comments and indentation
• Analyse real-world challenges in combination with OO programming concepts to write code in an effective way to solve the problem.
Skills
KNOWLEDGE & UNDERSTANDING: Understand fundamental theories of object-oriented programming
INTELLECTUAL AND PRACTICAL:
• Be able to design, develop and test programs, which meet functional requirements expressed in English.
• Programs designed, developed and tested will contain a combination of some or all of the features as within the Knowledge and Understanding learning outcomes.
This module introduces the concepts and techniques involved in developing embedded systems including small-board computers and IoT devices. It will include an introduction to microcontroller electronics and printed circuit boards as well as an introduction to the programming of embedded systems.
Learning Outcomes
On successful completion of the course the student will:
- understand the basis structure of a computer program suitable for deployment in an embedded system.
- understand the basic structure of an MCU (Microcontroller Unit).
- understand how to develop software for the MCU.
- understand how basic analogue and digital interface circuits are designed for an MCU.
- understand how to develop event-driven ISR (Interrupt Service Routines).
- understand in general how Printed Circuit Boards (PCBs) are designed and constructed.
Skills
The skills developed by the students during this course are as follows:
- How to use an IDE (Integrated Development Environment) for developing embedded software programs.
- Understand how to edit, compile and test/debug simple embedded programs.
- Design simple programming routines to carry out real-world tasks.
- Understand how to design simple embedded systems to solve real-world problems.
• Data structures: Stacks, Lists, Queues, Trees, Hash tables, Graphs, Sets and Maps
• Algorithms: Searching, Sorting, Recursion (with trees, graphs, hash tables etc.)
• Asymptotic analysis of algorithms
• Programming languages representation and implementation
Learning Outcomes
• Demonstrate understanding of the operation and implementation of common data structures and algorithmic processes (including stacks, lists, queues, trees, hash tables, graphs, sets and maps, alongside searching, sorting and recursion algorithms).
• Select, implement and use data structures and searching, sorting and recursive algorithms to model and solve problems.
• Perform asymptotic analysis of simple algorithms.
• Demonstrate understanding of the fundamentals of programming languages representation, implementation and execution.
Skills
Problem solving by analysis, solution design and application of techniques (e.g. suitable data structures, algorithms, and implementation in C++). Precision and conciseness of expression. Rigour in thought.
The module deals with the design and the development of software with service-oriented architecture (SoA). In particular, the module focuses on how software can be developed by designing well-separated and cohesive components (a.k.a. modular architecture) using SoA design principles. To make these components reusable over the Web, the module further illustrates how software components can be exposed using the current technology of Web services, accessed via Web APIs. Also, the module deals with the serialization techniques that service components use to exchange data to each other. The students will collaborate in small teams to build software as a composition of macro-services and/or micro-services.
Learning Outcomes
Students must be able to demonstrate knowledge and understanding on:
* designing (modular) service-oriented architecture software
* applying SoA design principles
* using the current technology of Web services
* defining Web APIs
* employing serialization techniques
* designing service compositions
*collaborative development of SoA team projects
Skills
The skills acquired after completing the module are the following:
* the design and the development of service-oriented architecture software
* the usage of the current technology of Web services (RESTful and SOAP services)
* the design of reusable software components
* the definition of JSON and XML Web APIs
* the employment of serialization techniques (e.g. Java to JSON/XML and vice versa) for the data exchange between Web services
* the collaborative SoA software development
• Software development as teamwork; roles and responsibilities within a team.
• The software engineering process: eliciting and specifying requirements – functional and non-functional; analysing, designing, implementing and testing software systems; deployment; maintenance.
• Software engineering in the context of sustainable development:
- understanding complex real-world problems from a social, economic and environmental perspective;
- designing sustainable real-world solutions that have a significant software element and that aspire to address the tensions between conflicting concerns.
• Contemporary software development methodologies – including:
- use-case-driven and model-based approaches; representing actors and aspects of system behaviour and architecture in the Unified Modelling Language (UML) ;
- agile and lean approaches; user stories, story estimation, sprints (planning, monitoring and review);
- hybrid approaches – e.g. combining use cases and stories.
• Specific software development techniques, tools and practices – including:
- version control software; automated tests and test-driven development; pair- and mob-programming; test coverage; continuous integration, delivery and deployment; DevOps.
• Specific investigative and problem-solving techniques, including:
- team-based, collaborative learning as part of a process of identifying and designing innovative sustainable solutions to problems identified from authentic case studies;
- gamification, game-based learning and simulation as means of representing key aspects of real-world problems and the steps in the process of formulating sustainable, software-supported, real-world solutions.
• Object-oriented design principles: evaluating the quality of a software design; questions of coupling and cohesion; configuring mechanisms of collaborating software objects.
• UI design principles: evaluating the quality of an interface design; usability and the user experience.
• Algorithmic design: formulating and representing stepwise solutions to a problem.
• Building security into the development process; awareness of and avoidance of vulnerabilities.
• Delivering reliable and secure working systems – from design to working software.
Learning Outcomes
• work as a member of a collaborative, mutually supportive team;
• actively develop and deliver a non-trivial, well-engineered software system that meets its functional and non-functional requirements, including avoidance of software vulnerabilities – the software system may take the form of a game, based on a real-world problem and the process of designing its solution;
• demonstrate an ability to confront, manage and shape contemporary social, economic and ecological conditions as part of the process of developing software systems;
• understand key aspects of modern software development practices;
• critically evaluate development challenges and resolve them methodically using appropriate techniques and tools;
• realise object and algorithmic designs using an appropriate implementation language (e.g. Java, C#) and operating system (e.g. Windows, Android);
• plan and implement a test strategy that incorporates automated tests (e.g. JUnit, Visual Studio Test Explorer) and manual tests (e.g. user acceptance testing and evaluation);
• use appropriate version and project management software (e.g. Git, Trello, Jira).
Skills
Problem solving (including the ability to analyse and mitigate problems that are characterised by change, uncertainty, risk, and complexity), time management, communication skills, team working, practical skills (competent use of development software and project management software in the context of a software engineering project).
This module will prepare students for employment by developing an awareness of the business environment and the issues involved in successful career management combined with the development of key transferrable skills such as problem solving, communication and team working. Students will build their professional practice and ability to critically self-reflect to improve their performance.
Key elements will explode legal, social, ethical and professional issues (LSEPIs) including intellectual property, computer-aided crime, data protection and privacy including GDPR, security, net neutrality, communication through technology, cultural sensitivity and gender neutrality. The British Computer Society (BCS) code of conduct will be exploded and understood.
Learning Outcomes
To prepare students for employment in industry and research through developing an awareness of the business environment and key skills.
To develop and demonstrate a range of transferrable skills including communication skills, presentation, group working and problem solving.
To develop skills in critical reflection of self and others feeding into improvements.
To explore legal, social, ethical and professional issues (LSEPIs). Examples of areas to be explored will relate to: Intellectual Property, Computer Crime, Work Quality, Challenges of On-line content Quality, Digital Divide including Net Neutrality, Privacy including GDPR, Security, Globalisation, Communication through effective use of technology, Cultural Sensitivity, Gender Neutrality. British Computer Society (BCS) Code of Conduct will be explored covering Public Interest, Professional Competence and Integrity, Duty to Relevant Authority and Duty to the Profession.
Skills
Problem synthesis and resolution as an individual and as a team. Development and use of suitable communication mechanisms. Business and professional awareness.
Introduction to Artificial Intelligence and Machine Learning
Overview
• Concepts of artificial intelligence and machine learning.
• Fundamentals of supervised and unsupervised learning
• Fundamentals of experimental settings and hypothesis evaluation
• The concept of feature selection
• Evaluation in machine learning
o Type I and Type II errors
o Confusion matrices
o ROC and CMC curves
o Cross validation
• Linear and non-linear function fitting
o Linear Regression
o Kernels
• Classification models:
o Nearest Neighbour
o Naïve Bayes
o Decision Trees
• Clustering models:
o k-Means
o hierarchical clustering
o Anomaly detection
Learning Outcomes
• Knowledge and understanding of techniques and selected software relevant to the field of artificial intelligence.
• Ability to identify techniques relevant to particular problems in artificial intelligence and data analysis.
• Ability to discuss and provide reasonable argumentation using artificial intelligence and machine learning concepts.
• Ability to identify opportunities for software solutions in artificial intelligence and data analysis.
• Ability to solve specific data analysis problems using techniques of artificial intelligence and machine learning.
Skills
Problem and data analyses, design of logical and statistical models, application of computational techniques, understanding results.
This module will cover fundamental concepts in cyber security and systems security. By the end of this module students should grasp the core principles of secure information system design, be aware of the current threats and challenges to the security of information systems, data, and services, and understand the application of cryptographic algorithms for confidentiality, integrity, and authentication.
• Security and Vulnerability
• Modern cryptography concepts and application
o Confidentiality, integrity and availability
o Symmetric cryptography and Public key cryptography
o Authentication, digital signatures and access control
o Use of cryptography in information systems
• Introduction to secure information system design
• Threats and challenges in cyber security
o Human threats/social engineering
o Physical layer attacks
• System protection technologies and countermeasures
Learning Outcomes
• Identify and analyse the current threats and challenges to the security of information systems, data, and services,
• Evaluate system protection technologies and methods,
• Apply knowledge of cryptographic algorithms to provide confidentiality, integrity, and authentication.
Skills
Problem solving, communication skills, time management, practical skills (including a base understanding of cryptography and challenges in cyber security).
• Networking fundamentals, classifications and protocols
• The Internet and World Wide Web including Client-Server approach
• Computer Network layers
• Routing algorithms/Scalable routing
• Local Area Network topologies and protocols
• Common Internet application protocols e.g., HTTP/HTTPS
• Software-Defined Networks
• Socket-based connections
• Selected networking topics e.g., Network Security, Wireless Networks, Network Resources
Learning Outcomes
• Describe Computer Network layers and models such as OSI, TCP/IP.
• Describe common network protocols including TCP/IP suite e.g. IP/TCP/UDP.
• Demonstrate knowledge and understanding of routing algorithms and scalable routing.
• Demonstrate knowledge and understanding of common Internet application protocols as well as client-server network architectures.
• Demonstrate knowledge and understanding of software-defined networks.
• Demonstrate knowledge and understanding of security and resource consumption in networking.
Skills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
The Cloud Computing module will provide an opportunity for you to learn about and explore a wide range of concepts, technologies, providers, and applications of cloud computing. Initially the module will focus on concepts including how we design, deploy, and manage cloud software and infrastructure to ensure both high availability and elastic scaling (being able to go from thousands of users to millions of users seamlessly). You will learn in detail how software can be developed in such a way as to easily allow (or not) cloud deployment including concepts of functional and stateless programming. After covering general concepts and generic technologies such as containerisation for micro-services, virtualisation, and devops pipelines, the module moves on to look at specific modern cloud providers such as AWS, GCP, and Azure. You will examine the differences between these platforms, learn how to deploy to them, and also gain experience of meta tools which are platform-agnostic and can be used to specify and manage cloud estates covering multiple providers.
Learning Outcomes
On completion of this module, students will be able to:
• Demonstrate knowledge, understanding and the application of:
o Core cloud concepts including data synchronisation, performance management, security, and infrastructure design
o Virtual machines and virtualisation stacks
o Container technology including coordinated container swarms and approaches
o Elastic scalable computing with automatic adjustment to load conditions
• Demonstrate knowledge, understanding and the application of the principles and application of appropriate software development considerations to ensure developed software is cloud-deployable
• Demonstrate knowledge and understanding of the principles of functional and stateless programming
• Demonstrate knowledge and understanding of the principles of modern devops pipelines including automated infrastructure, continuous integration, continuous deployment, and monitoring
• Demonstrate knowledge and understanding and the application of common widely used cloud hosting platforms and management tools
"Ambiguous problem situations (‘wicked problems’); design thinking and innovation; design thinking practice and agile software development practices; appropriate software development technologies; project management; collaboration and teams; remote and face-to-face collaboration"
Learning Outcomes
• Understand the principles of agile software innovation
• Understand a range of practices that agile, software-innovation teams can apply
• Understand the properties of, and the impact of, ambiguous problem situations, e.g., wicked problems
• Develop suitable interim and final software prototypes using agile practices and software innovation practices
• Demonstrate proficiency in using a range of contemporary tools and techniques
• Understand the range of factors that can influence the success of team-based software innovation
• Complete a project which demonstrates strong innovation, project and team skills.
Skills
This module provides an opportunity to exercise aspects of the following Key Skills (at QCA proficiency Level 4); ICT, Improving Own Learning and Performance, Problem Solving
• Cyber Security Overview
• Malware Analysis in Virtual Machines
• Basic dynamic analysis
• X86 Disassembly
• IDA Pro
• Recognising C Code Constructs in Assembly
• Malware Types
• Analyzing Malicious Window Programs
• Covert Malware Launching
• Malware Behaviour and Signatures
• Machine learning for malware detection
Learning Outcomes
Students should be able to:
- Ability to perform advanced static analysis
- Ability to perform basic dynamic analysis
- Understand the different types of malware and understand their behaviour
- Understand how automated malware detection works
Skills
Problem analysis, Problem solving. Rigour in thought. Ability to work individually or as part of a team. Demonstrate increased communication, library, research, time management and organisational skills.
Concepts, techniques, and tools in software testing including: Unit testing, integration and system testing, acceptance testing, GUI testing, test coverage analysis, automated testing, test tools, test management, test organisation, test planning, test maturity and career paths in Software Testing.
Learning Outcomes
On completion of this module, the successful student will have achieved the following learning outcomes, commensurate with module classification:
- Be able to understand and apply fundamental testing principles and techniques.
- Be able to develop an appropriate test plan alongside a relevant set of tests for a given piece of software against a set of defined test goals.
• Be able to efficiently organise, execute, report and evaluate a given test plan against a piece of software.
• Be able to effectively employ a range of test automation tools.
Skills
Understanding and applying various software testing concepts, techniques, and tools.
• Overview of imaging and video systems and generic machine learning pipelines
• Pattern recognition problems: Verification, detection and identification
• Data pre-processing:
o Image enhancement: Normalisation. Point Operations, Brightness and contrast.
o Filtering and Noise reduction. Convolution
• Classification
o Support Vector Machines (SVM).
o Boosting and ensemble of classifiers
o RF
o Neural networks.
o Deep Learning.
• Vision-specific Feature extraction:
o Simple features
o Gradients and Edge extraction
o Colour Extraction and colour histograms
o SIFT
o Histogram of Gradients HoG
• Unsupervised learning:
o Clustering and Bag of Words for vision
o Self-organised maps
• Segmentation, tracking and post processing
o Brightness segmentation
o Motion detection; Background modelling and subtraction; Optical Flow
o Template Matching
o Tracking: Kalman Filter, Particle Filter and tracking by detection
o Introduction to time series analysis
• Dimensionality reduction techniques and latent spaces.
o The curse of dimensionality
o Principal component analysis (PCA).
o Linear discriminant analysis (LDA).
• Introduction to Deep Learning
• GPU acceleration for video processing.
• Applications:
o Video Surveillance
o Cyber-physical security
o Medical imaging
o Secure corridors.
o Pose estimation.
o Biometrics
o Face detection
o Human behaviour analysis.
Learning Outcomes
Be able to:
• Explain when and how machine learning and computer vision is useful in industry, public institutions and research.
• Know and apply a range of basic computer vision and machine learning techniques.
• Demonstrate the ability to understand and describe the underlying mathematical framework behind these operations.
• Design and develop machine learning pipelines applied to computer vision applications
• Formulate and evaluate hypothesis
• Evaluate the performance of proposed machine learning solutions through rigorous experimentation
• Analyse quantitative results and use them to refine initial solutions
• Communicate finding effectively and in a convincing manner based on data, and compare proposed systems against existing solutions
Skills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
This course is a study of the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Topics may include performance issues and evaluation, instruction sets, processor micro-architecture and pipelining (basic design, hazards and speculation), caches, operating system support (virtual memory, exceptions, interrupts), in-order and out-of-order execution, parallel architectures and fault tolerance.
As computer scientists or software engineers understanding how a computer works and what techniques can be used to accelerate its performance is essential. The course will prepare students for jobs in the computer engineering industry and can act as a springboard to more advanced material in graduate-level courses
Learning Outcomes
By the end of this course, a successful student should be able to:
• Describe computer architecture concepts and mechanisms related to the design of modern processors and memories and explain how these mechanisms interact;
• Apply this understanding to new computer architecture design problems, and;
• Evaluate various design alternatives and make a quantitative and/or qualitative argument for why one design or execution strategy is superior to other approaches.
Introduction to Network Security
• Key concepts & principles
• Attack Types, Threats, Vulnerabilities in Internet Protocols.
• Firewalls, Access Control and Traffic Filtering
• Intrusion Detection and Prevention Systems
• Secure Network Architecture
• Internet Security Protocols
Learning Outcomes
A successful student will:
• Know and understand the administration of network security;
• Know and understand the technologies involved in the design and deployment of secure networks;
• Be able to demonstrate the use of tools for network security analysis, Firewalls etc.
Skills
This module provides an opportunity to exercise aspects of the following QCA Key Skills (at proficiency Level 4): Communication, ICT, Improving Own Learning and Performance and Problem Solving.
Concurrent Programming Abstraction and Java Threads, the Mutual Exclusion Problem, Semaphores, Models of Concurrency, Deadlock, Safety and Liveness Properties. Notions are exemplified through a selection of concurrent objects such as Linked Lists, Queues and Hash Maps. Principles of graph analytics, experimental performance evaluation, application of concurrent programming to graph analytics.
Learning Outcomes
To understand the problems that are specific to concurrent programs and the means by which such problems can be avoided or overcome.
Skills
To model and to reason rigorously about the properties of concurrent programs; to analyse and construct concurrent programs in Java; to quantitatively analyse the performance of concurrent programs.
Software Design Principles, Patterns, Practice and Innovation
Overview
Underlying Principles of Good Software Design. Creational patterns; Structural patterns; Behavioural patterns; Architectural Patterns. Commercialisation of software products, Entrepreneurship and Innovation, Legal Social and Ethical considerations.
Learning Outcomes
Learning Outcomes: On completion of this module, the successful student will have achieved the following learning outcomes, commensurate with module classification:
• Understand and be able to explain with examples the basic principles of good OO design;
• Demonstrate the capability to design and implement a range of well-known software design patterns;
• Be able to document a given software design pattern;
• Be able to choose between software design patterns;
• Know the steps in bringing a software product to market
• Appreciate the Legal, social and ethical considerations in delivering software.
Lectures:
• Introduction to the threat landscape
o Attack surface and vectors
o Attacker’s perspective (Bad actors)
• Software assurance initiatives and standards
o The need for software assurance (famous disaster)
o OWASP, CWE , SANS, etc.
• Secure software development lifecycle
o Examine the different approaches to software management
• Security issues, risks and risk management
o Types of attacks/vulnerabilities
o Roles and Responsibilities
o Vulnerability management (CVE)
• Requirements specification and threat modelling
o Identifying risks
o Misuse and Abuse Cases
o Model and manage risks
• Secure architecture and design
o Asset Protection
o Authentication, Authorisation
o Risk management
o Code deployment (signing)
• Secure coding, principles and practice
o Secure Coding standards
o Examine different programming languages
• Security analysis and testing
o Testing principles
o Black/white Box, Unit testing, Integration and Regression testing
o Testability of software (third party etc.)
• Development and code analysis tools
o Types of code analysis tools
o Scanner and penetration tools
Coursework:
• Implement an SQL filter to detect attack strings
• Secure Code Review Report
Practical Labs:
• Investigate three forms of injection attacks- Implement three attacks on a Weak-Server and write an evaluation report.
• Scanner Analysis tools – Perform a security risk assessment on a web application using appropriate scanner tools.
• Code analysis tools – Using both static and dynamic code analysis, evaluate a software program for vulnerabilities.
Learning Outcomes
A successful student will be able to:
• Explain industry's approach to software assurance
• Manage and implement software assurance processes
• Understand and critically assess security requirements
• Identify software risks and vulnerabilities
• Implement secure coding standards
• Demonstrate the use of software vulnerability verification tools
Skills
Successful participation in this module will enable students to develop skills in the following areas:
• Manage one’s own learning and development including time management and organisational skills
• Good cyber security practice in the specification, design, implementation, evaluation and maintenance of security solutions.
o Adversarial thinking, threat landscape and attack vector evaluation.
o Perform risk assessment and identify countermeasures.
o Understanding impact and consequences
o Design and development secure software programs
o Software security testing and vulnerability analysis
Digital Transformation: Software Design, Management and Practical Implementation
Overview
Opportunity Analysis, Entrepreneurship and Innovation, Business Planning, Modelling and documenting software design; Software Design principles and patterns; Software Architecture; Modern approaches to software design; Legal Social and Ethical considerations, Software Project and Team Management
Learning Outcomes
Students will
i) Have a good knowledge of market evaluation, opportunity scoping, background research and software design related to a modern commercial setting.
ii) Gain the ability to evaluate systems in terms of architecture, general quality attributes and possible trade-offs presented within the given problem.
iii) Gain knowledge of the commercial and economic context of the development use and maintenance of computer-based systems.
iv) Be able to frame the opportunity within an innovative business model outlining the overall requirements i.e. model and analyse the extent to which a computer-based system meets the criteria defined for its current need, use and future development.
v) Recognise the legal, social, ethical and professional issues involved in the exploitation of 36 computer technology and be guided by the adoption of appropriate professional, ethical and legal practices.
vi) Be able to apply analytical skills within a team to a practical commercial opportunity.
vii) Understand the realisation of software requirements as software designs.
viii) Appreciate how to operate and contribute as part of a team, understanding the different ways of organising teams and the roles within a team in the development and delivery of an end-to-end software solution.
ix) Appreciation of risk management within the development process from an end user, commercial, team and individual perspective.
x) Deploy effectively suitable tools for the construction and documentation of computer applications and to use and apply information from technical literature
Skills
Knowledge of opportunity analyses, business modelling, and commercial delivery of software against a created set of requirements
The project will take the form of a research investigation. A research problem should be investigated by developing a piece of software that can be used to generate research results. The results from the investigation should be analysed, validated and appropriate conclusions drawn.
Learning Outcomes
Following successful completion students will be able to demonstrate:
1. knowledge and understanding of a given research problem;
2. the ability to investigate a research problem;
3, the ability to develop a substantial software system;
4. the ability to analyse results;
5. the ability to conduct a survey of the literature;
6. the ability to write an article and defend the research presented in it.
Skills
The ability to apply investigative skills, research skills and general software engineering principles to the solution of problems - which may require investigative, practical or design skills or a combination of all three. Originality is encouraged.
Fairness, Interpretability and Privacy in Machine Learning
Overview
• Machine Learning and Fairness: How ML algorithms could make unfair decisions
• The Political and Philosophical Underpinnings of ML Fairness
• Overview of some recent Fair AI algorithms
• Machine Learning and Interpretability: The case for explainable and transparent ML
• Overview of how some recent Interpretable AI algorithms, and how they operationalize interpretability
• Machine Learning and Privacy: How ML could (inadvertently) violate privacy
• Overview of recent advances in Privacy oriented ML
• Situating Fairness, Interpretability and Privacy within the broader ML ethics context
Learning Outcomes
Be able to:
• Understand the risks of socially applied ML along several ethics dimensions.
• Critically analyse ML algorithms with respect to fairness and deliberate on improvements.
• Critically analyse the interpretability of ML algorithms, and contemplate on improving their interpretability characteristics.
• Understand the privacy implications of ML algorithms, and develop pathways towards enhancing privacy.
• Communicate findings and decision making processes grounded on data and ethical principles.
Skills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Analysis and design of algorithms, complexity, n-p completeness; algorithms for searching, sorting; algorithms which operate on trees, graphs, strings. Database algorithms, B-tree and hashing, disk access, algorithms. Applications of algorithms
Learning Outcomes
To understand some of the principal algorithms used in Computer Science; to be able to analyse and design efficient algorithms to suit particular applications.
Skills
Analysis, design and implementation of efficient algorithms.
Streaming workload modelling languages
* Algorithm optimisation schemes
* Handling of time in algorithm design
* Number systems and operations
* High Level Synthesis (HLS) technology for Field Programmable Gate Array (FPGA)
* Application partitioning for parallel processing platforms
* System optimisation
Learning Outcomes
Understand the design principles for design of heterogeneous hardware/software embedded digital signal processing (DSP) systems, in five specific areas: computing architectures, application modelling, parallel partitioning, scheduling and code generation, and implementation optimisation.
* In the area of computer architectures, students will be able to:
* The influence of number system on accuracy and cost of different number systems
* Handling time and dependency in custom architecture design
* Discriminate how behavioural expressions of a function translate to circuit architectures using High Level Synthesis (HLS) technology.
* In the area of application modelling and code generation, students will be able to:
* Analyse and compare dataflow languages for a given application
* Derive firing rules for dataflow actors
* Apply mathematical consistency checks to static dataflow models
* Appraise the implementation concerns of parallel processing algorithms
* Investigate constructive hierarchical and multi-stage partitioning algorithms
* Relate constructive and iterative partitioning algorithms
* Relate partitioning algorithms to achieve specific implementation goals
* Analyse dataflow models for deadlock
* Analyse the code and data memory requirements, throughput and efficiency of the resulting embedded schedules
* In the area of optimisation of custom systems, students will be able to:
* Outline the behaviour of system optimisation approaches.
* Contrast graph transformation techniques for optimisation of embedded dataflow schedules
* Transform embedded schedules for optimisation with respect to data memory, throughput and efficiency
* Relate advanced dataflow models for further optimisation with respect to a given criteria
* Illustrate retiming, folding and unfolding, hardware sharing for dedicated hardware optimisation
Skills
Assimilation of technical material
Critical thought in the design of resource-constrained computer designed problems
Application to practical data processing design examples
This module focuses on approaches to use multiple compute resources simultaneously to solve problems. Parallel programming is the use of closely located normally homogeneous computing resources such as multicore processors, high performance clusters or supercomputers to speed computation up through simultaneous execution. Distributed computing is the opposite end where multiple heterogenous systems with unreliable and/or slow communication links are used to spread workload.
This practically-oriented module will cover the theory and implementation of parallel and distributed systems using different programming techniques, environments and concepts.
Topics covered will include:
• Basic concepts and terminology
• Parallel programming models
• Program and problem analysis
• Practical parallel programming and implementation of parallel code
• Distributed computing theory
• Data synchronisation methods
Learning Outcomes
To demonstrate understanding of:
• The principles underpinning effective and efficient parallel programs
• The principles underpinning effective and efficient distributed computing
• Implementation of parallel and distributed solutions in an efficient fashion
• Modern multi-threaded execution environments and software development architectures
Skills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
AAB including at least one preferred A-level (see list below) + GCSE Mathematics grade C/4
OR
AAA including at least one relevant A-level (see list below) + GCSE Mathematics grade C/4
A maximum of one BTEC/OCR Single Award or AQA Extended Certificate will be accepted as part of an applicant's portfolio of qualifications with a Distinction* being equated to a grade A at A-level and a Distinction being equated to a grade B at A-level.
Irish Leaving Certificate
H2H3H3H3H3H3 including at least one preferred Leaving Certificate subject at grade H3 (see list below) + Ordinary Level grade O4 in Mathematics if not offered at Higher Level
OR
H2H2H3H3H3H3 including at least one relevant Leaving Certificate subject at grade H3 (see list below) + Ordinary Level grade O4 in Mathematics
International Baccalaureate Diploma
34 points overall including 6,6,5 at Higher Level to include at least one preferred Higher Level subject (see list below)
OR
36 points overall including 6,6,6 at Higher Level to include at least one relevant Higher Level subject (see list below)
If not offered at Higher Level/GCSE then Standard Level grade 4 in English and Mathematics would be accepted.
BTEC Level 3 Extended/National Extended Diploma
A relevant computing QCF Level 3 BTEC Extended Diploma (180 credits), with D*D*D + GCSE Mathematics grade C/4.
OR
A relevant computing RQF Level 3 BTEC National Extended Diploma (1080 Guided Learning Hours (GLH)), with D*D*D + GCSE Mathematics grade C/4.
OR
A relevant engineering or scientific QCF Level 3 BTEC Extended Diploma (180 credits), with D*D*D* + GCSE Mathematics grade C/4.
OR
A relevant engineering or scientific RQF Level 3 BTEC National Extended Diploma (1080 Guided Learning Hours (GLH)), with D*D*D* + GCSE Mathematics grade C/4.
Graduate
A minimum of a 2:1 Honours Degree, provided that subject specific requirements are met
Note
All applicants must have GCSE English Language grade C/4 or an equivalent qualification acceptable to the University.
Computer Science, Computing Information Technology and Software Engineering share a common core of modules in the first year, so students may therefore transfer between these degrees at the end of first year, subject to meeting the normal progression requirements.
Preferred subjects: Mathematics, Computing or Software Systems Development
Relevant subjects: Chemistry, Digital Technology, ICT, Physics, Technology and Design or Double Award Applied ICT
Further information
Applicants for the MEng degree will automatically be considered for admission to the BEng degree if they are not eligible for entry to the MEng degree both at initial offer making stage and when results are received.
How we choose our students
In addition, to the entrance requirements above, it is essential that you read our guidance below on 'How we choose our students' prior to submitting your UCAS application.
Applications are dealt with centrally by the Admissions and Access Service rather than by the School of Electronics, Electrical Engineering and Computer Science. Once your application has been processed by UCAS and forwarded to Queen's, an acknowledgement is normally sent within two weeks of its receipt at the University.
Selection is on the basis of the information provided on your UCAS form, which is considered by a member of administrative staff from the Admissions and Access Service and, if appropriate, the Selector from the School. Decisions are made on an ongoing basis and will be notified to you via UCAS. These decisions can only be made on the basis of the information given and applicants must show due care and diligence when completing their applications. In particular, full details must be included about qualifications completed or still to be completed.
For entry last year, applicants must have had, or been able to achieve, a minimum of six GCSE passes at grade B/6 or better though this profile may change from year to year depending on the demand for places. Applicants must have GCSE passes at grade C/4 or better in English Language and Mathematics. The Selector also checks that any specific entry requirements in terms of GCSE and/or A-level subjects can be fulfilled.
Offers are normally made on the basis of three A-levels. Two subjects at A-level plus two at AS would also be considered.
The offer for repeat candidates is normally the same as the offer for first time applicants. For repeat applicants acceptable grades may be held from the previous year.
A-level General Studies and A-level Critical Thinking are not normally considered as part of a three A-level offer and, although they may be excluded where an applicant is taking 4 A-level subjects, the grade achieved could be taken into account if necessary in August/September.
Applicants offering other qualifications, such as the International Baccalaureate, BTEC Extended Diploma or Irish Leaving Certificate, will also be considered. The same GCSE profile is usually expected of those candidates offering other qualifications.
The information provided in the personal statement section and the academic reference together with predicted grades are noted but these are not the final deciding factors in whether or not a conditional offer can be made. However, they may be reconsidered in a tie break situation in August.
Applicants are not normally asked to attend for interview.
Applicants who apply for the MEng degree will automatically be reconsidered for the BEng course if they are not eligible for entry to the MEng degree, both at initial offer-making stage and when results are received. Applicants will be offered a place on the BEng course, provided that they satisfy the normal entry requirements for admission to the BEng course.
If you are made an offer then you may be invited to an Open Day, which is usually held during the second semester. This will allow you the opportunity to visit the University and to find out more about the degree programme of your choice; the facilities on offer. It also gives you a flavour of the academic and social life at Queen's.
If you cannot find the information you need here, please contact the University Admissions and Access Service (admissions@qub.ac.uk), giving full details of your qualifications and educational background.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
An IELTS score of 6.0 with a minimum of 5.5 in each test component or an equivalent acceptable qualification, details of which are available at: http://go.qub.ac.uk/EnglishLanguageReqs
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
International Students - Foundation and International Year One Programmes
INTO Queen's offers a range of academic and English language programmes to help prepare international students for undergraduate study at Queen's University. You will learn from experienced teachers in a dedicated international study centre on campus, and will have full access to the University's world-class facilities.
These programmes are designed for international students who do not meet the required academic and English language requirements for direct entry.
Studying for a Software Engineering degree at Queen’s will assist you in developing the core skills and employment-related experiences that are valued by employers, professional organisations and academic institutions. Graduates from this degree at Queen’s are well regarded by employers (local, national and international).
Consultations
We regularly consult and develop links with a large number of employers including, for example, Civica and Liberty IT, who provide sponsorship for our students as well as Citi and Kainos who are members of the employer liaison panel for the course.
Employment Links
The School has links with over 500 IT companies both here and abroad. We benefit from the fact that there are more software companies located in N Ireland than any other part of the UK, outside of London. This offers benefits on many levels for our students, from industrial input to the content of our courses, through to year long and summer placements as well as activities such as competitions organised by the companies etc.
Northern Ireland has an excellent international reputation for the quality and supply of its software engineers. Indeed many companies, both national and international, have opted for Northern Ireland as a base for their computing divisions in recognition of the high quality of graduates produced by the local universities.
Given this situation, it is not surprising that our graduates have had unparalleled job opportunities over the years, both locally and internationally. Because of the achievements of Queen's graduates already in the software engineering profession, a Software Engineering degree from Queen's is a highly respected qualification.
Employers, from large multinational firms to small local organisations, actively target our students, recognising that Queen's Software Engineering graduates are equipped with the skills they need. On graduating the majority of graduates take up posts associated with software design and implementation. Opportunities exist in fields as diverse as finance, games, pharmaceuticals, healthcare, research, consumer products, and public services - virtually all areas of business. Some of the employers include BT, Liberty IT, Kainos, Accenture, Citi, Wombat Financial Software.
The types of career open to Software Engineering graduates include: Software Developer; Systems Analyst; Web Designer; Games Developer; Programmer; IT Consultant; Project Manager.
What employers say
“We’ve been recruiting QUB students for many years now. The reason why we do is that the students who join us have always bowled us over with their enthusiasm, can-do attitude and how quickly they can learn the skills required to work in Kainos. They fit in quickly to our team environments, and add a huge amount of value in terms of the work they do here.”
Kainos
“The students who join PwC NI from Queen's University Belfast EEECS not only come with a great academic record but also portray the key skills, we as a firm seek. Skills such as teamwork, good communication, willingness to learn and develop and reimagine the impossible. This I feel is due to a strong focus on both academics and the importance of the careers services during their university degree.”
PwC
“Every year, Liberty IT employs about 20 placement students, many of whom come from Queen’s. The standard of these students is very high, which is evidenced by the conversion rate to Graduate Software Engineers within the company once they graduate – this year it was 100%.”
Liberty IT
Degree Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Degree Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
The tuition fees quoted above for NI and ROI are the 2024/25 fees and will be updated when the new fees are known. In addition, all tuition fees will be subject to an annual inflationary increase in each year of the course. Fees quoted relate to a single year of study unless explicitly stated otherwise.
Tuition fee rates are calculated based on a student’s tuition fee status and generally increase annually by inflation. How tuition fees are determined is set out in the Student Finance Framework.
Additional course costs
Students may wish to become a student member of the British Computer Society at an annual cost of £37.
All Students
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
There are different tuition fee and student financial support arrangements for students from Northern Ireland, those from England, Scotland and Wales (Great Britain), and those from the rest of the European Union.
Application for admission to full-time undergraduate and sandwich courses at the University should normally be made through the Universities and Colleges Admissions Service (UCAS). Full information can be obtained from the UCAS website at: www.ucas.com/students.
When to Apply
UCAS will start processing applications for entry in autumn 2025 from early September 2024.
The advisory closing date for the receipt of applications for entry in 2025 is still to be confirmed by UCAS but is normally in late January (18:00). This is the 'equal consideration' deadline for this course.
Applications from UK and EU (Republic of Ireland) students after this date are, in practice, considered by Queen’s for entry to this course throughout the remainder of the application cycle (30 June 2025) subject to the availability of places. If you apply for 2025 entry after this deadline, you will automatically be entered into Clearing.
Applications from International and EU (Other) students are normally considered by Queen's for entry to this course until 30 June 2025. If you apply for 2025 entry after this deadline, you will automatically be entered into Clearing.
Applicants are encouraged to apply as early as is consistent with having made a careful and considered choice of institutions and courses.
The Institution code name for Queen's is QBELF and the institution code is Q75.
Additional Information for International (non-EU) Students
Applying through UCAS
Most students make their applications through UCAS (Universities and Colleges Admissions Service) for full-time undergraduate degree programmes at Queen's. The UCAS application deadline for international students is 30 June 2025.
Applying direct
The Direct Entry Application form is to be used by international applicants who wish to apply directly, and only, to Queen's or who have been asked to provide information in advance of submitting a formal UCAS application. Find out more.
Applying through agents and partners
The University’s in-country representatives can assist you to submit a UCAS application or a direct application. Please consult the Agent List to find an agent in your country who will help you with your application to Queen’s University.
