MEng|Undergraduate
Computer Science
Academic Year 2024/25
AAB/AAA
4 years (Full Time)
G402
From driving cancer research forward to accurately predicting the weather, Computer Scientists are powering progress. In fact everything from social care to cybersecurity or even space travel, relies on the talents of Computer Science graduates. We would say the sky is the limit, but we’re already well beyond that!
A degree in Computer Science teaches you to approach technical problems creatively. It also gives you the information and understanding to find ground-breaking solutions to the world’s emerging problems. The course will also equip you with the practical skills to approach the specification, design, construction and use of computer systems.
In an ever changing technological climate, the Computer Science degree programme is constantly evolving to anticipate emerging digital breakthroughs. You will cover topics like machine learning, augmented reality and data analytics, but also receive a grounding in skills like hardware architecture, software engineering and simulation & modelling too.
Employer experience is paramount in this this course, from day one you will learn from prospective employers about ‘real world’ challenges. Industry placements, company sponsored hackathons and project based learning form a core part of the curriculum and vastly improve our graduate employability rates.
Computer Science Degree highlights
Internationally Renowned Experts
- The School has a number of very strong research groups engaged in leading edge technology. Major new research centres have been established in Secure Information Technologies (the UK Centre of Excellence), Electronics, Telecommunications and Information Technology (ECIT), e-Science and in Sonic Arts.
Student Experience
- Due to the high demand for Computer Science graduates, some 15–20 scholarships are available, including some sponsored by Civica, Citi and Liberty IT, worth up to £25k. All provide for a cash stipend each academic year, a guaranteed industrial placement, an opportunity for additional
part-time work during the academic year, plus the opportunity of a permanent position on graduation.
(For further information on these and other scholarships available, see the School Website.)
http://www.qub.ac.uk/eeecs
Kevin Toner (Computer Science)
Attraction to QUB
All my older brothers went to Queen’s before me and enjoyed their time there. One of them did Computer Science and he talked me through the course, and it sounded very interesting. Plus, it is the top university in Northern Ireland.
Positive Experience during studies
Not easy to pinpoint one, I have so many!
But if I had to choose one, my studies in America would probably be the highlight. I met some of the most amazing people ever. And experienced so much kindness and have so many fond memories from there, such as walking round the houses in the town in knee-high snow to sing Christmas carols in French.
Placement
SpotX, here in Belfast. It was amazing. All my work colleagues were really helpful and supportive. I was able to put into practice a lot of the skills I learned at university and developed a lot of new ones. I was able learn so much.
Engaging in extracurricular activities
I engaged in a lot of things. I tried to participate in all the international programs available to me.
I’ll go through all the international opportunities first:
In first year, I took part in a Global Leadership program in Kuala Lumpur, Malaysia. During this program I worked as part of a team with the main objective of coming up with a solution to the major world challenge: “What makes a city smart?”
In second year, I went to Bangalore in India to work as a software engineer intern at Infosys for 10 weeks during the summer. I worked on a project creating automated testing software for Android apps.
After my placement year, I went to China for a month to participate in the Beijing Institute of Technology China International Summer Programme study. As part of this program I was taught the Chinese language and all about the Chinese culture.
Instead of going back to Queen’s for my final year, I opted instead to take part in the Study USA program. I studied Business at Hastings College, Nebraska for a year. I had an amazing time there, taking part in many extracurricular activities such as theatre and the college talent show, which I was fortunate enough to win, thanks to some magic card tricks. I got on really well and was awarded the Study USA Student of the Year award.
During the past summer I spent 3 months working as a software engineer intern at a software company called MakoLab in Lodz, Poland. I completed this internship through IAESTE. I was working on web development.
I also took part in local extracurricular activities here in Belfast also:
I was part of the Hurling club for first year.
I took part in the Insight into Management program. Here I learnt about management practices, activities and developing management and business skills through a range of business tasks/games.
I completed the Inspiring Leaders program. This program taught me how to translate my leadership and employability experiences into employability skills.
I am currently studying French at the language centre and have completed a course of Spanish in the past.
One piece of advice to potiential EEECS applicants
Take part in all the opportunities that Queen’s offers – say “Yes” to everything. As seen above, Queen’s offers a lot of opportunities and a lot of them are international opportunities. I’ve thoroughly enjoyed each and every program that I have participated in and they have helped me to develop a lot as a person
Going forward
As seen from all the programs that I have taken part in, I’m an opportunist. So, I’m going to cross my fingers that an amazing global opportunity comes my way that excites me and take part in it. There are a few internships abroad from IAESTE that interest me. Ideally in the future I would set up my own business.
If you had a time machine, and could go back to your first day at Queen’s, what would you do differently? (if anything!)
Study a language Queens Language Centre sooner.
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Course content
Course Structure
Introduction | These degrees aim to teach the fundamental principles of Computer Science, together with the necessary skills, tools and techniques to enable our graduates to embark on careers as professional software engineers, or to become suitably qualified to undertake research in Computer Science. As with all of our courses, industrial engagement forms an integral part, balancing academic theory with practical learning. Single Honours BEng/BSc students spend a year on a paid, full-time placement - the School has links with over 500 local, national and international employers, eg BT, Liberty IT, Asidua, Kainos (Belfast), IBM (England), Microsoft, Sun Microsystems (Dublin), Fujitsu (Japan) and Siemens (Germany), and students are assisted in obtaining placements. The programme contains the following themes which may change due to the nature of the IT Industry and keeping up with industrial trends. |
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Stage 1 | The first year gives solid foundational knowledge around core topics in computer science, topics covered may include: Reasons for Problem Solving Introduction to Software Engineering Foundations of Computing Systems Databases Programming |
Stage 2 | In the second year you will continue to build on more knowledge and take modules which will expand into more in depth topics such as: Professional Computing Practice Architecture and Networks Data Structures Algorithms and Programming Languages Information Modelling Software Development Theory of Computation |
Stage 3 | At stage 3 you will have some choice on topics as you begin to specialise in your chosen interests. Some of the topic areas which may be available include: Formal Methods Artificial Intelligence Concurrent Programming Cloud Computing |
Stage 4 | This is a four-year extended degree, established to provide a supply of particularly well-qualified graduates who will become industry leaders. It contains a blend of Computer Science knowledge and skills and business practice and management, as well as skills in conducting state-of-the-art research. Students have the option of a year's professional experience in industry. The first two years and much of Year 3 are common with the BSc/BEng degree. Transfer to the MEng is possible for selected students at the end of Stage 2, subject to performance. In the final (MEng stage 4 year) you will be studying advanced modules which cover technical issues to a deep level of detail alongside completing a large independent project. Such specialisms are constantly under review but example topics available may include: Advanced Software Engineering High Performance Computing Advanced Computer Engineering |
People teaching you
EEECS
T: +44 (0)28 9097 4669
E: eeecs@qub.ac.uk
Contact Teaching Times
Small Group Teaching/Personal Tutorial | 6 (hours maximum) 6 hours of practical classes, workshops or tutorials each week |
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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. |
Large Group Teaching | 9 (hours maximum) 9 hours of lectures |
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.
The MEng in Computer Science provides a range of learning experiences which enable 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. - E-Learning technologies
The 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.
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
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Overview
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Modules
Modules
The information below is intended as an example only, featuring module details for the current year of study (2022/23). 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.
- Year 1
Core Modules
Introduction to Computer Architecture (20 credits)Introduction to Computer Architecture
Overview
• 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 systemLearning 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 architecturesSkills
Application of Number, ICT, Improving Own Learning and Performance, Problem Solving, Design and Implementation of solutions, Programming
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
1
Credits
20
Module Code
CSC1033
Teaching Period
Autumn
Duration
12 weeks
Databases (20 credits)Databases
Overview
• Databases
o Introduction to the fundamental concepts in database systems
o Exploring and solving real world problems using data models and schemas.
o Creating and designing Relational databases including Tables, Fields, Keys and Joins
o Managing a relational database using Structured Query Language (SQL)
o Formal Approaches to Relational Database Design (normalization theory, dependency theory).
o Advanced Topics on Modern Data Management (data extraction, mining, integration).
o Database access from a programming language (e.g. Java) including being able to display, modify, delete and update data on it.Learning Outcomes
Be able to:
• Databases
o Demonstrate knowledge, understanding and the application of the fundamental concepts of basic database systems.
o Demonstrate knowledge, understanding and the application of the fundamental concepts in data modelling and database schemas
o Demonstrate knowledge, understanding and the application of the fundamental concepts of SQL queries to manage a relational database including Create, Insert, Select, Delete and Update.
o Demonstrate knowledge, understanding and the application of using a programming language to connect, manage and execute SQL queries.Skills
Application of Number, ICT, Improving Own Learning and Performance, Problem Solving.
Coursework
30%
Examination
30%
Practical
40%
Stage/Level
1
Credits
20
Module Code
CSC1023
Teaching Period
Spring
Duration
12 weeks
Fundamentals of Maths for Computing (20 credits)Fundamentals of Maths for Computing
Overview
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.Coursework
60%
Examination
0%
Practical
40%
Stage/Level
1
Credits
20
Module Code
CSC1026
Teaching Period
Autumn
Duration
12 weeks
Optional Modules
Introduction to Cyber Security (20 credits)Introduction to Cyber Security
Overview
• Foundational concepts – objectives of cyber security
• Principles - Saltzer and Schroeder principles, NIST principles etc.
• Attack Types, Threats and Vulnerabilities
• Confidentiality, integrity, and availability
• Authentication, Access Control, and Accounting
• Security policy
• Human threats/social engineering
• Secure information system design – security architecture and lifecycle
• System protection technologies and countermeasuresLearning Outcomes
• Demonstrate knowledge and understanding of the principles of cyber security.
• Identify and analyse the current threats and challenges to the security of information systems, data, and services.
• Evaluate system protection technologies and methods.
• Demonstrate knowledge and understanding of the design of a system from a security perspective.Skills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
1
Credits
20
Module Code
CSC1032
Teaching Period
Spring
Duration
12 weeks
Programming (20 credits)Programming
Overview
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.Coursework
50%
Examination
20%
Practical
30%
Stage/Level
1
Credits
20
Module Code
CSC1027
Teaching Period
Autumn
Duration
12 weeks
Software Design Principles (20 credits)Software Design Principles
Overview
The module will introduce the fundamentals of a good and secure software design i.e., software that applies fundamental design and cuber-security principles. The module offers not only the necessary theoretical foundation but also the required practical examples/exercises, along with real-world projects. The students will develop their projects via working in small teams and by following a well-defined software-engineering process. The process will start with the specification of the software requirements, will continue with the specification of the proper software design that meets the requirements, and will end with software testing to satisfy the specified requirements. The developed software will follow the object-oriented paradigm (esp., the java language will be used for the software development).
Learning Outcomes
Students must be able to demonstrate knowledge in:
- specifying software requirements by using UML language,
- employing object-oriented principles
-applying design and cyber-security principles
-specifying software design by using UML language
- employing principles of package design
-applying software testing to satisfy the specified requirements.Skills
KNOWLEDGE & UNDERSTANDING: Understand fundamental theories of software design and the importance of analysing a problem domain for a specific target audience.
INTELLECTUAL AND PRACTICAL:
• Be able to design and develop solution within a collaborative team to meet simple the requirements as expressed through the project.Coursework
100%
Examination
0%
Practical
0%
Stage/Level
1
Credits
20
Module Code
CSC1031
Teaching Period
Spring
Duration
12 weeks
Web Technologies (20 credits)Web Technologies
Overview
In this module, you will learn how the three key technologies of HTML, CSS and JavaScript combine to provide client-side web applications. In particular, you will look at designing relevant content, how to present this content and how to integrate interactive features using dynamic behaviours. You will also learn how to test and debug web pages across multiple browser platforms and ensure your pages conform to accessibility standards and relevant legislation. As part of your consideration for multiple browser platforms you will cover a range of web design principles for mobile web applications.
Learning Outcomes
On completion of this module, students will be able to:
• Demonstrate knowledge, understanding and the application of the principles and application of the three key technologies of HTML, CSS and JavaScript, to include:
o Applying the latest version of HTML to create fully compliant web pages
o Applying the latest version of CSS to control the layout and appearance of web pages across multiple browsers
o Applying JavaScript to add dynamic behaviour and interactive elements to web pages
• Demonstrate knowledge, understanding and the application of the principles and application of appropriate tools to test and debug web sites
• Demonstrate knowledge and understanding of the principles of web accessibility.Coursework
100%
Examination
0%
Practical
0%
Stage/Level
1
Credits
20
Module Code
CSC1030
Teaching Period
Spring
Duration
12 weeks
Object Oriented Programming (20 credits)Object Oriented Programming
Overview
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.Coursework
50%
Examination
20%
Practical
30%
Stage/Level
1
Credits
20
Module Code
CSC1029
Teaching Period
Spring
Duration
12 weeks
Computer Science Challenges (20 credits)Computer Science Challenges
Overview
Computer Science Challenges provides an opportunity for students to engage with practical and challenging problems in Computer Science. The course will offer a range of topical areas in Computer Science such as, data science, machine learning, cybersecurity or computing systems that students can select a project within. The choice of topical areas will depend on availability of resources. Through developing a project within one of the set areas, students will advance their programming experience, including the use of tools and technologies such as, version control, testing and secure and efficient programming.
Learning Outcomes
Students must be able to:
- demonstrate knowledge of the topical areas and be able to apply this knowledge in a software project.
Additionally, students must be able to demonstrate the application of:
• Professional programming standards: clean code techniques, commenting and code review.
• Tools: appropriate integrated development environments (IDE) for the specific tasks, version control, debugging tools and static analysis tools.
• Program design: modular decomposition and functional decomposition.
• Professional programming practices: error handling, input validation, use of standard libraries and secure programming considerations.Skills
Self learning, perseverance, debugging, user focused development, self promotion.
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
1
Credits
20
Module Code
CSC1028
Teaching Period
Spring
Duration
12 weeks
Procedural Programming (20 credits)Procedural Programming
Overview
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.Coursework
60%
Examination
0%
Practical
40%
Stage/Level
1
Credits
20
Module Code
CSC1025
Teaching Period
Autumn
Duration
12 weeks
- Year 2
Core Modules
Professional and Transferrable Skills (20 credits)Professional and Transferrable Skills
Overview
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.
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
2
Credits
20
Module Code
CSC2065
Teaching Period
Autumn
Duration
12 weeks
Data Structures and Algorithms (20 credits)Data Structures and Algorithms
Overview
• 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 implementationLearning 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.
Coursework
50%
Examination
0%
Practical
50%
Stage/Level
2
Credits
20
Module Code
CSC2059
Teaching Period
Autumn
Duration
12 weeks
Theory of Computation (20 credits)Theory of Computation
Overview
• Automata and Formal Languages
• Computability Theory (Turing Machines etc) and Decidability Theory (Halting Problem, etc)
• Complexity TheoryLearning Outcomes
• Explain how computation can occur using automata such as finite state machines and Turing machines.
• Reason about algorithmic complexity and determine what problems can/cannot be solved by computers.
• Describe the correspondence amongst Languages and Automata etc.
• Use proof techniques to construct simple proofs.Skills
Problem analysis, Problem solving. Precision and conciseness of expression. Rigour in thought. Constructing logical arguments and proofs.
Coursework
40%
Examination
60%
Practical
0%
Stage/Level
2
Credits
20
Module Code
CSC2060
Teaching Period
Spring
Duration
12 weeks
Software Engineering and Systems Development (40 credits)Software Engineering and Systems Development
Overview
• 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.
• 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 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.
• Object-oriented design principles: evaluating the quality of a software design; questions of coupling and cohesion; configuring mechanisms of collaborating software objects.
• GUI 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);
• 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, 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).
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
2
Credits
40
Module Code
CSC2058
Teaching Period
Full Year
Duration
24 weeks
Optional Modules
Networks and Protocols (20 credits)Networks and Protocols
Overview
• 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 ResourcesLearning 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
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
2
Credits
20
Module Code
CSC2066
Teaching Period
Spring
Duration
12 weeks
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 detectionLearning 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.
Coursework
60%
Examination
0%
Practical
40%
Stage/Level
2
Credits
20
Module Code
CSC2062
Teaching Period
Spring
Duration
12 weeks
Systems Security and Cryptography (20 credits)Systems Security and Cryptography
Overview
This module will introduce fundamental concepts in cyber security, including vulnerabilities, threats and attacks, principles of secure design, and cryptography. 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
• Introduction to modern cryptography
o Confidentiality, integrity and availability
o Symmetric cryptography and Public key cryptography
o Authentication 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 countermeasuresLearning 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 confidentiality, integrity, and authentication.Skills
Problem solving, communication skills, time management, practical skills (including a base understanding of cryptography and challenges in cyber security).
Coursework
80%
Examination
20%
Practical
0%
Stage/Level
2
Credits
20
Module Code
CSC2056
Teaching Period
Spring
Duration
12 weeks
- Year 3
Core Modules
Formal Methods (20 credits)Formal Methods
Overview
A rigorous approach to software development. Logical foundations. Specification of data types. Implicit and direct specification of functions and operations. Reasoning about specifications, refinement, axiomatic semantics.
Learning Outcomes
To present a scientific approach to the construction of software systems.
Skills
Precision and conciseness of expression. Rigour in thought.
Coursework
30%
Examination
70%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3001
Teaching Period
Spring
Duration
12 weeks
Optional Modules
Cloud Computing (20 credits)Cloud Computing
Overview
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 toolsCoursework
100%
Examination
0%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3065
Teaching Period
Autumn
Duration
12 weeks
Deep Learning (20 credits)Deep Learning
Overview
• Overview of generic machine learning pipelines
• Deep learning
o Feedforward neural networks
o Regularisation for deep learning
o Optimisation for training deep models
o Convolutional networks
o Auto-encoders
o Recurrent Networks
o Siamese Neural Network
• Evolving learned models
o Active Learning
o Transfer Learning
o Incremental Learning
• Applications of deep learningLearning Outcomes
Be able to:
• Explain when and how machine learning is useful in industry, public institutions and research.
• Know and apply state-of-art deep learning techniques.
• Demonstrate the ability to understand and describe the underlying mathematical framework behind these operations.
• Design and develop original deep learning pipelines applied to a variety of problems
• Formulate and evaluate novel hypothesis
• Analyse an application problem, considering its suitability for applying deep learning, and propose a sensible solution
• Evaluate the performance of proposed deep 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 state-of-art solutionsSkills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Coursework
60%
Examination
40%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3066
Teaching Period
Spring
Duration
12 weeks
Video Analytics and Machine Learning (20 credits)Video Analytics and Machine Learning
Overview
• 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 solutionsSkills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Coursework
40%
Examination
60%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3067
Teaching Period
Autumn
Duration
12 weeks
Network Security (20 credits)Network Security
Overview
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 ProtocolsLearning 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.
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3064
Teaching Period
Spring
Duration
12 weeks
Malware Analysis (20 credits)Malware Analysis
Overview
• Basic Static Techniques
• 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 detectionLearning Outcomes
Students should be able to:
- Ability to perform basic and advanced static analysis
- Ability to perform basic dynamic analysis
- Understand the different types of malware and understand their behaviour
- Understand how automated malware detection worksSkills
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.
Coursework
0%
Examination
40%
Practical
60%
Stage/Level
3
Credits
20
Module Code
CSC3059
Teaching Period
Spring
Duration
12 weeks
Software Testing (20 credits)Software Testing
Overview
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.
Coursework
60%
Examination
0%
Practical
40%
Stage/Level
3
Credits
20
Module Code
CSC3056
Teaching Period
Spring
Duration
12 weeks
Advanced Computer Architecture (20 credits)Advanced Computer Architecture
Overview
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 coursesLearning 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.Coursework
60%
Examination
0%
Practical
40%
Stage/Level
3
Credits
20
Module Code
CSC3058
Teaching Period
Autumn
Duration
12 weeks
Concurrent Programming (20 credits)Concurrent Programming
Overview
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.
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
3
Credits
20
Module Code
CSC3021
Teaching Period
Autumn
Duration
12 weeks
- Year 4
Core Modules
Research and Development Project (40 credits)Research and Development Project
Overview
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.
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
4
Credits
40
Module Code
CSC4006
Teaching Period
Full Year
Duration
24 weeks
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 literatureSkills
Knowledge of opportunity analyses, business modelling, and commercial delivery of software against a created set of requirements
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
4
Credits
20
Module Code
CSC4008
Teaching Period
Autumn
Duration
12 weeks
Optional Modules
Parallel and Distributed Computing (20 credits)Parallel and Distributed Computing
Overview
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 methodsLearning 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 architecturesSkills
Improving Own Learning and Performance, Problem Solving, planning and researching assignments, design and implementation of solutions
Coursework
100%
Examination
0%
Practical
0%
Stage/Level
4
Credits
20
Module Code
CSC4010
Teaching Period
Autumn
Duration
12 weeks
Fairness, Interpretability and Privacy in Machine Learning
Overview
• Ethics of Data-Driven Systems: Fairness, Bias, Privacy, Transparency, Interpretability
• Machine Learning and Fairness: How ML algorithms could make unfair decisions
• Sources of Discrimination:
o Data Sampling
o Unbalanced datasets
o Tainted Features
o Feature Proxies
• Fairness Testing
o Testing Systems for Fairness: How can we test and see whether a system is fair?
o Examples of targeted tests, quick tests and adversarial tests.
• Fairness in classification:
o Fair Classification Algorithms
o Fairness in Deep learning: Mechanisms to enhance fairness in deep learning
• Fairness in unsupervised learning and clustering
o Measuring Representational Fairness in Clustering Outputs
o Fair Clustering
• Fairness in Representation Learning
o The notion of representational fairness
o Fair Principal Component Analysis
o Fairness through Projections
o Fair Word Embeddings and Representation Learning for the Text Domain
o Fairness in Search and Retrieval in Databases
• Ensuring privacy in ML
o Reverse engineering
o Encryption and anonymization
o Differential privacy
o Applying differential privacy in ML and DL
• Understanding Machine Learning
o ML and Deep learning as black boxes
o Global and local explainable models
o White box and black box testing
o Explaining Supervised Deep Neural networks: Visualization Methods
o Explaining unsupervised DL: Visualization and other Method
• Attacking and compromising ML systems
o Adversarial attacks: poisoning, evasion, etc…
o Enhancing robustness through Adversarial learningLearning Outcomes
Be able to:
• Understand the complex regulatory and moral implications of using machine learning in industry, public institutions and research.
• Understand the risks of carelessly use ML in the society.
• Know and apply a range of fairness mechanisms to improve fair decision making in machine learning techniques.
• Know and apply a range of interpretability mechanisms to fully understand and explain both the global ML models and their individual predictions
• Know and apply a range of privacy preserving mechanism to avoid compromising private data in ML
• Demonstrate the ability to understand and describe the underlying mathematical framework behind these mechanisms.
• Design and develop fair, privacy-preserving and explainable machine learning pipelines
• Evaluate the performance of proposed machine learning enhancing solutions through rigorous testing and experimentation
• Communicate findings and decision making processes effectively and in a convincing manner based on dataSkills
Problem solving. Self and independent learning. Research. Working with others and organisational skills. Critical analysis. Quantitative evaluation. Mathematical and logical thinking.
Coursework
60%
Examination
40%
Practical
0%
Stage/Level
4
Credits
20
Module Code
CSC4009
Teaching Period
Spring
Duration
12 weeks
Advanced Computer Engineering (20 credits)Advanced Computer Engineering
Overview
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 optimisationLearning 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 optimisationSkills
Assimilation of technical material
Critical thought in the design of resource-constrained computer designed problems
Application to practical data processing design examplesCoursework
100%
Examination
0%
Practical
0%
Stage/Level
4
Credits
20
Module Code
ECS4003
Teaching Period
Full Year
Duration
24 weeks
Algorithms: Analysis and Application (20 credits)Algorithms: Analysis and Application
Overview
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.
Coursework
30%
Examination
70%
Practical
0%
Stage/Level
4
Credits
20
Module Code
CSC4003
Teaching Period
Spring
Duration
12 weeks
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Entry Requirements
Entrance requirements
A-level 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 BSc degree if they are not eligible for entry to the MEng degree both at initial offer making stage and when results are received. |
Selection Criteria
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.
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 BSc 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 BSc course, provided that they satisfy the normal entry requirements for admission to the BSc 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.
- Foundation
The INTO progression course suited to this programme is
http://www.intostudy.com/en-gb/universities/queens-university-belfast/courses/international-foundation-in-engineering-and-science. - International Year One
The INTO progression course suited to this programme is
https://www.intostudy.com/en-gb/universities/queens-university-belfast/courses/international-year-one-in-engineering.
INTO - English Language Course(QSIS ELEMENT IS EMPTY)
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Modules
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Careers
Career Prospects
Introduction
Studying for a Computer Science 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, Liberty IT and Asidua who provide sponsorship for our Computer Science degree as well as Citi and Kainos who are members of the employer liaison panel for the course.
Employer 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.
You should also take a look at www.prospects.ac.uk for further information concerning the types of jobs that attract Computer Science Graduates.
Further study is also an option open to Computer Science graduates. Students can choose from a wide range of Masters programmes as well as a comprehensive list of research topics, see the School website www.qub.ac.uk/eeecs for more information.
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 Computer Science degree from Queen's is a highly respected qualification. A good Honours degree in Computer Science from Queen's is of great benefit in seeking the best jobs.
Employers, from large multinational firms to small local organisations, actively target our students, recognising that Queen's Computer Science 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 Computer Science graduates include: Software Engineer; Systems Analyst; Web Designer; Games Developer; Systems Developer; IT Consultant; Project Manager.
Other Career-related information
Queen’s is a member of the Russell Group and, therefore, one of the 20 universities most-targeted by leading graduate employers. Queen’s students will be advised and guided about career choice and, through the Degree Plusinitiative, will have an opportunity to seek accreditation for skills development and experience gained through the wide range of extra-curricular activities on offer. See Queen’s University Belfast fullEmployability Statementfor further information.
Degree Plus and other related initiatives
Recognising student diversity, as well as promoting employability enhancements and other interests, is part of the developmental experience at Queen’s. Students are encouraged to plan and build their own, personal skill and experiential profile through a range of activities including; recognised Queen’s Certificates, placements and other work experiences (at home or overseas), Erasmus study options elsewhere in Europe, learning development opportunities and involvement in wider university life through activities, such as clubs, societies, and sports.
Queen’s actively encourages this type of activity by offering students an additional qualification, the Degree Plus Award (and the related Researcher Plus Award for PhD and MPhil students). Degree Plus accredits wider experiential and skill development gained through extra-curricular activities that promote the enhancement of academic, career management, personal and employability skills in a variety of contexts. As part of the Award, students are also trained on how to reflect on the experience(s) and make the link between academic achievement, extracurricular activities, transferable skills and graduate employment. Participating students will also be trained in how to reflect on their skills and experiences and can gain an understanding of how to articulate the significance of these to others, e.g. employers.
Overall, these initiatives, and Degree Plus in particular, reward the energy, drive, determination and enthusiasm shown by students engaging in activities over-and-above the requirements of their academic studies. These qualities are amongst those valued highly by graduate employers.
www.prospects.ac.uk
Employment after the Course
Studying for a Mathematics and Computer Science degree at Queen’s will assist students 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 many employers (local, national and international) and over half of all graduate jobs are now open to graduates of any discipline, including mathematics.
Although the many of our graduates are interested in pursuing careers in teaching, banking and finance, significant numbers develop careers in a wide range of other sectors. The following is a list of the major career sectors that have attracted our graduates in recent years:
Management Consultancy
Export Marketing (NI Programme)
Fast Stream Civil Service
Varied graduate programmes (Times Top 100 Graduate Recruiters/AGR, Association of Graduate Recruiters UK)
Employment Links
The School has links with over 500 IT companies, which benefits our students on many levels through providing industrial input into our degree content, summer and year-long placements and competitions organised by the companies.
What employers say
“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
Additional Awards Gained(QSIS ELEMENT IS EMPTY)
Prizes and Awards(QSIS ELEMENT IS EMPTY)
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.
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Fees and Funding
Tuition Fees
Northern Ireland (NI) 1 | £4,710 |
Republic of Ireland (ROI) 2 | £4,710 |
England, Scotland or Wales (GB) 1 | £9,250 |
EU Other 3 | £23,100 |
International | £23,100 |
1 EU 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.
All tuition fees quoted relate to a single year of study and will be subject to an annual inflationary increase, 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
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. A programme may have up to 6 modules per year, each with a recommended text.
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 final year 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.
Computer Science costs
Students may wish to become a student member of BCS - The Chartered Institute for IT - at an annual cost of £20, or £30 for four years (subject to change).
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.
Information on funding options and financial assistance for undergraduate students is available at www.qub.ac.uk/Study/Undergraduate/Fees-and-scholarships/.
Scholarships
Each year, we offer a range of scholarships and prizes for new students. Information on scholarships available.
International Scholarships
Information on scholarships for international students, is available at www.qub.ac.uk/Study/international-students/international-scholarships/.
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Apply
How and when to Apply
How to Apply
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 2024 from 1 September 2023.
Advisory closing date: 31 January 2024 (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 2024) subject to the availability of places.
Applications from International and EU (Other) students are normally considered by Queen’s for entry to this course until 30 June 2024. If you apply for 2024 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.
Further information on applying to study at Queen's is available at: www.qub.ac.uk/Study/Undergraduate/How-to-apply/
Terms and Conditions
The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study. Queen's University Belfast Terms and Conditions.
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 2024. - 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.
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Fees and Funding