MSc Cyber Security and Big Data degree

Entry requirements
2:2 +
Full-time
1 year
Part-time
Up to 4 years
Start date
September 2020
UK / EU fee
£10,900
International fee
£25,500
Location
London
Application status
Open

Overview

We are increasingly surrounded by high tech systems like cloud computing, smart and wearable devices, mobile network technologies, and artificial intelligence, all of which enable us to keep connected and carry out our daily activities with great ease. That's why our master's in Cyber Security and Big Data is designed to help develop the cyber professionals of the future.

As we become increasingly more vulnerable to cyber-attacks that affect individuals and organisations targeted by the shortcomings of these technologies and exploiting our reliance on those. A recent boom in ransomware cases is only one example, which can cause significant financial and other damages to individuals and corporations. The traditional cyber security mechanisms are becoming less effective in coping with the ever changing cyberattacks.

To compete with the advanced cyber threats, security industries and professionals are now seeking to leverage artificial intelligent and big data analytics technologies to detect and prevent unseen malwares and emerging cyber threats. Our master's in Cyber Security and Big Data has been developed to master the use of big data analytics in cyber security. This master's degree in Cyber Security and Big Data aims to provide you with the principles of deep learning and neural networks for cyber threat mitigation, advanced encryption techniques to protect the data privacy, enabling technologies such as cloud, Internet of Things and digital forensics to investigate the aftermath of cyberattacks.

The core aim of our cyber security master's degree is to develop the employment skills which are essential to Security Operational Centres, Antivirus Software Companies, Artificial Intelligent Start-ups, e-commerce companies, and governmental organisations.

Who should study this programme?

 Our cyber security course is appropriate for:

  • Individuals with a technology background and who want to become experts in cyber security and artificial intelligence
  • Individuals with an engineering background and an entrepreneurial mindset who want to start their own business in the domain of threat detection and prevention using data analytics
  • Security professionals who want to advance their skills in data analytics based cyber defence

Why you should choose us

Why you should study this degree

Institute For Digital Technologies

Hear from Sanjeev about studying within the Institute for Digital Technologies and what postgraduate life is like at Loughborough University London.

What makes this programme different?

  • This is a unique program that offers tools and skills, which are highly sought after by the job market in the interface of artificial intelligent, data science and cybersecurity
  • You will become an expert in deep learning and neural network to mitigate the cyberthreats
  • You will study the enabling technologies such as Internet of Things and cloud systems and learn how they contribute to data analytics in mitigating the advanced cyber threats
  • You will develop key digital competencies and skills necessary for the job market while studying in London, Europe’s top city for digital entrepreneurs based on the European Digital City Index 2016

What you'll study

You will learn from the most influential thought leaders, pioneering researchers and creative innovators, exposing you to the latest theories and developments from across your discipline.

Modules

Our Cyber Security and Big Data MSc covers a wide range of topics; to give you a taster we have expanded on some of the modules affiliated with this programme and the specific assessment methods associated with each module.

Students must choose and complete five of the eight optional modules to complete the MSc Cyber Security and Big Data. Two of these modules must be completed in Semester One, and three in Semester Two.

The following information is intended as an example only and is based on module information for the 2019/20 year of entry. Modules are reviewed on an annual basis and may be subject to future changes. Updated Programme and Module Specifications are made available ahead of each academic year. Please see Terms and Conditions of Study for more information.

Collaborative Project

With a multi-talented group of students you will work on a brief from a real company looking to solve a real social or business problem.

Together with your student team, you will research and build solutions to a business problem, supported by our project tutors, clients and staff. Previous clients include Foster + Partners, Speedo, The London Legacy Development Corporation as well as many other companies, start-ups and charities.

The Collaborative Project provides a means for you to engage in critical enquiry and to be exposed to project-based teamwork in multicultural and interdisciplinary settings. By undertaking this module, you will strengthen your cooperative and collaborative working skills and competencies, whilst raising your awareness and appreciation of cultural and disciplinary diversity and differences.

The Collaborative Project aims to provide you with a hands-on experience of identifying, framing and resolving practice-oriented and real-world based challenges and problems, using creativity and appropriate tools to achieve valuable and relevant solutions. Alongside the collaborative elements of the module, you will be provided with opportunities to network with stakeholders, organisations and corporations, which will give you the experience and skills needed to connect to relevant parties and potentially develop future employment opportunities.

Learning outcomes

On completion of this module, you will be able to:

  • Work effectively in diverse and interdisciplinary teams
  • Undertake and contribute towards a project-based development process
  • Apply critical enquiry, reflection, and creative methods to identify, frame, and resolve issues and problems at hand
  • Identify user and stakeholder needs and value creation opportunities, whilst collecting and applying evidence-based information and knowledge to develop appropriate insights, practices and solutions
  • Identify, structure, reflect on key issues and propose solutions to problems in creative ways
  • Enhance your appreciation for diversity and divergent individual and disciplinary perspectives
  • Be able to provide structured, reflective and critical feedback to peers and other stakeholders
  • Plan and execute a project plan including scope, resources and timing
  • Effectively communicate ideas, methods and results to a diverse range of stakeholders
  • Use multiple, state-of-the-art date media and technologies to communicate with collaborators
  • Make informed, critical and reflective decisions in time-limited situations.

Assessment

100% Coursework consisting of:

  • Peer evaluation (5%)
  • Group project report (40%)
  • Individual essay (55%)

Principles of Data Science

The aim of this module is to introduce students to the concepts of data science and their use in Data Analytics Systems. Students will also be able to gain theoretical and practical experience in simulating complex data systems involved in a variety of industries including, smart digital systems, Internet of Things, financial industries, and entertainment industries.

Learning outcomes

On completion of this module, you should be able to:

  • Critical awareness of the challenges caused by the proliferation of data generation processes
  • Systematic understanding of the process of extraction of actionable knowledge from data to enable decision making
  • Theoretical background in descriptive and inferential statistics for big data
  • Machine learning algorithms for classification and pattern analysis in large data sets
  • Advanced analytical techniques, such as model building, network graph analysis, outlier detection
  • Methods for maximising predictive performance of algorithms and validation techniques
  • Analyse common summary statistics and use statistical tests to determine confidence for a hypothesis
  • Demonstrate ability to fit a distribution to a dataset and use that distribution to predict event likelihoods
  • Examine and evaluate the capabilities of available classification algorithms and be able to select and use suitable for a particular data set
  • Integration of knowledge to critically evaluate different scenarios/problems and design practical solutions to data related problems
  • Application of knowledge to through programming skills to build predictive and descriptive models for a given dataset utilising available labelled data sets
  • Apply creativity and problem solving skills in the industry/research for challenging problems in a timely manner
  • Communicate complex problems and associated solutions to specialist and non-specialist audiences
  • Evaluate problems and design solutions to those problems through scholarship gained through self-directed study.

Assessment

  • In class test (30%)
  • Group project report (70%)

Applied Cryptography

This module will cover modern cryptographic algorithms and mechanisms for cyber security with emphasis on the applications and engineering implementations.

The first part covers some theoretical foundations of cryptography, cryptographic building blocks as well as the basic, intermediate and advanced protocols.

The second part is about cryptographic techniques including key and its management, algorithm types and modes.

The third part covers cryptographic algorithms which are widely used in the network and security industry, including various ciphers such as block ciphers (DES, AES, RC2, Blowfish, etc.) and stream ciphers (A5, RC4, SEAL, and cascading multiple stream ciphers), one-way hash functions, (MD2, MD5, SHA), public-key algorithms (RSA, ElGamal, Elliptic Curve), digital signature and key exchange algorithms.

The fourth part covers the applications and implementations of selected algorithms and protocols to address security issues in data and security service industry in the real world, such as the Blockchain technology and its applications.

Learning outcomes

On completion of this module, you should be able to:

  • Demonstrate knowledge of different cryptographic protocols, techniques, algorithms and implementations that are widely used in protection of confidentiality, integrity, authentication and non-repudiation, and be able to use their knowledge to address real-world security issues
  • Fundamentals of security including privacy, integrity, authentication and non-repudiation in internet-connected world
  • Concepts, protocols and algorithms of modern cryptographic mechanisms
  • Implementations and applications of cryptography in cyber security
  • Be able to apply gained knowledge in cryptography in protection of data and user security for real world scenarios
  • Critically analyse detailed cryptographic mechanisms for weakness and potential threats pertaining to big data systems
  • Be able to analyse the cryptographic requirements for real security issues in data systems
  • Apply gained knowledge in cryptographic protocols, algorithms and mechanisms in addressing the security concerns of data systems
  • Demonstrate gained knowledge in cryptography in security applications & APIs
  • Apply their critical analysis and problem solving skills in the industry for tackling problems and providing solutions for both cyber security and big data services
  • Ability to look at things in sufficient detail with critical thinking
  • Demonstrable competitiveness in data security protection
  • Build confidence in research, development, implementation and maintenance of advanced cyber security systems.

Assessment

  • Project presentation (10%)
  • Final coursework report (30%)
  • In-Class test (60%)

Dissertation

The Dissertation module will equip you with the relevant skills, knowledge and understanding to embark on your own research project.

You will have the choice of three dissertation pathways:

  1. A desk based research project that could be set by an organisation or could be a subject of the student's choice
  2. A project that involves collection of primary data from within an organisation or based on lab and/or field experiments
  3. A professional placement within an organisation during which time students will complete a project as part of their role in agreement with the organisation (subject to a suitable placement position being obtained)

By undertaking a dissertation at master's level, you will achieve a high level of understanding in your chosen subject area and will produce a written thesis or project report which will discuss your research in depth and with rigour.

Learning Outcomes

On successful completion of this module, you should be able to demonstrate knowledge and understanding of:

  • The importance of project planning
  • The importance of a clear hypothesis or research question
  • The ethical implications of research
  • The relevant empirical data and methodologies for data collection or knowledge assimilation for the subject area
  • Methods of data analysis and their suitability for the intended data
  • The areas of expertise or publications of the major individuals or organisations in the subject or business area
  • The previous research or current knowledge in the specific subject or business area
  • Theoretical perspectives relevant to your chosen topic
  • The most effective methods of presentation for data or knowledge
  • Developing a clear, coherent and original research question, hypothesis or business problem in a suitable subject area
  • Synthesising relevant sources (e.g. research literature, primary data) to construct a coherent argument in response to your research question, hypothesis or business problem
  • Analysing primary or secondary data collected by an appropriate method
  • Critically evaluating data collected in context with previously published knowledge or information
  • Engaging in critical debate and argumentation in written work
  • Applying principles of good scholarly practice to your written work
  • Performing appropriate literature searching/business information searching using library databases or other reputable sources
  • Planning a research project and producing a realistic gantt chart demonstrating your intended timelines
  • Synthesising information from appropriate sources
  • Demonstrating rational use of research method tools
  • Selecting and using appropriate investigative and research skills
  • Demonstrating effective project planning skills
  • Finding and evaluating scholarly sources
  • Engaging in critical reasoning, debate and argumentation
  • Demonstrating effective report writing skills
  • Recognising and using resources effectively
  • Successfully managing a project from idea to completion
  • Demonstrating commercial awareness or the impact of knowledge transfer in a business or research environment.

Assesement

100% coursework consisting of:

  • Research proposal (10%)
  • Dissertation report/essay (90%)

Semester one

Information Management

This module provides an introduction to information management concepts and frameworks, including:

  • Ethics data management, governance to establish a system of decision rights over data
  • Architecture, modelling, and design to discover, analyse, represent and communicate data requirements
  • Data storage and operation
  • Information security management
  • Data integration and interoperability between data stores, applications and organisations
  • Document and content management
  • Business intelligence to enable workers to get value from information
  • Information quality management
  • Introduction to big data systems and related technologies

Students will be provided with the fundamentals of Internet and communication systems to understand data management systems architecture and be introduce them to the important topics related to information management such as ethics, governance, storage, architecture, interoperability, quality, security and big data.

This module aims to study the information management principles, practices and technologies in different organisations to manage rapidly expanding data assets for business intelligence while complying with data regulations.

Learning Outcomes

On completion of this module students will have gained knowledge of information systems with communication networks and should be able analyse an organisation's data asset to develop strategies to increase its value, protect the data asset from third-parties via access controls, data modelling and design and comply with data regulations.

In addition, students will also be able to:

  • Identify best practices in information management
  • Understand business intelligence activities to achieve maximum benefit from information
  • Apply context of data management activities
  • Identify emerging trend in information storage techniques to manage rapidly growing data assets
  • Highlight key elements and operations of Internet and communication networks that enable data management
  • Demonstrate underpinning concepts of information management
  • Apply best practices in data management to comply with data regulations and ethics
  • Familiarise with technical concepts such big data and security management
  • Synthesise necessary information to evaluate data rates for different communication networks that affect the data quality
  • Develop customised data strategies for organisations to maximise the value of information
  • Critically evaluate the existing information management strategies to improve the data usability, comply with regulations and ethics
  • Critically analyse the drawbacks of legacy communication systems that hinder data management
  • Advise an organisation to develop effective information management plans using the latest technologies.

Assessments

  • In-class test (35%)
  • Exam (65%)

Cybersecurity and Forensics

This module will cover cybersecurity challenges, threat landscape, the principles of digital and cyber forensics methodologies, as well as the processes required to investigate cyber-attacks and cybercrime in networks, applications, and devices. You will be introduced to various tools and software packages used for digital evidence collection and processing, crime reconstruction, malware analysis and intrusion investigation.
 
This module aims to develop students' knowledge and understanding of cybersecurity incidents and processes required for the digital investigation involved aftermath of cyberattacks and cybercrimes.

Learning outcomes

On completion of this module, students should be able to:
  • Develop a critical understanding of the main concepts related to Cybersecurity incidents, Forensics Analysis, Electronic Discovery, Crime Reconstruction, and Intrusion Investigation
  • Examine soundness and fundamentals of forensics analysis, scientific methods, data abstraction layers, evidence dynamics, and identity of source
  • Understand the investigative methodologies, applying scientific methods for digital investigation, data gathering and observation, and crime reconstruction
  • Study about digital evidence collection, data processing and electronic discovery
  • Understand the methodologies associated with intrusion investigation, attribute tracking, and IDS alerts
  • Understanding of network boundaries, viewpoints of cyberattack models, perception of methods for containing incidents, and forensics analyst capabilities of investigating endpoint devices
  • Apply gained knowledge in forensics methodologies used in investigations involving Internet, web-based applications, and Application Programme Interfaces (APIs), smartphones, IoT devices, small, medium and large networks
  • Apply acquired knowledge when working in industry, particularly in sectors that are closely related to Internet.

Assessment

100% coursework consisting of:

  • Coursework Proposal (30%)
  • Final Coursework Report (70%)

Digital Application Development

In this module Python programming tools and environments will be covered in combination with the understanding of programming in C which is a fundamental tool for developing digital systems and applications.

Specific topics covered include omputer programming, and advanced programming techniques such as data, structure, pointer, and simulation.

Learning Outcomes

On completion of this module you should be able to:

  • Demonstrate programming skills in C and Python
  • Demonstrate necessary programming techniques to develop digital applications and simulations
  • Understand programming structure and skills in general
  • How to use digital data processing tools and functions
  • Techniques used in developing applications and related computer simulations
  • Understand the use and concept of advanced techniques of structure, and pointer
  • Identify, utilise and optimise tools, algorithms and functions for simulation of digital applications
  • Implement functions and algorithms in the programming languages
  • Develop digital media applications and simulations
  • Solve other complex problems involving data processing algorithms
  • Find solutions for practical problems by reasoning, deduction and implementing from idea to final application
  • Abstract features from complex objects/problems and develop ideas into algorithms
  • Fulfil the research and development requirements in a range of data processing and digital technologies sectors.

Assessment

Assessment is made up of 1 x 1,700 word report (60%) and 1 x 1,300 word report (40%).

Semester two

Advanced Big Data Analytics

This module aims to introduce the concept of Big Data systems and the challenges posed by such systems, as welll as introduce the requirement of advanced analytics, processing techniques and architectural solutions to tackle the problems encountered.

Learning outcomes

On completion of this module you should be able to:

  • Describe the theoretical background of big data, and recognise the need for big data analytics
  • Have a critical awareness of machine learning algorithms for data analytics in big data systems
  • Describe distributed architectures of big data systems including database technologies used in industrial big data systems
  • Understand signal processing techniques for big data systems with advanced matrix manipulation
  • Appreciate various visualisation tools and techniques
  • Select and apply various machine learning algorithms to a given data set to interpret the data and make necessary predictions
  • Demonstrate ability to evaluate and select an appropriate database technology for a given need for big data storage and retrieval
  • Analyse the need for visualisation and employ appropriate visualisation tools
  • Critically analyse building blocks of a practical big data systems for performance improvement
  • Demonstrate programming skills related to data analytics and usage of associated tools
  • Formulate creative data solutions that start with cleaning up raw data sets, to discover new patterns that are underlying, to make necessary predictions, utilizing established state-of-the-art tools and techniques
  • Develop experimental, analytical and problem solving skills in data driven applications
  • Illustrate professional report writing, presentation and communication skills to communicate complex ideas to expert and non-expert audiences
  • Develop creative thinking skills to demonstrate ways of solving problems with existing tools.

Assessment

100% coursework consisting of:

  • Individual Theoretical Analysis (40%)
  • Group Technical Report / Simulations (60%)

Cloud Applications and Services

This modules aims to provide the students with an overview of the cloud technology with a special emphasis on media cloud applications and the associated challenges.

The module will give a brief overview of the cloud technology and covers media cloud applications and challenges, such as energy efficiency in cloud systems, mobile cloud computing, cloud multimedia rendering, streaming, coding, transcoding, caching, adaptation, etc. In addition, cloud networking, privacy and security issues in cloud services will also be covered.

Learning outcomes

On completion of this module students should be able to:

  • Develop an overview of the cloud technology, demonstrate specific knowledge in cloud applications and the challenges that are associated with making such applications available to the end-users via cloud technology
  • Understand the principles of cloud computing technology, media cloud applications, and the associated challenges
  • Understand cloud networking and related topics
  • Highlight privacy and security issues in cloud services
  • Have knowledge of cloud case studies and business models
  • Identify the requirements pertaining to the applications used in cloud services
  • Design cloud computing service solutions
  • Apply gained academic knowledge and experience in real world scenarios
  • Apply their critical analysis and problem solving skills in the industry for tackling problems and providing solutions for cloud applications and services
  • Demonstrate the necessary knowledge and skills required by R&D and services providers in the cloud computing and applications/services domain.

Assessment

  • Coursework (30%)
  • Exam (70%)

Media Processing

This module will cover audio and video processing technologies.

The first part includes video processing techniques, such as motion estimation, edge detection, histogram equalisation, segmentation, object detection. In particular, 3D video processing techniques will be outlined (depth image based rendering, estimation and processing) and scalable and multi-view coding schemes will also be covered.

The second part of the module includes audio signal capturing, adaptation, compression, enhancement, data analysis, sound control, noise cancellation, spatial audio, digital signal processing (DSP) and various coding techniques. The topics will be discussed with a view to enable efficient storage but more importantly for efficient and effective communication systems.

The aim of this module is to provide you with theoretical and practical knowledge on audio and video processing and coding techniques.

Learning outcomes

By the end of the module, students are expected to have gained knowledge in essential topics including: audio and video signal processing, coding techniques including scalable and multi-view aspects, as well as practical implications.

On completion of this module, you should be able to:

  • Image and video processing fundamentals, fundamentals of image and video compression and key standards, advanced formats, including 3D media
  • Fundamental theory and practice related to audio capturing, processing, and coding, sound control, noise cancellation
  • Analyse digital data and formulate a diagnosis in media processing
  • Comprehend the use of multimedia signals in systems, and effectively apply multimedia signal processing skills for the design of those systems
  • Design digital filters for audio and video signals
  • Design multimedia rendering and control systems
  • Analyse and evaluate research data
  • Make oral presentations and produce well-structured written work based on data collection and analysis
  • Work effectively in a group environment
  • Demonstrate problem-solving skills in digital media processing and coding
  • Demonstrate a logical and analytical skills in media processing.

Assessment

  • Coursework report (30%)
  • Exam (70%)

Internet of Things and Applications

The next stage in the Future Internet is to progressively evolve to a network interconnected with environments including objects. The Internet of Things (IoT) is involved in interaction and communication between objects and furthermore, with the environment to support decision making, improve situational awareness, increase operational efficiency and enable to explore new business models.

This module explores the emerging computing concepts and deployment of emerging IoT platforms and devices. The module will present the usage scenarios of communication and highly scalable consumption of data from geographically dispersed physical objects and sensors and the processing and delivery of such data to end-users. Sensing, tracking, monitoring, actuator, data & control service, data processing, information management, integration methodology, and M2M are among the other topics covered. Students will also be introduced to recent examples of smart cities & smart homes.

The aim of this module is to provide you with the knowledge and understanding of computing concepts related to the emerging IoT platforms and devices and their deployment.

Learning Outcomes

On completion of this module, you should be able to:

  • Demonstrate understanding of the main concepts into the usage scenarios of IoT communication and highly scalable consumption of data from geographically dispersed physical objects and sensors, as well as the processing and delivery of such data to end-users
  • Interaction and communication between objects and with the environment to support decision making, improve situational awareness, and increase operational efficiency
  • Modern applications of smart cities and smart homes
  • Understand the concepts pertaining to the IoT systems
  • Critically analyse and reflect on the limitations and problems faced in those systems and relate some possible solutions
  • Analyse the emerging IoT platforms and devices, and the associated technologies considered in their design
  • Distinguish the requirements pertaining to different contextual information collected and exploited within different IoT scenarios
  • Apply the acquired IoT knowledge in designing future intelligent systems, particularly in sectors that are closely related to smart cities, homes, & eHealth
  • Demonstrate the technologies and research capabilities in the smart systems & Internet technologies areas.

Assessment

  • Coursework (40%)
  • Exam (60%)

Information Systems Security

This module will cover the essential topics on information systems security properties (e.g., secrecy, integrity and availability), legal and ethical issues, mechanisms and protocols (authentication, access control types), PKI basics and risk management, etc. It further introduces the principles and key technologies of security in information systems, including security at transport level and system level, firewall and VPN concept and architecture, intrusion detection and prevention, security maintenance in information system applications (e.g., e-commerce, email, etc.). In order to develop their expertise in information security, students will be led to explore various security algorithms, tools and methodology based on common security architectures & APIs.

Learning Outcomes

On completion of this module students should be able to:

  • Demonstrate knowledge and skills of main concepts related to the information systems security properties, mechanisms, protocols, and applications that are widely in use in today's information systems, and be able to relate their knowledge and skills to security challenges in real-world scenarios for more secure information systems
  • Apply principles of security in information systems, concepts, models and architectures of available information security mechanisms
  • Identify security in information systems and applications, including legal and ethical issues
  • Analyse detailed concepts pertaining to the information security architectures and their use
  • Recognise limitations, and design possible solutions for existing security problems in information system applications, common security architectures & technologies
  • Demonstrate gained experience in security concept and management in information system applications, common security architectures & technologies
  • Build a capacity to apply acquired knowledge when working in industry, particularly in sectors that are closely related to Internet & information system security
  • Develop skills to recognise, analyse and solve challenging problems with attention to details
  • Present themselves in the area of research and development in Internet & information systems security to secure advanced level jobs.

Assessment

Assessment for this module is made up of an exam and a final report:

  • Exam (70%)
  • Report (30%)

Students must choose and complete 5 of the 8 optional modules to complete the MSc Cyber Security and Big Data. Two of these modules must be completed in Semester One and three in Semester Two.

For more information email: London@lboro.ac.uk

How you'll be assessed

You will complete a combination of written and practical assessments, which may vary depending on the module choices you make. You can expect to complete essays and reports of varying lengths, as well as presentations, proposals and pitches in some cases. For information about the assessments you will be expected to complete for each module, please see the module lists for this programme.

How you'll study

  • Lectures
  • Seminars
  • Tutorials
  • Independent study
  • Group work
  • Workshops
  • Practical sessions

Your personal and professional development

Loughborough University London prides itself on the high calibre of graduates it produces, and provides great opportunities for you to develop the skills and attributes you need to progress successfully in your chosen career.

Future career prospects

Graduates from our MSc Cyber Security and Big Data programme can expect to enter senior roles in a wide range of digital sectors and other businesses that rely on the Internet and cloud technologies, including but not limited to finance, communications, marketing, commerce, as well as government organisations and other sectors handling large volumes of sensitive and personal data.

Graduates will also have the opportunity to enhance their knowledge and career prospects further by undertaking an MPhil or PhD programme.

Your personal development

The careers and employability support on offer at Loughborough University London and has been carefully designed to give you the best possible chance of securing your dream role.

Loughborough University London is the first of its kind to develop a suite of careers-focused activities and support that is positioned as the underpinning of every student’s programme. Opportunities include employability assessments, group projects set by a real businesses and organisations, company site visits and organisation-based dissertation opportunities.

Entry requirements

Our entry requirements are listed using standard UK undergraduate degree classifications i.e. first-class honours, upper second-class honours and lower second-class honours. To learn the equivalent for your country, please choose it from the dropdown below.

Entry requirements for United Kingdom

A 2:2 honours degree (or equivalent international qualification) in electronics, computing, physics, mathematics or a related discipline.

Afghanistan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Masters 95% 85% 70%

Albania

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diplomë e Nivelit të Pare (First Level (University) Diploma (from 2010) 9.5 8.5 8

Algeria

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licence (4 year) / Diplome d'Inginieur d'Etat / Diplôme d'Etudes Supérieures 16 14 12

Argentina

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Argentina 8.5 7.5 6.0

Armenia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bakalavri Kochum 90% 80% 70%
Magistrosi Kochum 3.9 3.5 3.0

Australia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Honours degree (AQF level 8) First Class, 80% Upper Second, 70%, H2A Lower Second, 60%, 2B
Ordinary degree - AQF Level 7 pass (mark 46 or 50) High Distinction (80% or 85%) Distinction (75% or 80%) Distinction (70% or 75%)

Austria

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree/ Diplomstudium / Magister degree A (or 1.5) mit Auszeichnungbestanden 60% or B or 3.0 (or 2) 50% or C or 2.7 (or 3)

Azerbaijan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bakalavr Diplomu 4.5 4 3.5
Diplomu (Specialist Diploma) 90% 80% 70%

Bahamas

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree from University of the West Indies only 1st (GPA 3.6) 2:1 (GPA 3.0) 2:2 (GPA 2.5)

Bahrain

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.8

Bangladesh

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
BUET or 'Good Private' University - 4 year degree BUET - 1st (70%) / 3.5 BUET - 2nd (60%) / 3.0 BUET - 2nd (55%) / 2.75
Other universities - Masters (1-2 years) following a 3 or 4 year degree 80% / 4.0 65% / 3.25 50% / 2.5

Barbados

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Barbados - Degree from University of the West Indies only 1st (GPA 3.6) 2:1 (GPA 3.0) 2:2 (GPA 2.5)

Belarus

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Specialist Diploma (5Yr) 9 7 5

Belgium

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bachelor degree Magna Cum Laude Cum Laude 60%/12
Licenciaat 80% 70% 60%
Licencie 17 14 12

Belize

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree from University of the West Indies only 1st (GPA 3.6) 2:1 (GPA 3.0) 2:2 (GPA 2.5)

Benin

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Maitrise 18 15 or Bien 12 or Assez Bien

Bermuda

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree from University of the West Indies only 1st (GPA 3.6) 2:1 (GPA 3.0) 2:2 (GPA 2.5)

Bolivia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
A Licenciado, 4 years Private (public/private) 85/78 75/66 67/55

Bosnia and Herzegovina

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diploma Visokog Obrazovanja / Diplomirani 10 9 8

Botswana

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's degree A or 80% B or 70% C or 60%

Brazil

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Brazil - 4 yr Bacharel or Licenciado/Licenciatura or Título Profissional 8.5 (A) 7.5 6.0

Brunei

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Brunei First Upper Second (60%/B/3.1) Lower Second (50%/C/2.7)

Bulgaria

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
5 yr Diploma za Zavarsheno Visshe Obrazovanie (Diploma of Completed Higher Education) 6 5 4

Cambodia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 years 90% or 9 or 4.0 80% or 8 or 3.5 70% or 7 or 3.0

Cameroon

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bachelor degree or Diplome d'Etudes Superiures de Commerce 1st or 15 2:1 or 14 2:2 or 12.5
Diplome d'Ingenieur or Diplôme d'Ingénieur de Conception or a Maitrise or a 4 year Licence 20 or GPA 3.7 20 or Bien (GPA 3.4) 20 or Assez Bien (GPA 3.1)

Canada

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0/percentage scale 3.7/85% 3.3/75% 2.7/68%
Out of 9 8 6 5
Out of 12 10 8 6

Chile

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Grado de Licenciado / Título (Profesional) de [subject area] (4 years) 6 5.5 5

China

Students are required to have a bachelor degree (4 years) for entry to a postgraduate programme. The University uses the Shanghai Academic Ranking of World Universities to identify the required final mark, as outlined on the table below:

First class (70%) Mid 2:1 (65%) 2:1 (60%) Mid 2:2 (55%) 2:2 (50%)
Shanghai Rank Top 250 85% 81% 80% 78% 77%
Shanghai Rank 251-500 89% 84% 83% 81% 80%
Shanghai Rank 501+ 92% 87% 86% 85% 82%

Affiliated colleges

The University will consider students from Affiliated Colleges in the following way:

Applicants from colleges affiliated to universities in the top 250 Shanghai rankings will considered if they have achieved or are likely to achieve final marks of 80%-84%.

Applicants from colleges affiliated to universities which are 251-500 in the Shanghai rankings will considered if they have achieved or are likely to achieve final marks of 82%-87%.

Applicants from colleges affiliated to universities which are above 500 in the Shanghai rankings will considered as follows:

  • School of Business and Economics: not considered
  • All other programmes if they have achieved or are likely to achieve final marks of 82%-87%.

Universities given special consideration

Applicants from a small number of Chinese universities that specialise in business, management, finance or creative arts will be given special consideration by the University. The full list of these universities and the Shanghai band under which they will be considered can be found in the PDF below.

Download the list of Chinese universities given special consideration here

Students who do not meet the above requirements may occasionally be considered if they have a relevant degree, can show good grades in relevant subjects, and/or have substantial relevant work experience.

Colombia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciado / Título de [subject area] 4.5 3.75 3.2

Costa Rica

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciado 9 8 or 80 7 or 75

Croatia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Baccalaureus / Prvostupnik 4.5 3.8 3.0

Cuba

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4-year Titulo de Licenciado / Licenciatura 5 4 3

Cyprus

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Cyprus 8.5 7.0 6.5

Czech Republic

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bakalár (after 2001) 6 yr integrated Magistr 1 1.5 2

Denmark

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
5 year Candidatus/Candidata Magisterii or Bachelor degree (7 point scale) 12 10 7

Dominican Republic

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 year Licenciado 3.8 Magna Cum Laude 3.5 Cum Laude 3.2
Título de [subject area] - 85% 82%

Ecuador

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Título de Licenciado 8.5 8 7
Título de [subject area] 85% 80% 70%

Egypt

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Egypt 3.5 3.2 2.8
Universities only BA 90%, BSc 85% BA 80%, BSc 75% BA 65%, BSc 65%

El Salvador

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
5 year Licenciado 8.5 7.5 6.5
Título de Ingeniero 85% 75% 65%
Arquitecto - Muy Bueno Bueno

Estonia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bakalaureusekraad or Magister or Magistrikraad 5 or A 4 or B 3 or C

Ethiopia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's A/GPA 4.0 A/GPA 3.5 B/GPA 2.8

Finland

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Kandidaattii/Kandidat (out of 3) 3 2 1
Maisteri/Magister (out of 5) 4.5 3 2.5

France

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licence (3 years)/ Maitrise/ Diplôme d'Ingénieur 14 12 11

Georgia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4-year degree (% = new system) 5 (95%) 4.5 (85%) 4 (75%)

Germany

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
German Bachelor/ Diplom, Magister Artium / Zeugnis über den Zweiten Abschnitt der Ärztlichen Prüfung 1.5 2.5 3.0

Ghana

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Ghana First Upper second/60% Lower second/50%

Greece

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
AEI 8.5 7.0 6
TEI 8.5 7 6.5

Grenada

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree from University of West Indies - classification 1st 2:1 2:2
Degree from University of West Indies - grade / percentage A B / 75% C / 55%
Degree from University of West Indies - GPA 3.6 3.0 2.0

Guatemala

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Liceniado / Titulo de (subject area) - 4years 90% (public university) / 95% (private university) 80% (public university) / 85% (private university) 60% (public university) / 70% (private university)

Guyana

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's GPA 4 GPA 3.5 3.0

Honduras

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Título de Licenciado / Grado Académico de Licenciatura (4 year degree) - GPA out of 5 GPA 5 or 90% GPA 4 or 80% GPA 3.5 or 70%

Hong Kong

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.5

Hungary

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Alapfokozt or Egyetemi Oklevel / Bachelor 5 4 3

Iceland

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Baccalaurreatus degree or Kandidatsprof/Candidatus Mag 8.5 7.5 6.5

India

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Institutions listed on the Indian Ranking of Higher Educational Institutions Framework 65% (First) 60% (First) 55% (Upper second)
All other Indian institutions 70% (First with distinction) 65% (First) 60% (First)

Indonesia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Sarjana I (S1) from A (or B) credited Universities 3.7 (4.0) 3.3 (3.7) 3 (3.3)

Iran

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Iran 17 15 13

Iraq

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Iraq 80% 75% 70%

Ireland

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Republic of Ireland First (70%) Upper second (60%) Lower second (50%)

Israel

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
3 yr Bachelor Degree 90% 80% 70%

Italy

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diploma di Laurea 109/110 104/110 (or 27) 100/110 (or 26)

Ivory Coast

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diplome d'Etude Approfondies, Diplome d'Etude Superieures or Diplome d'Etude Superieures 16 14 (Bien) 12 (Assez Bien)

Jamaica

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
For degrees studied at The University of West Indies or degrees accredited by UCJ and CCCJ 1st (GPA 3.6) 2:1 (GPA 3.0) or B 2:2 (GPA 2.0) or C

Japan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Japan 85% 80% or B or 3.0 70% or C or 2.0

Jordan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3 or 3.5/5 or 75% 2.8 or 65%

Kazakhstan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 5.0/percentage scale 4.5 or 90% 4 or 85% 3.5 or 80%
GPA 4.33 scale 3.9 3.7 3.2
GPA 4.0 scale 3.7 3.4 3

Kenya

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Kenya First / 70% / A Upper second / 60% / B Lower second / 50% / C

Kosovo

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Kosovo 10 9 8

Kuwait

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.6 3.0 2.8

Latvia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Latvia 9 7 6

Lebanon

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
American 90% (3.5) 80% (3.2) 70% (2.8)
French 18 15 12

Liberia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's 4.0 or 90% 3.5 or 85% 3 or 80%

Libya

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
BSc Engineering, Architecture, Medicine 85 (3.6) 75 (3.0) 65 (2.5)
Other bachelor's degree from a university 90 (4.0) 85% (3.6) 75% (3.0)

Lithuania

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Lithuania 9 8 7

Macau

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Macau 1st or GPA 3.7 2:1 or GPA 3.0 2:2 or GPA 2.5

Macedonia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Macedonia 10 9 8

Malawi

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's only MSc 75% MSc 70% MSc 65%

Malaysia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Classification First Class 2.1 2.2
GPA 4.0 scale 3.5 3.0 2.8

Malta

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Malta 1st (80%) 2:1 (70%) 2:2 (55%)

Mauritius

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Mauritius 1st or 70% 2:1 or 60% 2:2 or 50%

Mexico

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Mexico 9 8 7

Moldova

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diploma de Licenţă (Diploma of Licentiate) 10 9 8

Mongolia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Диплом Специалиста (Specialist Diploma) 90% or 3.5 80% or GPA 3.2 70% or GPA 3.0

Morocco

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Morocco 17 15 13

Mozambique

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 year Licenciatura 16 14 12

Myanmar (Burma)

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
2 year Master's degree 5 or 85% 5 or 75% 4.5 or 65%

Namibia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Namibia 80% or A 70% or B 60% or C

Nepal

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's (after 3 year bachelor degree) 90% or 3.9 GPA 80% or 3.8 GPA 65% or 3.3 GPA

Netherlands

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Netherlands 8 7 6

New Zealand

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 Year Honours degree (480 credits) - Level 8 First (7.0) Upper Second (6.0) Lower Second (4.0)

Nicaragua

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciatura (4 year) 90% 80% 70%

Nigeria

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
7 point Scale 6 5 4
5 point scale 4.5 3.8 3.5
4 point scale 3.5 3 2.5

Norway

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Norway A B C

Oman

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.5

Pakistan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Public Universities 4 Year degree only First with distinction (75%) / 4.0 First (65%) / 3.2 Second (59%) / 2.6
Private Universities 4 Year degree only First with Distinction (85%) First (75%) First (65%)
2 or 3 year bachelor's plus Master's First (60%) Second (55%) Second (50%)

Palestine

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bachelor Degree A / 90% / 3.7 B+ / 85% / 3.3 B / 80% / 3.0

Panama

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 Year Licenciado / Título de [subject area] 91 (A) 81 (B) 71 (C)

Papua New Guinea

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Papua New Guinea 1st 2:1 2:2

Paraguay

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Paraguay - 4 3.5

Peru

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 Year Título de Licenciado / Título de [subject area] 14 13 12

Philippines

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Degree from prestigious state universities or Centres of Excellence (COE) Summa Cum Laude 4.0 / 96% / 1.0 Magna cum Laude 3.5 / 92% / 1.5 Cum Laude 3.0 / 87%/ 2.0

Poland

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bachelor Degree (post 2003) Magister (pre- 2003) 5 4.5 / 4+ 4

Portugal

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Portugal 18 16 14

Qatar

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.8

Romania

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diploma de Licenta/ Diploma de Inginer 9 8 7

Russia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Russia 4.5 4.0 3.5

Rwanda

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
4 year bachelor (Hons) degree (480 credits) 1st, 16/20 (80%) 2:1,14/20 (70%) 2:2, 12/20 (60%)

Saudi Arabia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.8
GPA 5.0 scale 4.5 3.75 3.5

Senegal

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Maitrise, Diplome d'Etude Approfondies,Diplome d'Etude Superieures or Diplome d'Etude Superieures Specialisees 16/20 or Tres Bien 14/20 or Bien 12/20 or Assez Bien

Serbia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Diplomirani/ Bachelor's degree 9 8 7

Sierra Leone

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Classification - 2:1 2:2
Percentage grading - 60-69% 50-59%
Letter grading - B+ B

Singapore

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Classification First Upper second Lower second
GPA 4.0 scale 3.7 3.0 2.7
GPA 5.0 scale 4.5 3.5 3.0

Slovakia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Slovakia 1.5 or B 2.0 or C 2.5 or C/high D

Slovenia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Slovenia 9.5 8.5 7

South Africa

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Classification 1st 2:1 2:2
Percentage scale 75-100% 70-74% 60-69%

South Korea

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA out of 4.5 4.0 / A 3.5 / B 3.0 / C+
GPA out of 4.3 4.0 / A 3.0 / B 2.7 / C+

Spain

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciado / Título de Ingeniero / Título de Arquitecto 8.5 7 6.5
UCM grading 3.0 2.0 1.5

Sri Lanka

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Sri Lanka 70% 60% 55%

Sudan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Sudan (North and South) 1st or 70% or B+ 2:1 or 66% Mid 2:2 or 60% or B

Sweden

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Sweden - Overall grade of VG with a minimum of 90 credits at VG Overall grade of G with a minimum of 90 credits at G

Switzerland

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Switzerland 6 5 4

Syria

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
State universities 4 years of study 80% 70% 60%
Private universities 4 years of study 90% 80% 70%

Taiwan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Category 1 (4 year degree) 80% 75% 70%
Category 2 (4 year degree) 85% 80% 75%

Tajikistan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Tajikistan - 4.5 4

Tanzania

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Tanzania 1st 2:1 2:2

Thailand

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.2 2.8

Trinidad and Tobago

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
For degrees studied at The University of West Indies or degrees accredited by ACTT 1st or B+ or 70% 2:1 or B or 65% 2:2 or B- or 60%

Tunisia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licence, Maîtrise, Diplôme National d'Ingénieu 15 (tres bien) 14 (bien) 11 (assez bien)

Turkey

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Lisans Diplomasi or a Műhendis Diplomasi 3.5 3 2.5

Turkmenistan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Turkmenistan - 4.5 4

Uganda

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Uganda 1st or 4.4 2:1 or 3.8 2:2 or 3.0

Ukraine

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Dyplom Magistra or a Bachelors degree (11 / 5) 11 or 5 9 or 4.5 8 or 4

United Arab Emirates

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.0 2.6

United States of America

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
GPA 4.0 scale 3.5 3.2 2.8

Uruguay

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciado (4 year) 10 9 8

Uzbekistan

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Bakalavr Diplomi / Diplomi (Specialist Diploma) 90% or GPA 4.5 80% or GPA 4.0 70% or GPA 3.0

Venezuela

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Licenciado/Professional title. (4 year) 18/20 or 8/9 16/20 or 7/9 14/20 or 6/9

Vietnam

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Vietnam 8.0 7.0 6.0

Zambia

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
Master's A or 4.0 or 80% B+, 3.5 or 70% B or 3.0 or 60%

Zimbabwe

First-class honours (70%) Upper second-class honours (60%) Lower second-class honours (50%)
3/4 year degree 1st or 75% 2:1 or 65% 2:2 or 60%

English language requirements

Applicants must meet the minimum English Language requirements. Further details are available on the International website.

The standard University IELTS English language requirement is 6.5 overall with 6.0 in each individual element (reading, writing, listening and speaking).

Fees and funding

UK / EU fee

Full-time degree per annum
£10,900

International fee

Full-time degree per annum
£25,500

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment.

The fee stated is for a full-time student undertaking a master’s programme of 180 credits. Part-time students should divide the published fee by 180 credits and then multiply by the number of credits they are taking to calculate their tuition fees.

Find out more about master's degree funding