Programme information

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. However, we become increasingly more vulnerable to cyber-attacks that affect individuals and organisations by targeting 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 Cybersecurity and Big Data MSc programme has been developed to master the use of big data analytics in cybersecurity. This programme 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 of our MSc Cybersecurity and Big Data programme 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.

Entry requirements

An honours degree (2:2 or above), or an equivalent overseas qualification recognised by Loughborough University in electronics, computing, physics, mathematics or a related discipline.

Overseas qualification equivalencies

English Language requirements

All applicants for admission to Loughborough University must have a qualification in English Language before they can be admitted to any course or programme, whether their first language is English or not.

More on the Loughborough University website

Fees and funding

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. The tuition fees for 2019/20 entry are:

  • £10,550 (UK/EU)
  • £24,750 (International)

University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Special arrangements are made for payments by part-time students.

View scholarships for 2019 entry

Programme aims

  • Provide students with a comprehensive understanding of the challenges in cyber security and big data faced by industry and society, and will help them to develop the necessary skills to address those challenges in the most effective way
  • Utilise both cyber security and big data analytics techniques to analyse and evaluate problems and respond to challenges with practical applications in real time
  • Build students’ knowledge and develop expertise in network security and cryptography, including big data analytics to combat malicious activities and to detect anomalies in the network
  • Provide individuals and teams with employment skills essential to the cyber security and big data industries and related businesses, such as IT, e-commerce, and governmental organisations using action-based learning

Programme modules

This programme covers a wide range of topics; to give you a taster we have expanded on some of the core modules affiliated with this programme and the specific assessment methods associated with each module.

To complete the MSc Cyber Security and Big Data students must complete 8 x 15 credit modules. Students must choose and complete 5 of the 8 optional modules to complete the MSc Cyber Security and Big Data. 2 of these modules must be completed in Semester 1 and 3 in semester 2. All students must complete a Dissertation worth 60 credits.

Core modules

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 consists of 100% coursework which is made up of:
  • Individual Reflective Essay (55%)
  • Team Project Report (40%)
  • Peer Evaluation (5%)

Principles of Data Science

The aims of this module are to;
  • Introduce students to the concepts of data science and their use in Data Analytics Systems.
  • Enable them 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.


  • 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.
The aims of this module are to introduce the basic concepts of cryptography and develop students' knowledge in cryptographic protocols, techniques, algorithms and implementations in real world, which are the fundamentals of modern cyber security. 

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.


  • Project presentation (10%)
  • Final coursework report (30%)
  • Exam (60%)


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:
  • A desk based research project that could be set by an organisation or could be a subject of the student's choice
  • A project that involves collection of primary data from within an organisation or based on lab and/or field experiments
  • An Internship 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 more detail.

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.


100% Coursework consisting of:
  • Research proposal (10%)
  • Dissertation report (90%)

Optional modules

Advanced Big Data Analytics

This module is likely to explore the following topics: big data and associated challenges; machine learning for predictive analytics: generative models, reinforcement learning; neural networks and deep learning; deep learning techniques for dimensionality transformations and reductions; architectures for big data: introduction to Hadoop and map reduce; and visualisation tools for big data systems.
The aims of this module are to: introduce the concept of Big Data systems and the challenges posed by such systems; and 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 will be coursework based, consisting of:

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

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.
The aims of this module are 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, you will have a critical understanding of the main concepts relating to cybersecurity incidents, forensics analysis, electronic discovery, crime reconstruction, and intrusion investigation.
You will also be able to:
  • 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 the Internet.


Assessment is made up of two technical reports, consisting of:

  • Coursework proposal (30%)
  • Final coursework report (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.


  • Coursework (40%)
  • Exam (60%)

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). Scalable and multi-view coding schemes will 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 aims of this module are to provide students with theoretical and practical knowledge on image, video and audio processing techniques.

Learning outcomes

On completion of this module students should be able to:

  • Display knowledge in essential topics on: audio and video signal processing, coding techniques including scalable and multi-view aspects, as well as practical implications
  • Understand image and Video processing fundamentals, fundamentals of image and video compression and key standards, advanced formats, including 3D media
  • Implement 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.


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

Cloud Applications and Services

This module gives a brief overview of the cloud technology and covers cloud applications and challenges, such as energy efficiency in cloud systems, mobile cloud computing, cloud multimedia rendering, streaming, coding, transcoding, caching, adaptation, design etc. Also, cloud networking and related topics are addressed. Privacy and security issues in cloud services are also covered. The cloud case studies and business models are presented.
The aim of this module is to provide the students with an overview of the cloud technology with a special emphasis on cloud applications and the associated challenges.

Learning Outcomes

On completion of this module, you 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, cloud applications, and the associated challenges
  • Understand cloud networking and related topics
  • Identify privacy and security issues in cloud services
  • Apply cloud case studies and business models
  • Identify the requirements pertaining to the digital 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.


  • Coursework (30%)
  • Exam (70%)

Digital Application Development

In this module Python programming tools and environments will be covered in combination with the understanding of programming in C/C++ which are fundamental tools for developing digital systems and applications. Topics include: computer programming, objective foundation, and advanced programming techniques such as class, structure, pointer, and simulation.
The aim of this module is to provide students with an understanding and programming skills for developing digital applications and simulations.

Learning Outcomes 

On completion of this module students should be able to:

  • Demonstrate programming skills in C/C++ and Python
  • Demonstrate necessary programming techniques to develop digital applications and simulations
  • Show knowledge of programming structure and skills in general
  • Understand how to use digital data processing tools and functions
  • Understand the techniques used in developing applications and related computer simulations
  • Understand the use and concept of advanced techniques of class, 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 industrial complex problems involving digital processing algorithms
  • Find solutions for practical problems by reasoning, deduction and implementing from idea to final application
  • Understand the abstract features from complex objects/problems and develop ideas into algorithms
  • Fulfil the research and development requirements in a range of digital systems and digital technologies sectors.


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

Information Management

This module provides an introduction to information management concepts and frameworks, including ethics data management, document and content management, data storage and operation, information security management, and information quality management. Students will be introduced to big data systems and related technologies, and explore data integration and interoperability between data stores, applications and organisations. The teaching will provide real world examples of how business intelligence enable workers to get value from information, and how digital organisations have established a system of decision rights over data. Students will learn how architecture, modelling, and design are used to discover, analyse, represent and communicate data requirements within the digital industries.

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.

  • Best practices in information management
  • Business intelligence activities to achieve maximum benefit from information
  • Context of data management activities
  • Emerging trend in information storage techniques to manage rapidly growing data assets
  • 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.


Assessment is made up of an examination and an in-class test.

  • Preliminary Assessment (35%)
  • Final Assessment (65%)

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.
  • Principles of security in information systems, concepts, models and architectures of available information security mechanisms
  • 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 for this module is made up of an exam and a final report:

  • Exam (70%)
  • Report (30%)

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

Who should study this programme?

 Our MSc Cybersecurity and Big Data is appropriate for:

  1. Individuals with a technology background and who want to become experts in cybersecurity and artificial intelligence
  2. 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
  3. Security professionals who want to advance their skills in data analytics based cyber defence

Future career prospects

Graduates from this programme will be in a very strong position to take on digital technology posts in a wide range of sectors, including Internet and cloud based businesses, finance firms, governmental organisations, consultancy companies operating in information, communication and network security, as well as those sectors dealing with massive personal data, such as health and wellbeing, where users’ privacy and data security needs safeguarding. 

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


Your personal development

The careers and employability support on offer at Loughborough University London 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.


Modules are assessed by a combination of essays, group exercises, presentations and time constrained assignments. Subject to your choices, there may also be exams. Take a look at our modules to see what assessments you can expect to undertake.

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