Programme information

This programme is aimed at providing students with the very latest Cyber Security and Big Data principles, practices, tools, and techniques through analysing and evaluating practical application problems in Cyber Security and Big Data industry and responding to important challenges the world is facing. 

After completing this programme, students will have a comprehensive understanding of the challenges in Cyber Security and Big Data faced by industry and society and the necessary skills to address those challenges in the most effective way. The programme is designed to build students’ knowledge and develop their expertise in network security, cryptography, data science, and big data analytics through action-based learning, analysis and evaluation of application problems. An essential element built in the programme is to develop the students’ employment skills that are essential to the Cyber Security and Big Data industries or related businesses, e-commerce, 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 standard tuition fees for 2018-19 entry are:

  • £10,250 (UK/EU)
  • £23,300 (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 2018 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 also choose and complete 4 of the 9 optional modules; one of these modules must be completed in Semester 1, and three must be completed in Semester 2. Students will pick a second subject from the list of nominated second subject modules offered by the other Institutes in the first semester. All students must complete a Dissertation worth 60 credits.

Core modules

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.
 
Assessment
  • 10% Project presentation
  • 30% Final coursework report
  • 60% In-Class Test

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
 
Assessment
  • 40% Coursework
  • 20% Group presentation
  • 40% Exam

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:
 
  • 20% Group project proposal
  • 20% Individual reflection
  • 30% Final Project Report
  • 30% Project deliverables to the client

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:
 
  • 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.
 
Assessment
100% Coursework consisting of:
 
  • 20% Literature review
  • 20% Research proposal
  • 60% Dissertation report/essay

Optional modules

Network Security

This module covers the main concepts and technical details of network security properties, mechanisms, protocols and applications that are widely in use.

Learning Outcomes

On completion of this module students should be able to:

  • Demonstrate knowledge of main concepts related to the network security properties, mechanisms, protocols, and applications that are widely in use in today’s communication systems and networks, and be able to relate their knowledge to network security issues in real-world scenarios
  • Understand the principles of security in computer networks, concepts, models and architectures of available network security mechanisms
  • Understand the security in the Internet and web-based applications
  • Analyse detailed concepts pertaining to the network security architectures and their use
  • Recognise limitations, and design possible solutions for existing problems in web-based applications, common security architectures & APIs
  • Demonstrate gained experience in the Internet security and security concept in web-based applications, common security architectures & APIs
Assessment
  • 30% Coursework report
  • 70% Exam

Digital Forensics

This module will cover the principles of digital and cyber forensics methodologies, processes required to investigate cyber-attacks and cybercrime in networks, applications, and devices. The students learn 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 the methodologies 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 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 security
Assessment
  • 50% Coursework
  • 50% Exam

Advanced Big Data Analytics

The aims of this module are to:
 
Introduce the concept of Big Data systems and the challenges posed by such systems
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
  • 35% Coursework
  • 15% Presentation and Q&A
  • 50% Exam

Internet and Communication Networks

This module introduces the principles of communication networks and network technologies, including fixed and wireless networking, such as Ethernet, WLANs, mobile broadband networks, as well as basics related to communications, such as FEC coding and modulation. Topics will cover the layers within the Open System Interconnection (OSI) model, including the physical layer (e.g., copper, fibre, wireless, satellite), data link layer (e.g., MAC, 802.11, 802.3), network layer (routing, congestion, QoS, IPv4/v6), transport layer (UDP, TCP, socket programming), and application layer (HTTP, email, VoIP, VoD, streaming, conferencing). The module will also incorporate xDSL and Fibre broadband, P2P networks, and content distribution techniques.
 
The aim of this module is to provide you with the Internet and communication networks system details, their design constraints, limiting factors and application areas.
 
Learning Outcomes
On completion of this module students should be able to:
 
  • Show knowledge of communication networks, fixed and mobile communication systems and the Internet, and be able to relate their knowledge to real-world examples;
  • Key elements and operation of the communication networks, fixed and mobile systems, and the Internet;
  • The data rates available on these networks and systems;
  • What applications would operate using these data rates with their major limitations of channel characteristics variations;
  • Gain experience with system performance analysis, understanding of operational data rates and limitations;
  • Critically analyse and reflect on the problems that the current communication networks are facing due to channel variations and relate some possible solutions;
  • Obtain communication networks design experience and their practical limitations;
  • Synthesise necessary information to evaluate data rates for given services, their channel error performances and the provided Quality of user Experience (QoE);
  • Apply their communication networks knowledge when working in industry;
  • Critically evaluate communication networks related problems and deal with their possible solutions;
  • Present themselves in the area of research and development in communication networks & the Internet to secure advanced level jobs.
 
Assessment
 
  • 30% Coursework
  • 70% Exam

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
  • 40% Coursework
  • 60% Exam

Media Cloud Applications and Services

This module gives 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, quality probing, adaptation, etc. Also, media cloud networking and related topics are addressed. Privacy and security issues in media cloud services are also covered. The media cloud case studies and business models are presented.
 
The aim of this module is to provide you with an overview of the cloud technology with a special emphasis on media 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 media cloud applications and the challenges that are associated with making such applications available to the end-users via cloud technology;
  • The principles of cloud computing technology, media cloud applications, and the associated challenges;
  • Media cloud networking and related topics;
  • Privacy and security issues in media cloud services;
  • Media cloud case studies and business models;
  • Identify the requirements pertaining to the media applications used in cloud services;
  • Design cloud computing architectures;
  • 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 media 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
  • 30% Coursework
  • 70% Exam

Mobile Broadband and Wireless Networks

This module will introduce the latest mobile broadband and wireless communication technologies, including their network architectures and radio interface protocols. Topics covered include 3GPP LTE/LTE-Advanced mobile broadband radio access, propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques. The module will also introduce various other wireless communications technologies, such as WLANs, satellite communications for broadband, and will also provide an overview of the 5G mobile communications.
 
The aim of this module is to introduce you with the latest mobile broadband and wireless communication technologies, including their network architectures and radio interface protocols
 
Learning Outcomes
 On completion of this module you should be able to:
 
  • Understand essential mobile broadband and wireless networks topics: mobile broadband radio access, wireless propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques;
  • 3GPP LTE/LTE-Advanced, propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques;
  • Understand mobile broadband network architecture, radio interface protocol stacks, system performance, and data rates;
  • Critically analyse and reflect on the associated limitations, the problems of current networks are facing due channel variations and relate some possible solutions;
  • Design radio resource management algorithms, media applications delivery techniques over mobile broadband networks;
  • Synthesise solutions to the straining of cellular networks as the point-to-point radio link approaches its theoretical limits;
  • Apply their communication networks knowledge when working in industry;
  • Analyse communication networks related problems and deal with their possible solutions;
  • Apply the required knowledge and skills in the latest mobile broadband and wireless networks technologies;
  • Present themselves competitively R&D jobs in the telecommunications sector in general, especially in the mobile broadband and wireless communications sector.
 
Assessment
  • 30% Coursework report
  • 70% Exam

Media Processing and Coding

This module will introduce the latest mobile broadband and wireless communication technologies, including their network architectures and radio interface protocols. Topics covered include 3GPP LTE/LTE-Advanced mobile broadband radio access, propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques. The module will also introduce various other wireless communications technologies, such as WLANs, satellite communications for broadband, and will also provide an overview of the 5G mobile communications.
 
The aim of this module is to introduce you with the latest mobile broadband and wireless communication technologies, including their network architectures and radio interface protocols
 
Learning Outcomes
 
On completion of this module you should be able to:
 
  • Understand essential mobile broadband and wireless networks topics: mobile broadband radio access, wireless propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques;
  • 3GPP LTE/LTE-Advanced, propagation models, network architecture, radio interface architecture, Radio Resource Management (RRM) algorithms, scheduling algorithms for multimedia delivery, heterogeneous networks (HetNets), fixed/mobile relays, femto cells, e-MBMS, and wireless video communications techniques;
  • Understand mobile broadband network architecture, radio interface protocol stacks, system performance, and data rates;
  • Critically analyse and reflect on the associated limitations, the problems of current networks are facing due channel variations and relate some possible solutions;
  • Design radio resource management algorithms, media applications delivery techniques over mobile broadband networks;
  • Synthesise solutions to the straining of cellular networks as the point-to-point radio link approaches its theoretical limits;
  • Apply their communication networks knowledge when working in industry;
  • Analyse communication networks related problems and deal with their possible solutions;
  • Apply the required knowledge and skills in the latest mobile broadband and wireless networks technologies;
  • Present themselves competitively R&D jobs in the telecommunications sector in general, especially in the mobile broadband and wireless communications sector.
 
Assessment
  • 30% Coursework report
  • 70% Exam

Introduction to Programming and MatLab

In this module MATLAB simulation tools and environments will be covered in combination with the basic understanding of programming in C++ which is a basic tool for developing cross-platform DSP systems and multimedia applications. Topics include: basic C++ programing, objective foundation, C++ class & inheritance, and advanced programming techniques such as templates & exceptions, MATLAB programming skills, optimisation and simulation tools for signal processing & communications.
 
The aim of this module is to provide you with basic understanding and programming skills in C++ and MatLab for developing digital signal processing applications and simulation.
 
Learning Outcomes 
On completion of this module you should be able to:
 
  • Demonstrate basic programming skills in C++ and MatLab;
  • Demonstrate necessary programming techniques in digital signal processing to develop applications and simulations for media processing;
  • Cross-platform programming structure and skills in general;
  • How to use digital signal processing tools and functions;
  • Techniques used in developing applications and related numerical simulations;
  • Understand the concept of advanced techniques of class, inheritance, templates;
  • Identify, utilise and optimise tools, algorithms and functions for simulation of complex digital signal processing applications;
  • Implement functions and algorithms in C++ and MatLab;
  • Understand the use of advanced techniques of, e.g. class, inheritance, templates and optimisation;
  • Gain knowledge of different algorithms, functions and their usage in DSP tool box;
  • Develop multimedia DSP applications and simulations for digital media and communication systems;
  • Solve other industrial complex problems involving signal 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 multimedia and communication systems as well as other digital technologies sectors.
 
Assessment
100% Coursework consisting of:
 
  • 50% Coursework 1
  • 50% Coursework 2

Second subject modules (your choice of one)

Design Thinking

The module will include: visualization, using imagery to envision possible future conditions; journey mapping, assessing the existing experience through the customer's eyes, using customer oriented data collection techniques; value chain analysis, assessing the current value chain that supports the customer's journey; mind mapping, generating insights from exploration activities and using those to create; design criteria; brainstorming, generating ideas; rapid concept development, assembling innovative elements into a coherent proposition that can be explored and evaluated; rapid ‘prototyping', expressing a new concept in a tangible form for exploration, consumer testing, and refinement; consumer testing; and storytelling.
 
The aim of this module is to enhance your ability to use design approaches and tools for identifying and implementing human centred innovation opportunities. You will be expected to deploy knowledge learned in this module into parallel running Collaborative Project module.
 
Learning Outcomes
The module will introduce you to a systematic design-based approach aimed at identifying and implementing user centered innovation opportunities.
 
On completion of this module you should be able to:
 
  • Identify when and how to use range of Design Thinking tools;
  • Select appropriate tools to inform project development;
  • Appropriate use of the Design Thinking tools in a parallel module;
  • Develop communication skills in diverse teams;
  • Developed a systematic approach to tackle complex projects;
  • Apply tools in a project context;
  • Work with variety of stakeholders;
  • Understand how to tackle `wicked' problems;
  • Be able to deliver a succinct presentation to communicate key facts.
 
Assessment
  • 10% Presentation
  • 20% Peer Feedback
  • 70% Report

Principles of Entrepreneurship and Innovation Management

The theory of entrepreneurship and the importance of entrepreneurial action to the innovation process; the contemporary business environment; micro and macro environments; intellectual property; funding & finance; project management; corporate responsibility & sustainability; governance; ethics; business planning; strategy; risk analysis and failure.
 
The aim of this module is to equip you with an in depth knowledge of the innovation process, its importance to the economy and an understanding of all of the various factors affecting its success including intellectual property, funding and strategy. We will introduce the academic theories of entrepreneurship and analyse the personality traits and behaviours associated with entrepreneurs.
 
Learning Outcomes
On completion of this module you should be able to:
 
  • Innovation as a process;
  • Identify the academic theories of entrepreneurship;
  • Identify the factors influencing the success of organisations;
  • Relate innovation theory to the performance of organisations;
  • Use investigative and research skills;
  • Demonstrate effective report writing skills.
  • Demonstrate commercial awareness.
 
Assessment
100% Coursework consisting of:
 
  • 50% Report 1
  • 50% Report 2

Sport Media and Marketing

This module will include: the main themes that underpin media and marketing, the evolution of media and marketing in a sport context, the practices and techniques for effective sport media and marketing, the types of media and marketing, and consumer and fan engagement.
 
The aims of this module are to be introduced to key concepts in sport media and marketing and to develop understanding of the nature of sport media and marketing.
 
Learning Outcomes
On completion of this module you should be able to:
 
  • Assess the major theories, principles, and concepts surrounding sport media and marketing;
  • Apply techniques and practices involved in conceptualising and developing a marketing plan;
  • Gather, analyse, and present sport media and marketing ideas and concepts;
  • Apply sport media and marketing principles that can be utilised in different sport environments;
  • Demonstrate initiative and personal responsibility;
  • Continue to learn independently and to develop professionally.
 
Assessment
  • 40% Sponsorship Pitch
  • 60% Marketing Plan

The Key Topics in Media and Creative Industries

The module content will include: defining media and creative industries; ownership, concentration and control in media and creative industries; innovation and technological change; media and creative markets; business models in media and creative industries; copyright; global media cities; clustering of media and creative industries; media and cultural policy.
 
The aim of this module is to introduce you to key critical debates relating to the economics of media and creative industries and their social, cultural and political implications.
 
Learning Outcomes
On completion of this module you should be able to:
 
  • Understand how and why the media and creative industries have been defined;
  • Understand the importance of industrial structure in media and creative industries;
  • Understand the implications of innovation and technological change for media and creative industries;
  • Understand changing business models in media and creative industries;
  • Understand the importance of copyright and how this is affected by technological change;
  • Understand why media and creative industries cluster in particular spaces and cities;
  • Understand the globalisation of media and creative industries;
  • Understand media and cultural policy.
  • Identify, debate and evaluate relevant critical perspectives on media and creative industries;
  • Systematically assess the implicit theoretical assumptions of contrasting perspectives;
  • Use critical perspectives to analyse emerging trends in media and creative industries;
  • Communicate effectively in speech and writing, with academic and non-academic audiences;
  • Engage in critical reasoning, debate and argumentation;
  • Assess the empirical validity of competing perspectives;
  • Manage time and resources effectively;
  • Synthesise different sources of data and identify key arguments and issues at stake in particular fields of practice;
  • Understand the behaviour of firms in media and creative industries;
  • Understand emerging trends in media and creative industries;
  • Apply skills in written and verbal communication that are relevant to this field;
  • Be able to plan, organise and manage coursework assignments, demonstrating independence, initiative and originality.
 
Assessment
  • 100% Coursework

Introduction to Diplomacy

This module will introduce the main concepts, theories and practices of international relations and diplomacy.
 
The overarching aim of the module is to provide you with a wider understanding of the historical trends, conceptual bases and current practices of diplomacy in the context of current global affairs and to help them critically evaluate the relevance for all fields of social, economic and scientific practice. As part of such aim, the module will advance an interdisciplinary ethos.
 
Learning outcomes
On completion of this module you should be able to:
 
  • Evaluate the historical evolution of the modern international system
  • Critically evaluate the key concepts and theories of diplomacy
  • Evaluate the role and behaviours of international organisations and states in the international system
  • Evaluate the context of the present-day international political and economic relations within which diplomacy takes place
  • Identify and assess the importance of such dynamics for their respective fields of study
  • Construct reasoned arguments utilising concepts and approaches to the study of diplomacy
  • Recognise established and emergent phenomena in the practice of diplomacy within the current global affairs
  • Creatively and critically evaluate the possibilities of various intersections between diplomatic practice and the objectives and modes of delivery in their respective fields
  • Via in-class team tasks, and in assessed assignments, apply such skills to empirically grounded case study materials
  • Reflect on own learning and make use of constructive feedback
  • Gather and organise evidence, data and information from a variety of secondary and primary sources
  • Work in small groups
  • Develop research and presentation skills
  • Develop a cross-disciplinary and collaborative research and learning ethos
 
Assessment
  • 40% Presentation
  • 60% Essay

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 enhancemtheir knowledge and career prospects further by undertaking an MRes or PhD programme.

 

Your personal development

Enterprise Through the Curriculum is an intrinsic element of every master’s programme at Loughborough University London and has been carefully designed to give students the best possible chance of securing their dream role. From employability profiling to live group projects set by a business or organisation, and from site visits to organisation-based dissertation opportunities, Loughborough University London is the first of its kind to develop a suite of activities and support that is positioned as the underpinning of every student’s experience.

Assessments

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.

Speak to a programme specialist

If you'd like to know more about this programme, you can request an email or telephone call from an academic responsible for the teaching of this programme. 

 

Complete the contact request form