Programme information

Students who choose an MRes in Digital Technologies will receive guidance from academics leading research in the fields of data science, Internet, communications, and multimedia technologies. Academics will share their expertise on Internet and Communication Networks and Principles of Data Science, to enable students to gain specialised knowledge of these fields. 

The MRes programme will enable students with a passion for research to widen their skills, focus their interests and take the next step towards a PhD or future career. 

Studying an MRes with Loughborough University London will give students a fascinating introduction into the life of a postgraduate researcher. The MRes programme has been carefully designed to empower graduates with rigorous research and analytical skills, in order to progress onto high level researcher positions within their chosen sector or field. 

The MRes programme will explore the research processes, and uncover the designs, practices and methodologies used by experienced researchers from a range of disciplines. Whilst a traditional taught master’s degree programme focuses on the development of expertise in a chosen area, an MRes places more emphasis on the individual to uncover new knowledge and develop their own research expertise. 

In addition to a major research project all students studying an MRes programme will complete taught units such as:

  • The Collaborative Research Project
  • Research Design, Practice and Ethics
  • Quantitative Research Methods
  • Foundations in Qualitative Research

Entry requirements

Minimum of an upper second class honours degree or equivalent in a wide range of subjects. In exceptional circumstances an applicant may be admitted to the degree who does not possess the requirements mentioned, but who has substantial relevant work experience.

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

  • To gain advanced knowledge of the research processes, designs, practices and methodologies
  • To acquire in-depth training in the conduct and management of research, from its inception to its completion and dissemination
  • To develop your independent research capacity
  • To explore and reflect on their developing research skills in the context of their areas of research interest and their growth as professional researchers
  • To critically analyse ethical aspects of academic research and gain advanced ability to deal with ethical problems and challenges

Programme modules

This programme 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.
To complete the MRes Digital Technologies students must complete 7 modules. Students must select two optional modules and should seek advice on which modules to choose at the beginning of the academic year, depending on their research plans.

Core modules

Collaborative Research Project

This module focuses on research in a real-world context. On completion of the module, you will have completed a full rapid cycle of teamwork-based empirical research originating from a problem set by a real organisation, community, business or similar. You will spend the module identifying appropriate approaches and research methods, planning and executing the research and disseminating the results back to their external project partner.
This exciting module will encourage you to learn and apply concepts related to team roles, collaborative working and shared leadership. You will be expected to apply social science and design research methods, whilst utilising their critical thinking and reflection skills.
You will also engage with the project by developing your practical research skills and project management skills, and will discover how to successfully manage stakeholder expectations, while giving practical consideration to research ethics and sustainability issues.
This hands-on module acts as a springboard for those wishing to gain an introduction to the issues related to real-world research, in preparation for an MRes Dissertation or subsequent PhD.
Learning outcomes 
On completion of this module you should be able to:
  • Work in diverse and interdisciplinary research teams
  • Understand and be able to undertake a project and team based real-world research process
  • Apply critical enquiry, reflection, and appropriate methods to identify, frame, and resolve issues and problems related to research initiatives.
  • Identify stakeholder issues, values and worldviews, while collecting and analysing evidence-based information and knowledge to develop appropriate research insights, practices and outputs.
  • Identify, structure and reflect on key issues and propose solutions to undertaking research in creative ways using appropriate qualitative and quantitative research methods
  • Enhance their 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 implement a real-world and team based research initiative including identifying problems, objectives, resources, timing, and appropriate qualitative and quantitative research methods
  • Effectively communicate research ideas, methods and results to diverse stakeholders, using multi-channel, state of the art data, media and technologies
  • Make informed, critical and reflective decisions in time-limited situations
  • Coursework (100%)

Foundations in Qualitative Research

The content of this module is developed around current practice in qualitative research in a range of disciplinary fields. Content is drawn from the intended learning outcomes and is linked to the module assessment.
The module content covered includes:
  • How to become a skilled qualitative researcher
  • Epistemological and theoretical issues related to qualitative research
  • Design issues for qualitative studies
  • Selecting an appropriate research method (action research, case study, critical discourse analysis, ethnography types, gender theories, grounded theory, participant observation, textual analysis)
  • Analysing and interpreting qualitative data
  • Issues and debates related to conducting qualitative research
  • Evaluation of qualitative research studies
  • An examination of ethical considerations
  • Scholarly academic writing skills and information communication technologies
  • The future of qualitative research in solving research problems
Learning outcomes
On successful completion of this module, you should be able to:
  • Demonstrate advanced knowledge and understanding of qualitative research paradigms, principles and practices
  • Undertake an original and independent piece of qualitative research using a wide range of ontological and epistemological perspectives and methods
  • Present a rationale for the application of qualitative research to interdisciplinary research problems
  • Employ a range of qualitative approaches to record and analyse qualitative data
  • Solve complex research problems through qualitative approaches
  • Critically evaluate the characteristics of qualitative research
  • Analyse, interpret and critically evaluate qualitative data
  • Conceptualise and design qualitative research approaches for various research settings
  • Employ qualitative approaches to research and evaluate their use and value in research
  • Implement qualitative research projects for various disciplinary settings
  • Demonstrate awareness of relative activities elsewhere and always apply a self-check against the best practice
  • Find and evaluate scholarly sources
  • Engage in critical reasoning, debate and argumentation
  • Demonstrate effective communication and argumentation of the research findings with appropriate references
  • Demonstrate effective project management skills and show commercial awareness of the managed task
  • Demonstrate a positive attitude towards qualitative research approaches
  • Demonstrate qualitative problem-solving skills in a variety of research settings
  • Coursework (100%)

Research Design, Practice and Ethics

The aims of this module are to prepare you for research planning, design and execution cycles when carrying out research. You will discover how to implement good research practices and adopt professional research ethics. The module will also equip students with the relevant skills, knowledge and understanding to embark onto a professional research career in industry or academia.
Research execution stages will be discussed in detail. These include choosing the best research methods, executing the most efficient, professional and ethical research process and achieving set objectives.
Students will discover the best possible data collection methods and will understand how to perform critical analysis of data including comparison against the state of the art available in the public domain.
The importance of applying a self-check process at all stages is also covered, to ensure that students are aware of the correct methods and means to reach their intended research goals. All of these topics are of vital importance and will ensure students maintain a professional attitude towards research and critical analysis.
Learning outcomes
On successful completion of this module you should be able to demonstrate knowledge and understanding of:
  • The importance of design and planning of the overall task in hand
  • The importance of a clear hypothesis and coordination of the research objectives
  • The importance of efficient execution of the planned strategy to achieve the objectives
  • The ethical implications and the importance of professional approach to ethical issues in research
  • The methodologies for data collection or knowledge assimilation for the subject area
  • Methods of data analysis and their suitability for the intended data
  • The most effective methods of presentation of this data or knowledge Skills
  • 100% coursework

Quantitative Research Methods

By the end of this module you will be able to make informed assessments of the design and construction of quantitative data sources and their implications for analyses. The module will carefully guide you through the best methods to appropriately prepare and analyse quantitative data using statistical software; and apply and interpret a range of forms of statistical analysis competently, including descriptive, inferential and multivariate techniques.
The module will focus on concepts, methods and skills central to quantitative social research, including data collection approaches and concept operationalisation. Building on a grounding in ideas relating to probability sampling, sampling error and statistical inference will extend from comparisons of means and simple cross-tabular analyses to a discussion of multivariate analysis approaches, including linear and logistic regression, but also introducing other techniques relevant to specific analytical goals (e.g. factor analysis, ANOVA, principal components analysis, cluster analysis and hazard models).
Teaching methods will mostly consist of practical classes and workshops. You will gain hands-on familiarity with existing, high profile quantitative sources, including longitudinal studies, and the practicalities of handling data from such sources. This module can act as a springboard for those wishing to gain more specific and advanced quantitative skills in subsequent training, in preparation for an MRes Dissertation or subsequent PhD.
Learning outcomes 
On completion of this module you should be able to:
  • Critically reflect on and provide well-grounded analysis of the appropriate type of quantitative analysis required for a specific research project;
  • Understand the validity and robustness of the findings reported upon in research;
  • Critically reflect upon the presented results and the extent to which these support conclusions drawn in research;
  • Translate research question into research model into quantitative analysis strategy;
  • Perform basic quantitative methods (such as correlations, Chi-2, factor analysis, regression analysis);
  • Recognizing, and addressing commonly found quantitative research related issues in current scholarly work;
  • Be able to determine the extent to which research presented is appropriately researched;
  • Be able to challenge quantitative research presented;
  • Select and use appropriate quantitative research approaches;
  • Demonstrate effective research strategy decision-making.
  • Report (40%)
  • Assignments (60%)

Major Research Project

The aims of this module are to give you an opportunity to research a related subject in detail, and present the findings in a format that best suits your academic strengths and future career goals.
The module will create an opportunity for you to put into practice real research skills and knowledge by leaning about a specific area, understanding the current problems through investigating the related state of the art and proposing solutions to push the frontiers forward in your chosen subject area.
Detailed experimentation (where possible), critical analysis of your results and effective presentation of the findings are three key milestones of this module.
You have a choice of project pathways:
  • A research project suggested by you or your academic supervisor, which will be mutually agreed and carried out within the University, with or without links to the external organisations.
  • A University-based research project that is set by an external organisation and supervised by your University supervisor in collaboration with tan external organization representative.
  • A research project that involves direct collaboration and placement/visits to an external organization (subject to a suitable placement position being obtained)
Learning Objectives
On successful completion of this module, you should be able to:
  • Articulate a clear, coherent and original research question, hypothesis or business problem in a suitable subject area
  • Synthesise relevant sources (e.g. research literature, primary data, obtained results) to construct a coherent argument in presenting their findings to their research objectives
  • Analyse data collected by an appropriate method during their research project
  • Critically evaluate data collected in context with the state of the art available in public
  • Engage in critical debate and argumentation in written work
  • Apply principles of good scholarly practice to their written work
  • Perform an appropriate literature review using library databases or other reputable sources
  • Determine the most appropriate research methods for a particular subject area
  • Plan a research project and produce a realistic gantt chart demonstrating their intended timelines
  • Synthesise information from appropriate sources
  • Demonstrate rational use of research methods and available tools
  • Select and use appropriate investigative and research skills
  • Demonstrate effective project planning skills
  • Find and evaluate scholarly sources
  • Engage in critical reasoning, debate and argumentation
  • Demonstrate effective researching to reach the set objectives
  • Demonstrate effective presentation and report writing skills
  • Successfully manage a project from idea to completion
  • Demonstrate commercial awareness or the impact of knowledge transfer in a business or research environment
You should also be able to demonstrate knowledge and understanding of:
  • The importance of research project planning
  • The importance of researching the state of the art and understanding the subject area using literature review or other means such as software programmes, standard specifications etc.
  • Theoretical as well as practical perspectives of the research objectives
  • Methods of data analysis and their suitability for the intended project objectives
  • The most effective methods of presentation of the collected and analysed data to relate it to the project objectives
  • The ethical implications of research
  • Final Project Report/Thesis (60%)
  • Final Presentation (20%)
  • Interim Project Report (20%)

Optional modules

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.
  • 30% Coursework
  • 70% Exam

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

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.
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
  • 100% coursework

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
  • 35% Coursework
  • 15% Presentation and Q&A
  • 50% 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.
  • 40% Coursework
  • 60% Exam

Media Design and Production

This module introduces the you to applications containing multimedia design, creation and distribution over networks. In particular, multimedia transmission aspects over the Internet Protocol are covered.
First, the production side is covered, including video and audio capturing, synchronisation, recording and interchange formats, and compression workflows. Fundamental knowledge on sound engineering and audio technology, including the design, manipulation and production of audio, will be given to you.
In the second part, different classes of media applications are presented, such as conversational (e.g., Skype, audio-visual conferencing), streaming applications (e.g., Video on Demand), and broadcast applications (e.g., mobile TV, satellite TV), and how those applications are classified according to their bandwidth requirements, packet loss, latency, jitter, and synchronisation requirements. This is then followed Internet-based transmission methods, protocols, and techniques for robust media application delivery.
In the third part, audio-visual media quality assessment techniques and Quality of Experience (QoE) models are presented, where media quality is studied using the principles of human perception of audio/video. Examples from modern media transport methods (e.g., HTTP adaptive streaming) with a view to maximising the end-users’ QoE. User interfaces for interactivity (e.g., motion and video sensors, touch and gesture interfaces) and emerging interaction technologies and associated examples will also be presented.
Learning Outcomes
On completion of this module, you should be able to:
  • Demonstrate knowledge of media applications that are in use in modern digital systems, media quality assessment and Quality of Experience (QoE) models, user interfaces and multi-modal interaction technologies.
  • The principles of multimedia production and requirements of multimedia delivery over networks, in particular the Internet
  • Quality of Service parameters, classes of multimedia services, Internet Protocol suite
  • Quality of Experience, relations between QoS and QoE, user interfaces
  • Define the streams making up a multimedia application, how they are produced, express their transport mechanisms, and associated QoS requirements
  • Analyse the metrics associated with a transmission environment in terms of their suitability for supporting certain multimedia applications
  • Discuss how quality assessment is performed
  • Formulate multimedia applications with realistic design constraints and associated QoE considerations
  • Extensively use the knowledge gained in practical media application design and implementation work
  • Apply their knowledge on certain statistical analysis techniques learned in this module on other real-world problems
  • Develop skills to recognise, analyse and solve challenging problems with attention to details
  • Present themselves in the areas of digital content creation, streaming media, IPTV, mobile multimedia applications and user experience design.
  • 30% Coursework
  • 70% Exam

Future career prospects

Research skills are greatly in demand across the high value industries of the UK. Alongside the taught elements of the programme you will be able to access a tailor-made professional development programme mapped to the Researcher Framework that will support you to market yourself and your skills for a rewarding career. You will get access to:

  • Exclusive workshops on diverse topics suitable for a research career
  • Mentoring
  • Employability Profiling and Careers Support

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. There may also be exams.

Students will be asked to produce project briefs, concept drawings, user scenarios, storyboards, project blogs and multimedia documentation. Take a look at our modules to see what assessments you can expect to undertake.

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