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MSc Advanced Computing *

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Institution code: 

One year full-time.

Two years part-time.

Typical Offer: 

2:2 or above in an undergraduate degree.

Please see Entry Requirements below.

* Subject to validation


This degree is designed for those with previous qualifications or experience in the fields of technology, computing or computer science. If you do not have experience of studying or working in these fields, please consider our MSc in Applied Cyber Security or MSc in Data Science and Artificial Intelligence.

The MSc Advanced Computing course is a taught postgraduate course aimed at students with a substantial background in computing who would like to study advanced computing concepts and technologies, covering a wide variety of topics in depth with dedicated experts teaching on each of the pathways. The MSc Advanced Computing degree provides you with a wealth of opportunities to choose your own path of specialisation into a computing career. The course includes four core modules (Dissertation and Research skills, Topics in Emerging Technologies, DevOps, Managing Projects and Teams) and three optional modules from a range of modules offered in artificial intelligence, cyber security, and software development.

Core modules will cover aspects of computing that are common across all subdomains of computing, whereas optional modules will include a choice of topics helping you specialise in an area of your preference (cyber security, software development, artificial intelligence). The programme is suitable for students from computing backgrounds who are interested in a career orientated towards an industry role, as well as in academia.


Course Highlights:

  1. A flexible MSc in Advanced Computing that gives you a choice of pathways or the freedom to create your own. You can choose from one of three pathways in Cyber Security, Software Engineering or Artificial Intelligence.
  2. Access to resources from some of the largest tech companies including Amazon Web Services, Juniper, Oracle and our Google Student Club.
  3. Access to world-class specialist laboratories at our new state-of-the-art DigiTech Centre located at Adastral Park, home to over 150 high-tech ICT companies and BT’s innovation labs.
  4. Opportunities to work on real-world projects in collaboration with industry leaders and experts using the latest technologies.
  5. An annual schedule of industry events and hackathons allowing you to apply the knowledge and skills you learn from the course.
  6. An opportunity to start your own business with the University of Suffolk’s Innovation Centre (IWIC) and gain guidance from business leaders and academics.


Cyber security range

Course modules

Full downloadable information regarding all University of Suffolk courses, including Key Facts, Course Aims, Course Structure and Assessment, is available in the Definitive Course Record.

All students will take the following four required modules:

  1. Topics in Emerging Technologies
    The topics that this module covers vary from year to year, as we try to stay current with issues and ideas being discussed in the industry. However, an indicative set of topics  might include: Bitcoin, crypto and ledger technologies; Microcontrollers for assisted patient living; Quantum computing; Software Architecture and Evolution; Continuous Delivery; Advanced Programming Concepts; Microservice Architectures; Natural Language Processing; Computer Vision; Machine Learning for Medical Imaging;

  2. DevOps
    In this module, you will have the opportunity to learn about the latest industrial practices for working on large, existing systems and creating new effective disruptive solutions. DevOps is both an evolution of agile methodologies and a transformation of how software systems can be created, by resolving the conflicts between discordant goals and incentives of Development and Operations. This module will motivate the issues underpinning the need for DevOps and then explain DevOps in terms of agile, continuous delivery and the three “ways” – the principles of flow, of feedback, of continual learning and experimentation. The module will teach you to how to implement DevOps through value stream mapping and integrating operations into development. It will examine how each of the sets of principles can be implemented through technical practices, e.g., flow through deployment pipelines, automated testing, continuous integration, etc, feedback through telemetry, testing and review, and learning by sharing best practices etc.  Specialisations of DevOps such DevSecOps where security practices are integrated into the DevOps approach – and MLOps to accelerate the process of gaining business value from machine learning – will also be considered.

  3. Managing Projects and Teams
    At some point in a computing professional’s career, the opportunity to become responsible for people and projects will come. This module will help you become an effective technical manager. Firstly, it will teach the soft skills and people skills needed to be variously a mentor, a tech lead, and a team manager, and how to work effectively at higher levels of management. You will learn how to create a good team culture, how to be a good leader, and how to deal with difficult situations and people. Secondly, it will teach the technical skills of estimation, scheduling, risk management and project management to ensure that you deliver solutions on time, within budget and meet customer needs.

  4. Dissertation
    In this module you will have the opportunity to demonstrate independence and originality, to plan and organise a large project over a long period, and to put into practice the knowledge, skills and research methods that you have learnt throughout the module and the course as a whole


In addition to the above modules, students are required to choose three of the below optional modules:


  1. Advanced Software Engineering
    Software architects decide the high-level structure and behaviour of software. This module explores architectural thinking, core architectural concepts (modules, components, cohesion, coupling), and the critical importance of trade-offs and context. You will learn how to extract architectural characteristics from requirements and from the application domain itself, and how to use this to identify potential components of a solution. You will learn about architectural styles and patterns – client-server, multi-tier, distributed, layered, pipelined, microkernel, service-based, event-driven, etc, how to choose the appropriate style, and how to present your architecture. By the end of the module, you will be able to make the trade-offs among competing “right” and “wrong” answers, and find the best solution for your application.
  2. HCI and User Experience
    We design computer systems for the use of and by human beings. Making the use of computers, systems and software a delight rather than a nightmare requires attention to be paid to the user interface and the entire user experience. This module will cover best practices for a variety of user interfaces – graphical user interfaces, voice user interfaces, mobile and web apps, tangible computing, embedded systems, etc., and teach effective techniques for requirements elicitation, user-centric design and prototyping. It will examine how to plan, design and conduct effective usability testing.
  3. Cloud Computing
    For many organisations, migrating internal applications and externally facing customer solutions to the cloud (AWS, Microsoft Azure, or Google Cloud) is part of their trajectory to controlling costs, adapting to change, and delivering services effectively and elastically with a global reach. . Through this module, you will understand the drivers for cloud computing and when it is an appropriate solution. You will gain comprehensive knowledge and practical skills in how to design and construct cloud solutions to deliver business needs. Your architectures will be secure (in terms of access to resources, workloads and applications), resilient (in terms of highly available, loosely coupled and/or fault-tolerant design choices), high-performing and scalable (in storage, compute, database and networking) and cost-optimised (in storage, compute, database and networking). At the end of this module, you may choose to take the AWS Solutions Architect certification exam.
  4. Cryptography and Applications
    In this module, you will learn how cryptographic techniques can be used to design and implement secure communicating systems for a variety of different needs and applications, and to do so by considering all aspects from theory to more practical issues. You will see how the various key concepts are used to support advanced secure communication systems or protocols, secret sharing schemes, commitment schemes, oblivious transfer, zero-knowledge proofs, and secure multi-party computation.
  5. Cyber Detection and Forensic Investigation
    Cyber security teams are routinely called on to investigate incidents ranging from the downtime of critical resources such as servers and networks, to complex cyber-attacks which lead to loss of resource, reputational damage and potential fines. Digital investigation is the process of identifying and analysing the causes of incidents and providing a robust and comprehensive response and explanation to stakeholders on the cause of an incident and the steps that can be taken to mitigate against it occurring again in the future. The endpoint of a digital investigation is often a report which must clearly, cogently and convincingly attribute the root cause of the incident, whilst at the same time be easily understood by lay audiences which range from members of a court to chief executives in an organisation. This ability to organise important information and present it professionally and clearly is a key skill within the cyber security domain. This module outlines the steps that an investigator must follow in a wide range of incidents and equips participants with the skills required to apply scientific techniques and industry standard tools to a digital investigation and present convincing results. The module draws on case studies of example incidents which require investigation. Participants perform an investigation through the stages of evidence analysis and report writing. Throughout this process, participants are introduced to the range of tools available during an investigation and issues relating to the admissibility of evidence produced by these tools. Participants gain a thorough understanding of how the mode of investigation differs between different types of investigation, for instance corporate and criminal investigations. Participants are made acutely aware of the importance of drawing the correct inference from digital evidence and the significant challenges faced by investigators, namely that digital data is fragile, its quantity may be overwhelming, it may be transient or volatile, it may not be legally accessible, it may not be technically accessible, and its structure may be unclear.
  6. Network and IoT Security
    In this module, you will have the opportunity to gain a broad knowledge of network and web security from the network to the application layer. The emphasis of the course is both on the underlying principles and techniques, and on examples of how such principles are applied in practice. Indicative topics could include: Cyber security overview, Threat analysis and bug finding Internet security, Server­-side security, Client-side security, Secure Web Sessions, Emerging security standards, Online Privacy issues, Network security and cybercrime, Analysis of real-world network security incident (IoT botnet), Email security issues (spam and phishing attacks; spam filtering systems).,Spyware (system vulnerabilities; stealth techniques; detection and removal), Network-related data security (data breaches; data loss prevention; remote sniffer detection), Security of WiFi networks, IoT network security, Network forensics and incident response, Emerging network protocols, IPv6 security, Honeypots and honeynets, Software-defined networking, Penetration testing.
  7. Neural Networks and Reinforcement Learning
    The course provides both basic and advanced knowledge in Neural network algorithms and reinforcement learning across three core skills: theory, implementation, and evaluation. Students will learn the fundamentals of both tabular reinforcement learning and deep reinforcement learning and will gain experience in designing and implementing these methods for practical applications. Specifically, students will learn the theoretical foundations of reinforcement learning (Markov decision processes & dynamic programming); learn the algorithmic foundations of reinforcement learning (temporal difference and Monte-Carlo learning); gain experience in framing low-dimensional problems and implementing solutions using tabular reinforcement learning; learn about the motivation behind deep reinforcement learning and its relevance to high-dimensional applications, such as playing video games, and robotics; discover the state-of-the-art deep reinforcement learning algorithms such as Deep Q Networks (DQN), Proximal Policy Optimisation (PPO), and Soft Actor Critic (SAC); implement and experiment with a range of different deep reinforcement learning algorithms in Python and PyTorch, and learn how to visualise and evaluate their performance.
  8. Applied AI
    Artificial Intelligence (AI) is powering innovations everywhere, from virtual assistants to self-driving cars. You may have heard of the ‘fourth industrial revolution’; AI is a fundamental technology that enables systems to transform our everyday lives. This module will look at applications of AI in various fields (Computer Vision, Natural Language Processing, Explainable AI) at the forefront of this technological revolution such as Machine Learning for AgriTech, AI applications for FinTech, Sports Analytics, Predictive Maintenance and AI, Computer Vision for Disease Detection.
  9. Cloud-Centric AI
    This module will give you a deep grounding of development and cloud technologies underpinning AI and perspectives on deploying AI in a professional environment. Building on the DevOps module, it will help you apply MLOps, working with different end-to-end case studies/ projects to gain a solid understanding of tools, technologies and cloud platforms for AI. You will be working with three Cloud platforms for AI, including understanding cloud architectures for AI and Cloud AI research directions. Topics will include: Machine Learning and Deep Learning implementation in the Google Cloud, Microsoft Azure, and Amazon Web Services (AWS) platforms; Time series development; Industrial and autonomous systems (AS); Embedded Al (Intel, ARM platforms, Nvidia); NLP using GPT-3 and other large language models and foundation models, Anthos and hybrid cloud; Mist AI. At the end of the module, students will have the option to take one of the AWS certification exams appropriate to their expertise (AWS Machine Learning Specialisation or AWS Data Analytics Specialisation exam).

Students that wish to meet the requirements for a specialist pathway award need to choose the following options:

Pathway Options Required
Cyber Security Cryptography and Applications
Cyber Detection and Forensic Investigation
Network and IoT Security
Software Engineering Advanced Software Engineering
HCI and User Experience
Cloud Computing
Artificial Intelligence Neural Networks and Reinforcement Learning
Applied AI
Cloud-Centric AI

Career opportunities

There is extensive research indicating a shortage of advanced specialist computing graduates in industry - for example demand for workers with specialist data skills like data scientists and data engineers has more than tripled over five years (+231%), according to a labour market analysis commissioned for Dynamics of data science skills by the Royal Society and the (ISC)² Cybersecurity Workforce Study surveyed nearly 12,000 cybersecurity professionals worldwide (2022) and identified a global cybersecurity workforce gap of 3.4 million people, indicating strong career prospects for graduates. Many local businesses with whom we have strong links, such as BT, Juniper, Viavi, Gallagher, MSC, local Councils, NHS and other large local employers have strong and sustainable need for individuals with advanced computing training.

Graduates from this programme can work as expert computing professionals in areas of cyber security, cloud computing, distributed systems, network and embedded systems, AI and machine learning specialists in industry in the UK and around

Fees and finance


  • UK full-time tuition fee: £9,495 p.a
  • UK part-time tuition fee: £1,055 per 20 credits (please contact the Student Centre for further information)
  • International full-time tuition fee: £14,607 p.a

Further Information

At the University of Suffolk, your tuition fees provide access to all the usual teaching and learning facilities that you would expect. However, there may be additional costs associated with your course that you will need to budget for.

Entry requirements


Lecturer in Computing

Lecturer and Course Leader for Data Science and Artificial Intelligence

Lecturer in Computing

Facilities and Resources

This degree will be delivered as a mixture of theoretical and practical sessions, making use of facilities at the new Adastral Park DigiTech Centre (in particular the Cyber Security Suite, the Network Embedded Systems Labs, the Data Science and AI labs) and the IT suites on campus in the Atrium Building. As a student on the course, you will have access to library resources, online books and subscriptions to IEEE Xplore and ACM Digital Library and other related publications. You will also have access to industry-standard cloud tools for computing (TryHackMe, HackTheBox).