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MSc Advanced Computing *

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RS2725 Open Day (5) (2)
Institution code: 

One year full-time.

Two years part-time.

September intake.

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
    Emerging technologies are innovations regarded as developments helping a field move forward at a competitive advantage. In this module you will look at such innovations within computing that are helping push the field to its boundaries and are contributing to the continuous rapid advancement of technologies and applications. You will have the opportunity to be exposed to the latest advances at the frontiers of computing via a series of invited talks, research activities and self-enquiry. 

  2. DevOps
    Delivering value quickly, securely and reliably to customers is as vital to companies as it is to software consultancies and giant technology firms (such as Google, Microsoft, Apple and Meta). DevOps, which is an evolution of agile development approaches, has become the key industrial practice to achieving these goals.  In this module, you'll gain an understanding of how DevOps combines Development and Operations to achieve a fast flow of planned work into production, where its successful implementation means fewer problems experienced by customers and faster fixes to those problems.  

  3. Managing Projects and Teams
    As you gain more experience as a computing professional, at some point, opportunities will arise to move beyond the role of a competent team player and project contributor. This can involve taking on responsibility for people becoming an engineering manager or can involve taking on leadership of a project or both. The career progression paths are sometimes known as the Manager’s Path and the Staff Engineer’s Path. Both career pathways necessitate a grasp of soft skills and people skills whether to manage staff members reporting to you or influence without authority. In this module, you'll learn how to manage and/or lead technical projects to successful outcomes including skills in estimation, scheduling, risk management and project management.

  4. Masters Dissertation
    This module provides you with an opportunity to apply the knowledge and skills you have acquired so far from your course on a single significant technical project. It will require you to utilise practical, intellectual and decision-making skills in novel situations. The project will provide a mechanism for you to develop and demonstrate your autonomy and self-direction, whilst undertaking a problem-solving approach to a chosen topic. To support you with this, you will be allocated a Project Supervisor who is a member of the Technology Academic Team. You will meet with this supervisor on a regular basis.


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


  1. Advanced Software Engineering
    Software engineering is the systematic, disciplined and quantifiable approach to the development, operation and maintenance of software. Over time, the scale and complexity of the problems addressed by software have increased with a corresponding increase in the scale and complexity of the software solutions. In this module you'll learn how to address software engineering problems through the use of advanced techniques in requirements engineering, software architectures, designing for specific attributes, advanced coding, and new testing practices.
  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. Different types of user interfaces necessitate different approaches. There is an ethical, legal and business imperative to ensure that computer solutions are accessible to all users, but accessibility has to be designed. This module will include topics such as user-centred design and UX design processes, user behaviour, usability, prototyping and accessibility.
  3. Cloud Computing
    For many organisations, migrating 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. This module explores cloud ecosystems, the drivers for cloud computing and when it is an appropriate solution. It provides practical skills in how to design and construct cloud solutions to deliver business needs. The resultant 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) and sustainable (energy efficient).
  4. Cryptography and Applications
    Cryptography has always been an essential tool in the armoury of a cyber security professional, not least in its obvious use in protecting confidentiality. Over time cryptographic techniques have expanded to include applications to areas such as digital signatures, secure hashing and public key infrastructures. Thus, anybody wishing to pursue cyber security through an industrial or academic pathway will need a strong foundation in cryptographic techniques. This module will provide those foundations, with the right mix of theory and practical work to bring techniques to life. 
  5. Cyber Detection and Forensic Investigation
    This module addresses the growing demand for skilled cyber security and digital forensics professionals. This module is designed to equip students with the necessary knowledge and practical skills to successfully detect, analyse, and respond to cyber security incidents and conduct comprehensive forensic investigations. The module recognises the challenges associated with the increasing volume and diversity of data sources involved in digital forensic investigations and teaches students how to manage and overcome these challenges. By the end of the module, students will have the knowledge and skills necessary to contribute to digital forensic investigations in various contexts, including criminal investigations, civil litigation, and corporate investigations.
  6. Network and IoT Security
    This module covers basic and advanced security concepts related to wired/wireless networks and Internet of Things (IoTs). The module is focused mainly on presenting security issues that are common to wired/wireless and IoT deployments and environments, whilst maintaining a high-level view of general security aspects. The approach taken aims at first introducing the ecosystem of network topologies with the intention of producing students who will fully understand the benefits and requirements of the IoT system design cycle, from design and validation to deployment. This module will also cover critical design considerations that have emerged with the evolution of the Internet of Things, such as cybersecurity, coexistence, compliance, and continuity. Furthermore, the module presents common defence techniques and tools used to counter different security threats. Whilst the bulk of the module is aimed at Wired/Wireless Local Area Network, Smart Home (WLAN), other protocols are examined as well.
  7. Neural Networks and Reinforcement Learning
    This module introduces fundamentals of deep learning and how it can solve problems in many areas, such as image classification, filter design and natural language processing. Neural networks are first described and how training can be achieved with backpropagation. Various forms of deep neural networks are developed, such as multilayer perceptrons, convolutional neural networks and deep reinforcement learning. The mathematics of stochastic optimisation is used to interpret and understand the behaviour and training of these networks. Programming approaches are discussed for training and deploying neural networks.
  8. Applied AI
    Artificial Intelligence (AI) includes many techniques for classification, analysis and prediction. This module aims to give students an appreciation of the types of application areas and problems that advanced AI techniques can enhance and optimise including artificial intelligence in business and financial applications, artificial intelligence in games, artificial intelligence in health sciences and medicine, and artificial intelligence in industrial control. These techniques also have the advantage of being “explainable AI,” more so than deep learning approaches, and some are long established techniques of “business intelligence.”
  9. Computer Vision
    Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labelling objects. This module aims to develop an understanding of methods for extracting useful information (eg 3-D structure; object size, motion, shape, location and identity, etc) from images. It will allow you to understand how to construct computer vision systems for robotics, surveillance, medical imaging, and related application areas. 


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
Computer Vision

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 and Course Leader for Data Science and Artificial Intelligence

Lecturer in Computing

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).