MSc Computing Science

Key facts

(National Student Survey, 2015)


Computer Scientists have developed a new programme that will unearth the missing links in our planet’s past.

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Key facts

(2014 Research Excellence Framework)

Our core Computing Science MSc is designed for graduates of non-computing subjects who might be interested in a new career direction or who simply want to learn about the subject. The course gives you a firm grounding in the fundamentals of computing, with plenty of options to steer learning in your chosen direction – you’ll also develop valuable skills in project management, research, communication and team work which are extremely attractive to potential employers.

The degree culminates in a Masters dissertation which gives you the chance to specialise in a specific topic and work closely with our world-leading academics. We’ve got strong research expertise in areas as diverse as Artificial Intelligence, Audio and Visual Processing and Data Mining, and our research was highly rated in the latest Research Excellence Framework (2014).


Why take this course?

The MSc Computing Science is designed for the graduates of non-computing subjects to study computer technologies and skills to broaden their knowledge and to create new career prospects. It is a 12-month full-time course but may be studied part-time over 24 months.

The training in this course not only teaches essential computing technical knowledge but also develops generic, transferable skills such as in communication, critical thinking and reasoning, problem solving, independent and team working and project management, with an aim to make the graduates of this course professionally competitive and flexible in a challenging and changing employment environment. Thus, the graduates can find employment in a wider range of careers in industry, business, public sectors, education and research institutions, working as diverse roles, ranging from, software developers, systems analysts, data analysts, IT managers, to independent consultants and academic or commercial researchers.

Contact time

Students have on average 15 hours of contact time per week with teaching staff through lectures, laboratory sessions and seminars, though this may vary depending on module choices. Additionally, students should allocate at least 25 hours per week for study, coursework assignments and projects.

Teaching and Assessment

The course is delivered through lectures, seminars, directed studies and laboratory exercises, involving individual and team work. The assessments are carried out by coursework and/or written examinations. Students will learn modules including Research Techniques, Object-Oriented Programming (in java) and Software Development Methodologies with UML (Unified Modelling Language), Databases, and Internet and Multimedia Technology. These are all integrated in a Web based framework and students are grouped as teams to design and implement a substantial Web-based application. Students also take few optional modules, from the optional module list, which includes Data Mining, Applied Statistics, Networks, Systems Engineering, Systems Development, Artificial Intelligence, Image, and Speech and Language Processing.

Starting in the Spring Semester to August, students will undertake an MSc Dissertation project on a topic that is usually related to the School's research areas, often in collaboration with an outside body. A project requires students to apply the knowledge and skills they have learned from the course to carry out in-depth research on a topic, or develop a working system for various applications. Some project work may be done with companies and could involve paid placement at a company.

Samples of recent dissertation titles:

  • Hybrid positioning technologies for location based services with iPhone
  • Predicting earthquakes with time series data mining
  • An application of video shot detection
  • Machine learning ensemble methods for identifying fake web sites
  • Predicting the results of Tennis matches in real time

Career opportunities

As a graduate from this course, you will be able to find employment in private industry, public sector organisations and in research, working in diverse roles, ranging from independent consultants, software developers, systems analysts, data analysts and IT managers to academic or commercial researchers.

"I have found a job as a junior software developer and I am finding that the course has prepared me well for this. Once again I’d like to thank you for getting your students ready for the real-world."

Rungano Mudimu, (former student).

Course Modules

Students must study the following modules for 80 credits:

Name Code Credits


In this module, each Masters student is required to carry out project work with substantial research and practical elements on a specified topic for their MSc dissertation from January to late August. The topic can be chosen and allocated from the lists of proposals from faculty members, or proposed by students themselves with an agreement from their supervisor and also an approval from the module organiser. The work may be undertaken as part of a large collaborative or group project. A dissertation must be written as the outcome of the module.




This module aims to prepare postgraduate students with necessary intellectual and practical skills for successfully carrying out research work for their MSc Dissertation in Computing Sciences and Computational Biology. Specifically, it teaches research methodologies, techniques and tools used in computing sciences, and more importantly, provides systematic trainings to enhance students' transferable skills and their understanding in ethics, social and legal issues involved in computing professions.



Students will select 60 credits from the following modules:

Name Code Credits


The module aims to establish a clear understanding of Object Oriented Programming (OOP) and essential Objected Oriented Methodologies for developing application software. It teaches Java programming language and uses it as a vehicle to learn important concepts, such as objects, classes, inheritance, encapsulation and polymorphism. It also covers the Unified Modelling Language (UML) as a tool for object-oriented analysis and design, software development life cycle models, and software testing strategies and techniques.




This module introduces most aspects of databases, database manipulation and database management systems. Practical experience of database manipulation is provided through the use of SQL and the Java JDBC interface on a relational database management system. Database design is introduced using Entity-Relationship modelling and normalisation.




This module surveys the current and emerging technologies of the Internet and its impact on society, particularly e-commerce. The practical part of the module concentrates on the design and integration of web sites, using a range of tools and techniques in current use.



Students will select 40 credits from the following modules:

Students who have studied the material covered in Option Range A modules may ask for exemption on the basis of prior learning, or may ask for a concession to study suitable alternative modules.

Name Code Credits


This is a module designed to give students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have some background in probability and statistics before taking this module. The aim is to introduce students to R statistical language and to cover Regression, Analysis of Variance and Survival analysis. Other topics from a list including: Extremes and quartiles, Bootstrap methods and their application, Sample surveys, Simulations, Subjective statistics, Forecasting and Clustering methods, may be offered to cover the interests of those in the class. BEFORE TAKING THIS MODULE you have taken CMP-4004Y or CMP-4005Y or CMP-4006Y or have an equivalent basic knowledge of probability and statistics.




Computer Vision is about "teaching machines how to see". It includes methods for acquiring, analysing and understanding images. The unit comprises lectures and laboratories. Practical exercises and projects, undertaken in the laboratory support the underpinning theory and enable students to implement contemporary computer vision algorithms.




This module is designed for postgraduate students studying on MSc courses. The module explores the methodologies of Knowledge Discovery and Data Mining (KDD). It aims to cover each stage of the KDD process, including preliminary data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining. Through this module, students should gain knowledge of algorithms and methods for data analysis, as well as practical experience using leading KDD software packages.




Single computer systems have limited processing power and are vulnerable to failure. Using distributed computing, processing speeds exceeding the limits of any single computer, and systems that continue to be available when individual computers fail can be realised. Achieving these features requires use of adequate algorithms, software architectures and networking techniques. This is the subject of this module.




This module is an introduction to information visualization. It covers techniques for summarizing and presenting a wide range of data. The problems and techniques for dealing with large data flows are a major theme and there is a strong emphasis on understanding the appropriate context and use of visualization techniques.




Embedded processors are at the core of a huge range of products e.g. mobile telephones, cameras, passenger cars, washing machines, DVD players, medical equipment, etc. The embedded market is currently estimated to be worth around 100x the 'desktop' market and is projected to grow exponentially over the next decode. This module considers the design and development of real-time embedded system applications for commercial off the shelf (COTS) processors running real time operating systems (RTOS) such as ARM-RT, uCLinux etc.




This module draws together a wide range of material and considers it in the context of developing modern large-scale computer systems. Topics such as Outsourcing, Process Improvement, System Failure, Project Management, Configuration Management, Maintainability, Legacy Systems and Re-engineering, Acceptance and Performance Testing, Metrics and Human Factors are covered in this module. The module is supported by a series of industrial case studies and includes speakers from industry.




Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules and regular (five-yearly) review of course programmes. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, there will normally be prior consultation of students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff or sabbatical leave. Where this is the case, the University will endeavour to inform students.

Entry Requirements

  • Degree Classification Good first degree (minimum 2.1 or equivalent). at bachelor level.

Students for whom English is a Foreign language

We welcome applications from students whose first language is not English. To ensure such students benefit from postgraduate study, we require evidence of proficiency in English. Our usual entry requirements are as follows:

  • IELTS: 6.5 (minimum 6.0 in all components)
  • PTE (Pearson): 62 (minimum 55 in all components)

Test dates should be within two years of the course start date.

Other tests, including Cambridge English exams and the Trinity Integrated Skills in English are also accepted by the university. The full list of accepted tests can be found here: Accepted English Language Tests

INTO UEA also run pre-sessional courses which can be taken prior to the start of your course. For further information and to see if you qualify please contact

Fees and Funding

Tuition Fees for 2017/18

  • Home/EU:

     Full-time £7,300, Part-time £3,650

     If you choose to study part-time, please assume a pro-rata fee for the credits you are taking, or 50% of the equivalent fee per year if you are taking a full-time course on a part-time basis.  

  • Overseas:

      Full-time £14,800

      If you are classed as an 'overseas' student and are coming to UEA on a student or visitor's visa, UK visa rules won't normally allow you to study on a part-time course. You should always check with the UKVI for the latest requirement.



50% Final Year Undergraduate Continuation Scholarship

Current final year UEA undergraduate students who gain a First class degree and progress onto a postgraduate course in September 2017 will receive a 50% fee reduction scholarship. Who do not gain a First class degree will be eligible for the 10% UEA Alumni Scholarship outlined below. Terms and conditions apply.


UEA Alumni 10% Scholarship

A scholarship of 10% fee reduction is available to UEA Alumni looking to return for postgraduate study at UEA in September 2017. Terms and conditions apply.


How to Apply

Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.

You can apply online, or by downloading the application form.

Further Information

To request further information & to be kept up to date with news & events please use our online enquiry form.

If you would like to discuss your individual circumstances prior to applying please do contact us:

Postgraduate Admissions Office
Tel: +44 (0)1603 591515

International candidates are also encouraged to access the International Students section of our website.

    Next Steps

    We can’t wait to hear from you. Just pop any questions about this course into the form below and our enquiries team will answer as soon as they can.

    Admissions enquiries: or
    telephone +44 (0)1603 591515