MSc Data Science


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Key facts

(2014 Research Excellence Framework)

This course is designed to train highly qualified data analysts – or data scientists – to embark on careers in a wide range of industries. You’ll be given an excellent practical and theoretical grounding in data mining and statistics with the chance to customise your degree through modules in artificial intelligence, visualisation, programming and database manipulation.

Data Scientists are highly prized for their advanced, practical skill set and their increasing importance to the success of a modern business. Organisations in almost any industry need to source, analyse and utilise vast amounts of data to aid strategic decision-making, so you’ll have great graduate career prospects as well as a wide range of transferrable skills.

We have a large Data Mining, Machine Learning and Statistics research group, which has made significant contributions to the field in the last 10 years, so you’ll be working directly with pioneering experts.


Organisations today have a vast amount of raw data generated from their computerised operational systems. So how will they turn this into high quality information for strategic decision-making? They need a new generation of data analysts who understand effective and efficient data analysis methods and the Knowledge Discovery and Data Mining (KDD) process.

This course – one of the most established in this area with over 15 years of history – offers an excellent platform to help you forge a successful career in data analysis.

As a student, you will be part of our vibrant research community and will have very good opportunities to progress to a PhD. You will be part of a research group that has made significant contributions in techniques for data mining and KDD – including KDD Methodologies; use of metaheuristics for rule and tree induction; all-rule induction; clustering techniques; feature subset selection; feature construction; time series classification as well as many applications in the financial services industry, medicine and telecommunications.

The research group has collaborated in research or consultancy projects with a wide range of organisations, including: the Biotechnology and Biological Sciences Research Council (BBSRC), the Engineering and Physical Sciences Research Council (EPSRC), the Institute and Faculty of Actuaries and The Royal Society, Alston Transport, Derbyshire Police, Lanner Group, Master Foods, MET Office, National Air Traffic Services, Aviva, Process Evolution Ltd, Simultec AG Zurich, Virgin Money and the Norwich Football Club.

What’s more, this degree has full Chartered IT Professional (CITP) accreditation (Further Learning Element)as well as partial fulfilment of Chartered Engineer (CEng) status from The Chartered Institute for IT (BCS).

You will graduate with a wealth of knowledge, prestigious connections and research experience – putting you one step ahead of other graduates in your career or further studies.

Course Structure

The MSc Data Science course is a full-time, one-year taught programme, designed for advanced students and practitioners. You can also take it part-time over two or three years.

On this course you will take compulsory modules in research techniques, data mining, statistics and artificial intelligence or visualisation.

Alongside this you’ll take two optional modules from a range – which may include applications programming, database manipulation, human computer interaction, computer vision or a research topic.

A key element of the course is your dissertation, which will give you the chance to explore a topic or work on a problem (which may be with an industry partner) in depth, under the supervision of a member of faculty. 

Recent dissertation titles include:

  • Classification rule induction for atmospheric circulation patterns
  • Keyword-based email classification
  • Data analysis of orthopaedic operations

Teaching and Learning


You will have an average of 15 hours of contact time per week with teaching staff, depending on your module choices. This will be made up of a mixture of lectures, seminars and lab classes – where the lab and seminar classes reinforce and expand on the lecture material.

The course has both theoretical and practical elements, so you’ll get hands on experience in commercial data mining and statistical software. You will even have the opportunity to participate in commercial data mining projects as part of your assessment, gaining experience on all the stages of the KDD process. 

Independent study

Your individual study (around 25 hours per week) will complement the formal teaching and will evolve along with your skills and expertise in data analysis. Beginning with an initial focus on the basics of programming and data manipulation, you’ll move on to much deeper study and appreciation of specialist topics such as data mining and statistics. 

Your dissertation will also form a key part of your course, which will involve extensive independent study supported by your supervisor.


We’ll assess your work in different ways depending on the module content and learning objectives. These might include programming assignments, essays, class tests, problem sheets, laboratory reports, presentations and demonstrations.

Most modules are assessed through a mixture of coursework and exams, while some are entirely assessed by coursework. In your dissertation, you will be assessed particularly on your understanding and how you integrate what you’ve learnt to solve a real problem.

After the course

You’ll graduate ready for a career in data analysis or data science – an area of rapid growth at the moment.

You can expect to earn a high salary – the median annual wage for data science in the UK was 60,000 (source

Career destinations

Examples of careers that you could enter include;

  • Data scientist
  • Data analyst
  • Data miner
  • Business intelligence analyst

Course related costs

Please see Additional Course Fees for details of course-related costs.


This course has been accredited by the British Computer Society for full CITP and partial CEng. Accreditation means that a candidate has fully or partially fulfilled the academic requirement for registration as a Chartered IT Professional (CITP) and Chartered or Incorporated Engineer (CEng / IEng) and / or a Chartered Scientist (CSci) and / or Registered IT Technician (RITTech).

The current period of accreditation is for a five year period, from the 2016 student cohort intake to the 2020 student cohort intake.

We would expect to apply for renewal of accreditation at the end of this period.

Course Modules 2020/1

Students must study the following modules for 120 credits:

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 teach the R statistical language and to cover a range of topics in applied statistics, such as: Linear Regression.




This module is designed for postgraduate students studying on MSc courses. You will explore the methodologies of Knowledge Discovery and Data Mining (KDD). You will 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, focusing on clustering, classification and association rule induction. Through this module, you will gain knowledge of algorithms and methods for data analysis, as well as practical experience using leading KDD software packages.




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. More importantly, it provides systematic training to enhance students' transferable skills and their understanding in ethics, social and legal issues involved in computing professions.



Students will select 20 credits from the following modules:

Name Code Credits


This module will introduce you to core techniques in Artificial Intelligence. Topics covered may include state space representation and search algorithms, knowledge representation, expert systems, Bayesian networks, Markov Models, Neural networks, Deep learning and an Introduction to Robotics and Drone.




This module is an introduction to information visualisation. You will learn techniques for summarising and presenting a wide range of data. There is a strong emphasis on understanding the appropriate context and use of visualisation techniques. You will also learn about problems and techniques for dealing with large data flows and issues of integrating multiple data sources.



Students will select 40 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, software testing strategies and techniques and version control.




This module will introduce you to core techniques in Artificial Intelligence. Topics covered may include state space representation and search algorithms, knowledge representation, expert systems, Bayesian networks, Markov Models, Neural networks, Deep learning and an Introduction to Robotics and Drone.




This module explores how computers process audio and video signals. In the audio component, the focus is on understanding how humans produce speech and how this can be processed by computer for speech recognition and enhancement. Similarly, the visual component considers the human eye and camera, and how video is processed by computer. The theoretical material covered in lectures is reinforced with practical laboratory sessions. The module is coursework only and requires you to build a speech recogniser capable of recognising the names of students studying the module using both audio and visual speech information.




Computer Vision is about "teaching machines how to see". You will study methods for acquiring, analysing and understanding images in both lectures and laboratories. The practical exercises and projects that you undertake in the laboratory will support the underpinning theory and enable you to implement contemporary computer vision algorithms.




This module looks into how best to perform database manipulation, exploring techniques that allow us to use and manage data of various types efficiently. We will examine methods to ensure that correctness of data, in terms of availability, reliability, consistency and scalability can be achieved and maintained. In turn, this will provide opportunities to turning data into knowledge intelligently and informing stakeholders alike and helping them to make better decisions.




Human Computer Interaction (or UX) covers a very wide range of devices, including conventional computers, mobile devices and "hidden" computing devices. In this module you will learn about interactions from a variety of perspectives, such as cognitive psychology, ethnographic methods, security issues, UI failures, the principles of good user experience, heuristic and experimental evaluation approaches and the needs of a range of different audiences.




This module is an introduction to information visualisation. You will learn techniques for summarising and presenting a wide range of data. There is a strong emphasis on understanding the appropriate context and use of visualisation techniques. You will also learn about problems and techniques for dealing with large data flows and issues of integrating multiple data sources.




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. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will endeavour to inform students.

Further Reading

Entry Requirements

  • Degree Subject Computing, Mathematics or a related subject.
  • Degree Classification Bachelors degree (minimum 2.1 or equivalent).

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 5.5 in all components)
  • PTE (Pearson): 58 (minimum 42 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


The next intakes for this course are: September 2020 / February 2021 / September 2021 / September 2022.

Fees and Funding

Tuition fees for the academic year 2020/21 are:

  • UK/EU Students: £7,850 (full time)
  • International Students: £16,400 (full time)

If you choose to study part-time, the fee per annum will be half the annual fee for that year, or a pro-rata fee for the module credit you are taking (only available for UK/EU students).


Living Expenses

We estimate living expenses at £1,015 per month.



A variety of Scholarships may be offered to UK/EU and International students. Scholarships are normally awarded to students on the basis of academic merit and are usually for the duration of the period of study. Please click here for more detailed information about funding for prospective students.

How to Apply

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

To apply please use our online application form.

Further Information

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