MSc Knowledge Discovery and Datamining

Key facts

(National Student Survey, 2015)


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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.


Why take this course?

  • This course offers an excellent platform to a career in data analysis and is taught by one of the leading groups in Data Mining research in the UK.
  • The course has both theoretical and practical elements and students will get hands on experience on commercial data mining and statistical software.
  • Students may have the opportunity to participate on commercial data mining projects as part of their assessment, gaining experience on all the stages of the KDD process.
  • This programme has full Chartered IT Professional (CITP) accreditation (Further Learning Element) as well as partial fulfilment of Chartered Engineer (CEng) status from the (BCS - The Chartered Institute for IT)

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Career opportunities

As a graduate from this course, you will be prepared for a career in data analysis. Job postings for data analysts, also called data scientists, are increasing rapidly (see graph taken from the LinkedIn Corp).

The average salary associated with jobs in Data Mining for the UK, in the three months to 18 April 2016, was £55,000 (Source:

The degree can also act as a very good platform for a research degree in KDD.

What is KDD?

All organisations depend on high quality information for making strategic decisions.  The information is often derived from the rapidly growing mountains of raw data generated from the organisations’ computerised operational systems. This task requires a new generation of analysts with knowledge of effective and efficient data analysis methods and understanding of the process known as Knowledge Discovery and Data Mining (KDD).

The popularity of this area is driven by its tremendous application potential in areas as diverse as finance, medicine, biology and the environment.

The course is a full-time, one-year taught programme, designed for advanced students and practitioners; it can also be taken part-time over two years.

Why study this subject at UEA?


The Data Mining, Machine Learning and Statistics group at UEA comprises eight faculty members, eight research assistants and between 10 and 20 PhD students.

Members of the group have made significant contributions in techniques for data mining and KDD in the last 10 years, in particular: KDD Methodologies; use of metaheuristics for rule and tree induction; all-rule induction; clustering techniques; feature subset selection; feature construction, as well as many applications in the financial services industry, medicine and telecommunications.

Support for this research has been received from BBSRC, EPSRC, the Institute and Faculty of Actuaries and The Royal Society, as well as numerous companies (including Alston Transport, Derbyshire Police, Lanner Group, Master Foods, MET Office, National Air Traffic Services, Aviva, Process Evolution Ltd., Simultec AG Zurich and Virgin Money).

Master students will be part of our vibrant research community and will have very good opportunities for progression to PhD.

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

On this course you will take compulsory modules in research techniques, data mining, statistics and artificial intelligence or visualisation, as well as two optional modules from a range, which may include applications programming, database manipulation, information retrieval and NLP, or a research topic.

Assessment will be conducted using a variety of formats including essays, project reports, presentations, and examinations.

Some project work may be done with companies and could involve paid placement at a company. 

You can either choose from a number of related dissertation topics proposed by faculty or formulate your own project proposal. These projects often address real-world problems.

Recent dissertation titles:

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

Course Modules

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 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.




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.




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 20 credits from the following modules:

Name Code Credits


This module introduces the students to core techniques in Artificial Intelligence and some topics in algorithmics. Topics covered include First-Order logic and resolution proofs, introduction to Prolog programming, state space representation and search algorithms, knowledge representation, and expert systems, Bayesian and neural networks.




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.



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, and software testing strategies and techniques.




This module introduces the students to core techniques in Artificial Intelligence and some topics in algorithmics. Topics covered include First-Order logic and resolution proofs, introduction to Prolog programming, state space representation and search algorithms, knowledge representation, and expert systems, Bayesian and neural networks.




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.




An overview of Human Computer Interaction, including user interfaces on conventional computers and small footprint devices (e.g. smartphones). Human-Computer interactions are approached from a variety of perspectives, with an emphasis on experimental evaluation.




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.




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 Subject Computing, Mathematics or a related subject.
  • Degree Classification Good first 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 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 2016 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 2016. Terms and conditions apply.


Faculty of Science International Scholarships

International students applying to this course can be considered for one Faculty of Science half-fees scholarship (up to £8,000 for one year) or one of two £2000 scholarships.  



To be eligible to apply for these scholarships you must meet the following criteria:


  • Be classified as an international (usually non-EU resident) applicant.  See our Fees and Funding page if you are unsure of your fee status. 
  • Hold a conditional or unconditional offer for one of our MSc programmes starting in September 2016.
  • Be independently funding your studies - i.e. students sponsored by organisations that are funding their fees are not eligible to apply.  Students applying for or expecting to receive loans are eligible to apply.
  • Awards are for one year of study only.  

Application deadline: 15th May 2016


How to apply:


Once you have an offer of a place to study with us in September 2016, please email us your answers to the following two questions:


  • Question 1: In approximately 250 words, please tell us your reasons for applying to the University of East Anglia 
  • Question 2: In approximately 250 words, please explain how your postgraduate degree at the University of East Anglia will enable you to make a difference in the future.

Please include your UEA application number, name and the course you have applied for in your email.  You must already be holding an offer of a place on an eligible course to apply.  Emails should be sent to

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

    Need to know more? Take a look at these pages to discover more about Postgraduate opportunities at UEA…

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