Postgraduate Taught Degrees

MSc Knowledge Discovery and Datamining

Duration:: 1 years
Attendance:: Full Time
Award:: Degree of Master of Science
School of Study:: Computing Sciences
Brochure:: Faculty of Science Postgraduate Brochure (PDF)

Overview
Why Choose Us
Course Profile
Requirements
Fees and Funding
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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 will 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 leading to Chartered Engineer (CEng) status from the (BCS - The Chartered Institute for IT)

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 salaries associated with jobs in Data Mining for the UK, during the 2010-2012 are between £44,000 and £52,000 (Source: http://www.itjobwatch.co.uk/)

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. As such, it is one of the largest such groups in the UK.

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 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 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 Organiser:Dr. Beatriz De La Iglesia

95% of research activity was classified as internationally leading, excellent or recognised in the 2008 Research Assessment Exercise.
Our Masters programmes are accredited by the BCS - The Chartered Institute for IT to full Chartered IT Professional (CITP Further Learning Element) as well as leading to Chartered Engineer (CEng) status.
The School maintains close links with industry and many of our student assignments relate to real world problems.
The School of Computing Sciences leads the university in utilizing its internationally recognized research commercially, through SYS Consulting, its Consulting company and through Knowledge Transfer Partnerships.
Our students also use their technical and entrepreneurial skills to play influential roles in the creation of companies such as Travel Republic, Evoke systems, Fyfo and Shoretech systems.

Come and Visit Us

Our Open Days will give you the opportunity to experience the University of East Anglia's unique campus atmosphere.

Year 1

Compulsory Study (140 credits)

Students must study the following modules for 140 credits:

Name	Code	Credits
APPLIED STATISTICS	CMPSMC28	20
This is an applied statistics module designed to give Masters 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 done some background in probability and statistics before taking this course. The aim is to cover 4 topics from a list including: Estimating population abundance; Extremes and quantiles; Linear models; Bootstrap methods and their application; Sample surveys; Simulations; Subjective statistics (MCMC); Forecasing; and Clustering methods. If there is a demand for a specific topic we will always consider providing a project in that area if possible. The assessment will be by coursework. More...
ARTIFICIAL INTELLIGENCE AND ALGORITHMICS	CMPSMA24	20
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems. More...
DATA MINING	CMPSMC24	20
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 data gathering, preliminary data anaylsis or 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 each stage of the process, as well as practical experience using leading KDD software packages throughout all of the stages of KDD process. More...
DISSERTATION	CMPSMP6X	60
Reserved for postgraduates in the School of Computing Sciences, this module provides the student with a piece of individual work, with substantial research and practical elements. The subject of the dissertation will be determined by agreement between the student and his or her supervisor. The work may be undertaken as part of a large collaborative or group project. More...
RESEARCH TECHNIQUES	CMPSMP2Y	20
A module that provides students with the training of some transferable skills, an overview of research methods used in computing sciences, and introduces individual students to background material preparatory to their dissertation project which they would not otherwise study systematically to the required depth. The learning objectives for this module are to enable students to approach the dissertation with the intellectual and practical skills necessary to successfully complete a masters dissertation in Computer Science. More...

Option A Study (40 credits)

Students will select 40 credits from the following modules:

Name	Code	Credits
APPLICATIONS PROGRAMMING	CMPSMA23	20
This module aims to establish a clear understanding of the Object Oriented Programming paradigm, including Encapsulation, Inheritance and Polymorphism. Simple data structures will be considered and classes will be indentified as vehicles for implementing abstract data types. The benefits of modular software design will be emphasised and the Unified Modelling Language (UML) will be introduced as a design tool for large software systems. Topics include: Data structures, programming, and program design. More...
DATABASE MANIPULATION	CMPSMB11	20
This module provides an overview of the philosophy and development of database technology. Practical experience of database manipulation is provided through the use of SQL and the Java JDBC interface on the IBM DB2 database management system. Database design is introduced using Entity-Relationship modelling and normalisation. More...
HUMAN COMPUTER INTERACTION	CMPSMM23	20
An introduction to Human Computer Interaction including user interfaces on conventional computers and small footprint devices (e.g. PDAs and smart mobile phones). Human-Computer interactions are approached from a variety of perspectives: cognitive, ethnographic, socio-technical and systems theoretic. The module covers aspects of cognitive psychology and ethnographic methods necessary to understand and evaluate HCI. It brings together established practice and newer perspectives in order to fully understand what is meant by the usability and accessibility of modern interactive systems. More...
INFORMATION RETRIEVAL	CMPSMB29	20
The module explores the development of Information Retrieval technologies, which have been driven by large increases in on-line documents and the Internet search engines, surveys multimedia and cross-language IR, reviews, current NLP techniques and their role in IR. More...

Disclaimer

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)
TOEFL: Internet-based score of 88 (minimum 18 in listening, 21 speaking, 19 writing and 20 reading)
PTE (Pearson): 62 (minimum 55 in all components)
Test dates should be within two years of the course start date.

Other tests such as TOEIC and the Cambridge Certificate of Advanced English are also accepted by the university. Please check with the Admissions Office for further details including the scores or grades required.

INTO UEA and INTO UEA London 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 intopre-sessional@uea.ac.uk (INTO UEA Norwich) or pseuealondon@into.uk.com (INTO UEA London).

Fees and Funding

Tuition Fees 2013/14

UK/EU £5,000
International £12,500

Funding

International applicants applying to this course can be considered for one Faculty of Science half fees scholarship or one £2000 scholarship. The deadline is 1st April 2013.

Faculty of Science Scholarships
Students wishing to apply should submit an essay answering the following question in 1000 words: 'Is it OK to let the data speak for itself'? Essays should be emailed to the Admissions Office. Please ensure you include your full name, the course you have applied to, and your applicant number in your email.

For more information please contact the Computing Sciences Postgraduate Admissions Office (cmp.pgt.admiss@uea.ac.uk).

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
Email: admissions@uea.ac.uk

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