Back to Course List
Degree of Master of Science
School of Study
Dr. Beatriz De La Iglesia
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)
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.
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
- 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.
Come and Visit Us
Our Open Days will give you the opportunity to experience the University of East Anglia's unique campus atmosphere.
Compulsory Study (140 credits)
Students must study the following modules for 140 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.
ARTIFICIAL INTELLIGENCE AND ALGORITHMICS
This module introduces the students to core techniques in Artificial Intelligence an 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.
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
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.
Option A Study (40 credits)
Students will select 40 credits from the following modules:
Students without database experience MUST take CMPSMB11 and students without programming experience MUST take CMPSMA23.
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.
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.
HUMAN COMPUTER INTERACTION
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 empasis on experimental evaluation. The module covers aspects of cognitive psychology and ethnographic methods necessary to understand and evaluate HCI.
Nowadays, millions of people worldwide make use of IR systems every day via search engines, and the exponential increase in the number of websites and documents available means that these systems have been developed to be highly efficient. In this module, we will cover the essential theoretical ideas that underpin modern information retrieval (e.g. the vector-space model, probabilistic approaches, relevance feedback etc.) and examine how they are practically implemented in current systems. Lecture material is re-enforced by a set of laboratory exercises and an assessment that enable you to implement some of these ideas practically. We also examine natural language processing techniques that are increasingly used in IR, and the emerging technologies of audio and video retrieval.
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.
- 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 overall (minimum 19 in the listening and writing components; 20 in the reading component; and 21 in the speaking component)
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 firstname.lastname@example.org (INTO UEA Norwich) or email@example.com (INTO UEA London).
Fees and Funding
Tuition Fees 2014/2015
- UK/EU £6,000
- International £12,900
Faculty of Science Scholarships:
One half fees scholarship is available to students on any MSc course in the Faculty of Science.
Four £2,250 scholarships are available to international students on any MSc in the School of Computing Sciences.
For more information, please contact the Computing Science Postgraduate Admissions Office: firstname.lastname@example.org.
Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.
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.