Overview
Why take this course?
All modern organisations depend on high quality information for making strategic decisions, much of which is derived from the rapidly growing mountains of raw data that are generated from the organisations’ computerised operational systems. To analyse this data and recognise useful patterns and trends requires a new generation of analysts.
This specialism requires people who understand techniques for effective and efficient data analysis methods. These techniques are 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.
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 undertake a mix of specialised modules that will give you a thorough knowledge of techniques and tools for knowledge discovery and data mining. You will gain a comprehensive understanding of the role of data in modern business, its collection, storage, maintenance and access. 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. You will acquire experience of working with the commercial tools used to undertake data analysis. 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
Career opportunities
As a graduate from this course, you will be prepared for a career in data analysis. The degree can also act as a very good platform for a research degree in KDD.
Course Organiser
Dr Beatriz De La Iglesia
Why Choose Us?
- 90% of research activity classified as internationally leading, excellent or recognised in the 2008 Research Assessment Exercise.
- Teaching of the highest quality; rated “Commendable” in the most recent Teaching Quality Assessment.
- In the last National Student survey, rated 26th out of over 200 computing departments in England, Scotland and Wales for overall satisfaction.
- In March 2009 the British Computer Society (BCS) renewed accreditation for taught programmes for five years.
- The School has its own consultancy company, SYSCO, through which it maintains close links with industry.
- Some of the projects in the taught programmes may be done in collaboration with industry and could involve paid placements.
Come and Visit Us
Our
Open Days will give you the opportunity to experience the University of East Anglia's unique campus atmosphere.
Entry Requirements
-
Good first degree (minimum 2.1 or equivalent) in Computer Science or a related subject at bachelor level.
Students for whom English is a foreign language
International applicants are required to provide evidence of proficiency in the English language (if English is not their first language).
Preferred qualifications are:
- IELTS Minimum score of 6.5 with a minimum of 6.0 in each component
- TOEFL Minimum score of 92 (internet based test)
- Pearsons Test of English (PTE) Minimum score of 62 with no less than 55 in each component
Applicants who have previously studied in the English language may not be required to provide evidence of English language ability.
Year 1
Compulsory Study (140 credits)
Students will select 140 credits from the following module(s).
| Code |
Credits |
Period |
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.
more...
|
CMPSMC28 |
20 |
Semester 2 |
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...
|
CMPSMA24 |
20 |
Semester 2 |
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.
more...
|
CMPSMC24 |
20 |
Semester 2 |
In this module, Masters students are required to carry out project work with substantial research and practical elements on a specified topic for their MSc dissertation. The topic can be chosen and allocated from the lists of proposals from faculty members, and/or determined by agreement between the students and their supervisor. 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.
more...
|
CMPSMP6X |
60 |
Semester 2 |
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.
more...
|
CMPSMP2Y |
20 |
Year Period |
Option A Study (40 credits)
Students will select 40 credits from the following module(s).
| Code |
Credits |
Period |
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, ecapsulation 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.
more...
|
CMPSMA23 |
20 |
Semester 1 |
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.
more...
|
CMPSMB11 |
20 |
Semester 1 |
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.
more...
|
CMPSMM23 |
20 |
Semester 1 |
Information Retrieval technologies have been driven by large increases in on-line documents and the success of Internet search engines. This module explores the development of surveys in a range of IR topics and the use of natural language processing techniques and their role in IR. Some experience of a high level programming language (e.g. Java) is required.
more...
|
CMPSMB29 |
20 |
Semester 1 |
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.
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
hard copy application form, or by using the application form in the University’s Postgraduate Prospectus.
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.
