入学要求 Requirement：
学术要求： Entry Requirements
Good first degree (minimum 2.1 or equivalent) in Computer Science or a related subject at bachelor level.
英语要求：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 580 (paper based test), 230 (computer based test) or 92 (internet based test)
•Pearsons Test of English (PTE) Minimum score of 62 with no less than 47 in each component
•GCSE or GCE Ordinary Level English Language at grade C or above
Applicants who have previously studied in the English language may not be required to provide evidence of English language ability.
学费 Tuition Fee：Fees And Funding
Fees And Funding
Tuition Fees 2011/12
•UK/EU £4,500
•International £11,200
Funding
•International (ISF) Scholarships, which cover up to 50% of tuition fees, available for excellently qualified international students.
课程特征 Course Features
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.
课程内容 Course Content ：
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.
•Year 1
Compulsory Study (140 credits)
Students will select 140 credits from the following module(s).
Name Code Credits Semester
Applied Statistics CMPSMC28-B-SEM2 20 Semester 2
Artificial Intelligence and Algorithmics CMPSMA24-B-SEM2 20 Semester 2
Data Mining CMPSMC24-B-SEM2 20 Semester 2
Dissertation CMPSMP6X-B-SEM2 60 Semester 2
Research Techniques CMPSMP2Y-A-YEAR 20 Semesters 1 & 2
Option A Study (40 credits)
Students will select 40 credits from the following module(s).
Name Code Credits Semester
Applications Programming CMPSMA23-A-SEM1 20 Semester 1
Database Manipulation CMPSMB11-A-SEM1 20 Semester 1
Human Computer Interaction CMPSMM23-A-SEM1 20 Semester 1
Information Retrieval and Natural
Language Processing CMPSMB29-A-SEM1 20 Semester 1
教学与评估 Teaching and Assessment：
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
其它信息 Other Information：
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.