Postgraduate Taught Degrees
MSc Knowledge Discovery and Datamining (Industry based) (Part time)
- Duration:
- 2 years
- Attendance:
- Part Time
- Award:
- Degree of Master of Science
- School of Study:
- Computing Sciences
- Brochure:
- Faculty of Science Postgraduate Brochure (PDF)
Why take this course?
- This is a very unique course, short-listed as one of the most innovative collaborations between Business and a University by the East of England Development Agency.
- This MSc is one of the few academic qualifications of this type to be specifically tailor-made for the needs of industry.
- Teaching sessions take place mostly in the company. This in practice means that the students/employees can inter-link their academic work with their everyday work, putting to practices the principles they are learning in the classroom.
- Some of the Modules may take into consideration knowledge and experience that the employees have acquire in the place of work, and may involve line managers in the assessment of skills and knowledge.
- We have run this course for Aviva for the last 14 years producing over 70 MSc graduates. There is scope for running this course for other interested companies
- 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 2010-2012 are between £44,000 and £52,000 (Source: http://www.itjobswatch.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 in 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.
Teaching and Assessment
On this course you will take compulsory modules in data mining, statistics and visualisation as well as project modules on data mining and statistics which will allow you to put what you have learn into practice in the context of your own organisation. There are also optional modules on database manipulation, Marketing Leadership and meta-heuristics.
Assessment will be conducted using a variety of formats including essays, project reports, presentations, and examinations.
The dissertation will again be a project relevant to your company and it will give you the opportunity to show the mastery of the skills you learn through the course.
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.
This is the first year of your taught Masters programme. This course is taught as a rolling programme and a full cycle of all taught modules is completed every 18 to 24 months. Modules in option ranges B and C may be taken in EITHER year of study. In the final year, students will work on their dissertation.
Option A Study (20 credits)
Students will select 20 credits from the following modules:
| Name | Code | Credits |
|---|---|---|
| PROJECT IN DATA MINING | CMPSMC8Y | 20 |
| PROJECT IN STATISTICS | CMPSMC4Y | 20 |
Option B Study (60 credits)
Students will select 60 credits from the following modules:
| Name | Code | Credits |
|---|---|---|
| DATA MINING | CMPSMC6Y | 20 |
| DATABASE MANIPULATION | CMPSMB1Y | 20 |
| STATISTICAL MODELLING | CMPSMC2Y | 20 |
Option C Study (40 credits)
Students will select 40 credits from the following modules:
| Name | Code | Credits |
|---|---|---|
| INFORMATION VISUALISATION | CMPSME1Y | 10 |
| MARKETING LEADERSHIP | CMPSMC9Y | 20 |
| METAHEURISTICS | CMPSMC1Y | 10 |
| PROJECT IN DATA MINING | CMPSMC8Y | 20 |
| PROJECT IN STATISTICS | CMPSMC4Y | 20 |
This is the second year of your taught Masters programme. The modules from ranges B and C shown in year one may be taken in EITHER year of study. Students will work on the dissertation in their final year.
Compulsory Study (60 credits)
Students must study the following modules for 60 credits:
| Name | Code | Credits |
|---|---|---|
| DISSERTATION | CMPSMP6X | 60 |
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:
- Computer Science 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.