All modern organisations are critically dependent of high quality information for making strategic decisions. Much of the data from which this information 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 KDD MSc programme addresses training needs in this area. Students on this programme undertake a mix of specialised units which give them a thorough knowledge of techniques and tools for knowledge discovery and data mining.

The course provides a comprehensive understanding of the role of data in modern business, its collection, storage, maintenance and access. Students take compulsory units in Research Techniques, Data Mining and Statistics. Students also take three optional units from a range which may include Artificial Intelligence, Databases, Systems Development or a research topic. Students will gain experience of working with commercial tools used to undertake data analysis.

Entry Requirements

Applicants should have a good honours degree in computing, mathematics or a related subject, or equivalent qualifications and experience. Students with little computing experience may be required to undertake a pre-sessional course.

Applicants whose first language is not English are required to have either IELTS 6.5, TOEFL 580 (paper-based), 230 (computer-based) or 92 (internet-based), or equivalent.

Course profile

Overall total: 180 credits

Compulsory modules: (140 credits)
 

Options range

Students will select 40 credits from the following modules: