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Knowledge Discovery and Datamining (Part time 3 Yr), MSc, University of East Anglia (UEA)

the United Kingdom

University of East Anglia (UEA)

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About Knowledge Discovery and Datamining (Part time 3 Yr), MSc - at University of East Anglia (UEA)

Study

The world requires a new generation of analysts. All organisations depend on high quality information when making their strategic decisions, and most of that information comes from mountains of raw data generated from the organisation's computerised operational systems. This MSc programme addresses the training needed in this area by professionally preparing you to analyse important data and spot business critical patterns and trends.

Structure

Through this course, you will understand techniques and methods required for effective and efficient data analysis, through a comprehensive study of the role of data in modern business, its collection, storage, maintenance and access. You will undertake a mix of specialised modules that supply you with the techniques and commercial tools needed for knowledge discovery and data mining. Choose from a number of faculty-suggested dissertation topics or formulate your own project proposal. Some project work with companies could involve paid placement.

Teaching

The School of Computing Science at UEA maintains close ties with industry professionals, with many of the student assignments directly seeking solutions to real world problems. The School utilises its internationally recognised research commercially, and graduates of this course often use taught technical and entrepreneurial skills to fulfil influential roles within the local and international community.

Employability

Graduates of this course will be prepared for a career in data analysis. This area of study also offers tremendous application potential in areas such as finance, medicine, biology and the environment. There is also the opportunity to pursue a research degree in Knowledge Discovery and Data Mining.

Entry requirements

Open to students with a good first degree (min 2.1 or equivalent) in Computing, Mathematics or a related subject. Equivalent qualifications and experience will also be considered. Students with limited computing experience may be required to undertake a pre-sessional course.

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