Knowledge Discovery and Data Mining (MSc)
Why take this course?
The course has both theoretical and practical elements and students will get hands on experience on commercial data mining and statistical software. Students will also have the opportunity to participate on commercial data mining projects as part of their assessment, gaining experience on all the stages of the KDD process.
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 the 2010-2012 are between £44,000 and £52,000 . 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 at 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.
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 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. Assessment will be conducted using a variety of formats including essays, project reports, presentations, and examinations.
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
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).