Knowledge Extraction Course

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Introduction

Most large companies have accumulated vast databases of material which represents one of their most valuable resources, but only provided they are adequately 'mined' for the knowledge they contain. The extraction of this knowledge requires a range of skills not generally included in any undergraduate degree programme.

Description

The MSc in Knowledge Extraction is a 12 month, full-time course for students from a range of technical backgrounds that wish to gain experience and expertise in extracting hidden knowledge from large databases.

Objectives

The course offers students an opportunity to acquire a basic understanding of database manipulation and data visualisation techniques, as well as developing skills in the extraction of knowledge from databases using state-of-the-art rule induction techniques and advanced statistical methods. The project element presents an opportunity for students to apply these techniques to a real-world application.

Course Profile

Over two semesters, students take a number of taught courses totaling 120 credits. A typical programme includes units on Research Methods, Scientific Visualisation, Statistics, Modern Heuristic Techniques, Data Mining, Database Manipulation, and one or two optional units. The third period is devoted entirely to a dissertation (60 credits) on a topic related to knowledge extraction.

Compulsory Units (120 credits)

• Data Mining (15 credits)
  (SYSCMC13 or SYSCMC14)
• Statistics or Advanced Statistics (20 credits)
• Modern Heuristic Techniques (15 credits)
  (SYSCMC11 or SYSCMC12)
• Scientific Visualisation (10 credits)
• Dissertation (60 credits)

Options Range A (45 credits)

• Units totalling a rating of 45 credits chosen from the M level units offered by the School.
• Full time students must take Research Methods (15 credits).
• Students with no database experience must take Database Manipulation (15 credits)

Free Choice (15 credits)

• Units totalling a rating of 45 credits chosen from the list of all SYS Units and others offered by the University.

Notes

• The combination of units allowed in Option Range A and Free Choice is restricted to those that make an academically coherent programme in Knowledge Extraction.
• Students perform assessed coursework exercises individually and in small groups. Examinations of taught courses are held at the end of each semester. The dissertation is an individual piece of work, often undertaken in co-operation with an outside body.
• There is also a part-time, modular version of this course for students working in industry and commerce who wish to have formal training in statistics and knowledge discovery.

Entry Requirements

A second class degree in Computer Science or a cognate subject, or equivalent qualifications and experience.

Additional Information

For further information on this course, either full-time or part-time, email Professor V J Rayward-Smith. Students taking this MSc will normally be associated with the School's Data Mining Research Group.