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ID5059   Knowledge Discovery and Datamining

Academic year(s): 2018-2019

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: 2

Availability restrictions: Not automatically available to General Degree students

Planned timetable: 11.00 am Mon (odd weeks), Wed and Fri

Contemporary data collection can be automated and on a massive scale e.g. credit card transaction databases. Large databases potentially carry a wealth of important information that could inform business strategy, identify criminal activities, characterise network faults etc. These large scale problems may preclude the standard carefully constructed statistical models, necessitating highly automated approaches. This module covers many of the methods found under the banner of Datamining, building from a theoretical perspective but ultimately teaching practical application. Topics covered include: historical/philosophical perspectives, model selection algorithms and optimality measures, tree methods, bagging and boosting, neural nets, and classification in general. Practical applications build sought-after skills in programming (typically R, SAS or python).

Learning and teaching methods and delivery

Weekly contact: Lectures, seminars, tutorials and practical classes.

Scheduled learning hours: 35

Guided independent study hours: 115

Assessment pattern

As used by St Andrews: 2-hour Written Examination = 60%, Coursework = 40%

As defined by QAA
Written examinations : 60%
Practical examinations : 0%
Coursework: 40%

Re-assessment: 2-hour Written Examination = 60%, Existing Coursework = 40%


Module teaching staff: Dr T Kelsey, Dr R Hoffmann