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ID5059 Knowledge Discovery and Datamining

SCOTCAT credits:15
Academic year(s):2017/8
SCQF Level 11
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).

Place in programme(s) and relationship to other modules


Optional for Computer Science BSc, Joint Computer Science degrees, Computer Science MSci


Compulsory for Applied Statistics and Datamining Postgraduate Programme. Compulsory for Data-Intensive Analysis MSc Programme.

Optional for all Postgraduate Programmes.

PG Anti-requisite(s):MT5759

Learning and teaching methods and delivery

Weekly contact:Lectures, seminars, tutorials and practical classes.
Total module hours:
  • Scheduled learning: 35
  • Guided independent study: 115

Assessment pattern

UG As defined by QAA:
  • Written examinations: 60%
  • Practical examinations: 0%
  • Coursework: 40%
UG As used by St Andrews:2-hour Written Examination = 60%, Coursework = 40%
UG Re-assessment:2-hour Written Examination = 60%, Existing Coursework = 40%
Postgraduate Assessment :2-hour Written Examination = 60%, Coursework = 40%


Module teaching staff: