- Student Records
Programme & Unit Catalogues

MN50645: Data mining

Follow this link for further information on academic years Academic Year: 2017/8
Further information on owning departmentsOwning Department/School: School of Management
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 40%, EX 60%
Further information on unit assessment Assessment Detail:
  • Coursework (CW 40%)
  • Exam (EX 60%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Further information on descriptions Description: Aims:
This unit covers contemporary statistical and algorithmic methods for cleaning, processing and extracting hidden information and knowledge out of raw data.

Learning Outcomes:
At the end of this unit, students will be able to:
* Choose appropriate algorithms to detect previously unknown rules and patterns within data and infer their business implications
* Measure the accuracy and precision of the rules and patterns detected
* Identify clusters within multi-dimensional data and classify the members of these classes and the outliers

Intellectual skills:
* Develop algorithmic thinking for rule extraction and exception detection (T, F, A)
* Enhance perspective of knowledge discovery (T, F, A)
Practical skills:
* Simplify and convert data for analysis (T, F, A)
* Use state-of-the-art data mining software (T, F)
Transferable skills:
* Improve assessment of the value of knowledge (F)

Topics covered include rule extraction, clustering methods, self-organizing maps, support vector machines, neural networks, and outlier detection.
Further information on programme availabilityProgramme availability:

MN50645 is Compulsory on the following programmes:

School of Management