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MN50645: Data mining

Academic Year: 2018/9
Owning Department/School: School of Management
Credits: 6      [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CW 40%, EX 60%
Assessment Detail:
  • Coursework (CW 40%)
  • Exam (EX 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
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

Skills:
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)

Content:
Topics covered include rule extraction, clustering methods, self-organizing maps, support vector machines, neural networks, and outlier detection.
Programme availability:

MN50645 is Compulsory on the following programmes:

School of Management

Notes:

  • This unit catalogue is applicable for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this unit will be available in future years in the format displayed here for 2018/19.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Undergraduates: .
  • Postgraduates: .