Subject details

Students who successfully complete this subject will be able to:

  1. Formulate an understanding of the basic theory and principles of data mining
  2. Assess prediction and classification methods such as regression, neural networks and decision trees, understanding how to choose between these methods
  3. Evaluate unsupervised data mining methods and determine when these methods should be combined with supervised methods
  4. Design ontologies based on text and creative visualisations of textual relationships
  5. Report on the results of specific data mining projects.
    • Introduction to data mining and a data mining package
    • Linear Models
    • Classification and regression trees
    • Random forests and boosting
    • Neural networks for classification and prediction
    • Self-organising maps
    • Cluster analysis
    • Memory Based Reasoning
    • Support Vector Machines
    • Text Mining
  • Study resources

    • Instructional methods

      • Discussion forum/Discussion Board
      • Online Quizzes/Tests
      • Online assignment submission
      • Podcasting/Lecture capture
      • Standard Media
      • Web links
    • Print materials

      • Welcome letter
    • Online materials

      • Printable format materials

Equivalent subjects

You cannot enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:

  • SWI-HMS794

You must have successfully completed the following subject(s) before starting this subject:

SWI-STA70002-Multivariate Statistics , or SWI-HMS780

Special requirements

  • OtherDetails -

    You will require access to a recent version of SPSS and the latest version of SAS Enterprise Miner. Note: SAS will only run on a Windows platform.

Please note: this subject was previously titled Statistical Marketing Tools

This subject introduces the principles and techniques used by data miners. Data mining is used to add value to large collections of data, delivering discoveries that continue to revolutionise lives in our data-rich but knowledge-hungry world.

Please note: assessment values are indicative only, details will be advised at the start of the subject.

  • Invigilated Exam (50%)
  • Quizzes — Online (10%)
  • Assignments — 2 (40%)

Textbook information is pending.

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