Data Warehousing and Mining

    Unit code: NIT6160 | Study level: Postgraduate
    (Generally, 1 credit = 10 hours of classes and independent study.)
    City Campus
    Footscray Park
    Online Real Time
    VU Brisbane
    VU Sydney
    NIT5150 - Advanced Object Oriented Programming
    (Or equivalent to be determined by unit coordinator)


    Data mining is the computational process of discovering patterns from large data sets. This unit discusses concepts and techniques of data warehousing and mining. Data mining is one of the most advanced tools used by IT industries. The topics covered include introduction to data warehousing, data pre-processing and foundational data mining techniques such as supervised learning including regression and classification, and unsupervised learning such as clustering and association rules. Students will perform data mining tasks with neural networks, as well as be exposed to open-source data mining software. In this process, students are to design solutions for data privacy, practice with professional ethics.

    Learning Outcomes

    On successful completion of this unit, students will be able to:

    1. Critically analyse the application of data warehousing concepts;
    2. Critically evaluate data quality to advocate application of data pre-processing techniques;
    3. Devise implementation solutions that involve supervised learning including regression and classification on real world data sets;
    4. Experiment and evaluate unsupervised learning solutions including clustering and association rules mining on large datasets; and
    5. Critically reflect on advantages and disadvantages of particular data mining solutions to solve real life problems with considerations of data privacy and professional ethics, and evaluate their usefulness and useability.


    For Melbourne campuses

    Assessment type: Laboratory Work
    Grade: 20%
    Practical labs
    Assessment type: Case Study
    Grade: 40%
    In-class Problem Solving Case Study for a scenario based real world problem
    Assessment type: Project
    Grade: 40%
    Final Project

    Required reading

    Required readings will be made available on VU Collaborate.

    As part of a course

    This unit is studied as part of the following course(s):

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