Data Warehousing and Mining

Unit code: NIT6160 | Study level: Postgraduate
12
(One credit point is usually equivalent to one hour of study per week)
City Campus
Footscray Park
Online Real Time
VU Sydney
NIT5150 - Advanced Object Oriented Programming
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

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 are introduced to perform data mining tasks with neural networks, as well as exposed to open-source data mining software.

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.

Assessment

For Melbourne campuses

Assessment type: Laboratory Work
|
Grade: 20%
Practical labs
Assessment type: Test
|
Grade: 40%
Open-book test
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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