Overview

This unit introduces students to the definition and scope of modern data mining. Students will learn how to manage large data sets and to appropriately select and transform variables. The unit builds understanding of the importance of appropriate data mining procedures in analysing “big data”, also developing student capability in conducting appropriate data mining approaches to investigate relationships in large data sets and in critically assessing reports derived from such data.

Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 1
Location
Hawthorn
Start and end dates
26-February-2024
26-May-2024
Last self-enrolment date
10-March-2024
Census date
31-March-2024
Last withdraw without fail date
12-April-2024
Results released date
02-July-2024

Learning outcomes

Students who successfully complete this unit will be able to:

  • Appropriately apply common data mining terms.
  • Critically read and evaluate data mining reports that appear in media and other publications
  • Understand, identify and describe the essential elements of unsupervised and supervised learning
  • Select, transform and visualize the variables and relationships to be used for particular data mining purposes
  • Choose and successfully apply query tools and exploratory, predictive, classification and segmentation data mining procedures in a variety of areas
  • Interpret outputs from appropriate data mining software to report on copious amounts of data

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Online
Lecture		2.00 12 weeks 24
On-campus
Class		2.00 12 weeks 24
Specified Activities
Various		2.00 12 weeks 24
Unspecified Activities
Various		6.50 12 weeks 78
TOTAL150

Assessment

Type Task Weighting ULO's
AssignmentIndividual 50% 1,2,3,4,5,6 
Online QuizIndividual 10% 1,6 
TestIndividual 40% 1,3,6 

Content

  • Introduction to Data Mining and Data Warehousing
  • Introduction to Rattle
  • Exploratory Data Analysis
  • Data Transformation
  • Association Analysis
  • Regression for Classification and Prediction
  • Trees for Classification and Prediction
  • Random Forests and Boosting
  • Neural Networks for Classification and Prediction
  • Support Vector Machines

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.