Overview

This unit enables students to understand the nature and challenges of Big Data especially those issues related to computation, security and privacy., and to Students will be expected to evaluate and apply solutions to address complex problems of storing, integrating and analysing heterogeneous data sets with varying degrees of structure from diverse sources and considerations such as privacy and security while using the appropriate tools taking into considerations of security and privacy in order to extract meaningful information for decisions.

Requisites

Prerequisites

COS20015 Fundamentals of Data Management

OR
INF10002 Database Analysis and Design
OR
INF10025 Data Management and Analytics
AND
COS10009 Introduction to Programming
OR
Entry to MA-ITPC1
OR
COS60009 Data Management for the Big Data Age
OR
INF60009 Database, Analysis and Design
AND
COS60006 Introduction to Programming
OR
TNE60003 Introduction to Network Programming
OR
COS60010 Technology Inquiry Project

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 2
Location
Hawthorn
Start and end dates
04-August-2025
02-November-2025
Last self-enrolment date
17-August-2025
Census date
31-August-2025
Last withdraw without fail date
19-September-2025
Results released date
09-December-2025

Learning outcomes

Students who successfully complete this unit will be able to:

  • Discuss the challenges and opportunities of Big Data including but not limited to computation, privacy and security in the context of its potential for organisations
  • Propose efficient solutions to address complex problems for the storage and management of large data sets considering issues such as privacy and security of the data stored
  • Use efficient tools to query diverse forms of data storage, such as NoSQL data sources
  • Compare and apply technologies and algorithms to extract and integrate information from data sets with varying degrees of structure taking into consideration such as privacy and security
  • Appraise the quality and fitness for purpose of the information extracted, using suitable statistical methods

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Live Online
Lecture
1.00 12 weeks 12
On-campus
Class
2.00 12 weeks 24
Unspecified Activities
Independent Learning
9.50 12 weeks 114
TOTAL150

Assessment

Type Task Weighting ULO's
Portfolio Individual 40 - 60%  1,2,3,4,5 
Project Individual 40 - 60% 1,2,3,4,5

Content

  • Big Data, its nature, challenges and opportunities including but not limited to computation, privacy and security
  • Storage and management technologies as well as challenges such as privacy and security
  • Query tools
  • Tools and techniques for extraction of information from structured and unstructured data
  • Integration of data/information from diverse sources considering the security and privacy of data
  • Statistical methods for information extraction and quality evaluation

Study resources

Reading materials

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