Overview

This unit introduces students to the architectures of contemporary Big Data platforms and ecosystems of tools. These technologies are the foundation of Big Data analytics which facilitate scalable management and processing of vast quantities of data. The unit is delivered in collaboration with industry and prepares students with the essential foundations towards undertaking professional certifications.

Requisites

Prerequisites
COS10022 Data Science Principles

AND EITHER
COS20007 Object Oriented Programming
OR
COS30016 Programming in Java *

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Teaching Period 1
Location
Online
Start and end dates
09-March-2026
07-June-2026
Last self-enrolment date
22-March-2026
Census date
07-April-2026
Last withdraw without fail date
28-April-2026
Results released date
30-June-2026
Semester 2
Location
Hawthorn
Start and end dates
03-August-2026
01-November-2026
Last self-enrolment date
16-August-2026
Census date
01-September-2026
Last withdraw without fail date
22-September-2026
Results released date
08-December-2026
Teaching Period 3
Location
Online
Start and end dates
02-November-2026
07-February-2027
Last self-enrolment date
15-November-2026
Census date
01-December-2026
Last withdraw without fail date
22-December-2026
Results released date
02-March-2027

Unit learning outcomes

Students who successfully complete this unit will be able to:

  1. Explain and compare the architecture of contemporary Big Data tools and platforms
  2. Demonstrate skills in working with a contemporary Big Data platform to manage large data sets
  3. Apply Big Data related technologies in developing applications to solve common problems faced by organisations

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Lecture		2.00 12 weeks 24
On-campus
Class		2.00 12 weeks 24
Unspecified Learning Activities (Phasing out)
Independent Learning		8.00 12 weeks 96
TOTAL144

Swinburne Online

Type Hours per week Number of weeks Total (number of hours)
Online
Directed Online Learning and Independent Learning		12.50 12 weeks 150
TOTAL150

Assessment

Type Task Weighting ULO's
PortfolioIndividual 100% 1,2,3 

Content

  • Introduction to Big Data platforms and their ecosystems
  • Big Data platforms programming
  • Partitioners and Reducers
  • Big Data acquisition and workflows
  • The Data Analytics Lifecycle
  • Analysis and Management of structured and unstructured Big Data

Study resources

Reading materials

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