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
11-March-2024
09-June-2024
Last self-enrolment date
24-March-2024
Census date
05-April-2024
Last withdraw without fail date
26-April-2024
Results released date
02-July-2024
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024
Teaching Period 3
Location
Online
Start and end dates
04-November-2024
09-February-2025
Last self-enrolment date
17-November-2024
Census date
29-November-2024
Last withdraw without fail date
27-December-2024
Results released date
04-March-2025

Learning outcomes

Students who successfully complete this unit will be able to:

  • Explain and compare the architecture of contemporary Big Data tools and platforms
  • Demonstrate skills in working with a contemporary Big Data platform to manage large data sets
  • 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.