ITO5202 Data processing for big data
Faculty of Information Technology
ITO5202 Data processing for big data is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2026. It needs ITO4132 and (ITO4131 or ITO4133).
- Credit points
- 6
- Offered in 2026
- Other periods
- Monash Online
- Assessment
- No exam
- 6 tasks
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before ITO5202
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After ITO5202
No unit lists ITO5202 as a prerequisite in the 2026 handbook.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit focuses on big data processing, including volume, complexity, and velocity using the latest big data technologies. In big data volume, it covers large volume data processing using parallel technologies. In large dimensionality (or complexity), it covers various data analytics methods for parallel processing. For the velocity, it covers data streaming processing.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Teaching period 1 | Monash Online | Mo |
| Teaching period 5 | Monash Online | Mo |
Assessment
- Assignment 1: Large-Scale Data ProcessingProject40%
- Assignment 2: Machine Learning and Streaming AnalyticsProject50%
- Final QuizQuiz / Test10%
- Fortnightly quizQuiz / Test30%
- Group AssignmentProject55%
- End of term quizQuiz / Test15%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
identify and explain big data concepts and technologies;
- 2
write and interpret parallel database processing algorithms and methods;
- 3
apply common data analytics and machine learning algorithms in a big data environment;
- 4
use and evaluate streaming methods in big data processing;
- 5
use big data streaming technologies.
Workload and teaching
- Workshops12 hours
- Teaching approachOnline learning
A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.
Learning resources
Recommended resources
- A. Kozlov: Mastering Scala machine learning : advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop, Birmingham, UK : Packt Publishing, 2016
- A. Alexander: Scala cookbook, Beijing : O'Reilly, 2013
- A.S. Tanenbaum, T. Austin: Structured Computer Organization, 6th Ed, Boston : Pearson, 2013
Contacts
- Chief Examiners
- Associate Professor David Taniar