FIT5202 Data processing for big data
Faculty of Information Technology
FIT5202 Data processing for big data is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2021 in Semester 1, Semester 2 and Summer B at Malaysia, Clayton and Monash Online. It needs FIT9132 and (FIT9136, FIT9131 or FIT9133).
- Credit points
- 6
- Offered in 2021
- Semester 1, Semester 2, Summer B
- Malaysia, Clayton, Monash Online
- Assessment
- Exam 60%
- and 7 other tasks
- Workload
- 144 hours
- per semester
This is the 2021 handbook entry. See the 2027 entry.
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Requisites
Before FIT5202
After FIT5202
No unit lists FIT5202 as a prerequisite in the 2021 handbook.
Enrolment rules
Prerequisite: A working knowledge of Python.
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 2021
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Malaysia | Evening |
| Second semester | Clayton | On campus |
| Summer semester B | Clayton | On campus |
| Teaching period 3 | Monash Online | Mo |
Assessment
- Assignment 1AssignmentThreshold hurdle10%
- Assignment 2AssignmentThreshold hurdle20%
- FLUX participationParticipationThreshold hurdle5%
- Lab tasksOtherThreshold hurdle5%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle60%
- Assessment 1Other10%
- Assessment 2Project50%
- Assessment 3Other40%
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
- Lectures24 hours
- Laboratories24 hours
- Workshops12 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
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
Where it fits
FIT5202 is part of 1 area of study in the 2021 handbook.
Contacts
- Chief Examiners
- Associate Professor David Taniar
- Dr Vishnu Monn
- Unit Coordinators
- Associate Professor Ting Chee Ming
Common questions
What are the prerequisites for FIT5202?
You need FIT9132 and (FIT9136, FIT9131 or FIT9133) before you enrol. Enrolment rules also apply.
When is FIT5202 offered?
In 2021, FIT5202 runs in Semester 1, Semester 2 and Summer B at Malaysia, Clayton and Monash Online.
How much work is FIT5202?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5202 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 7 other tasks.
Which majors and minors include FIT5202?
FIT5202 is part of Software engineering.