FIT5196 Data wrangling
Faculty of Information Technology
FIT5196 Data wrangling is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2024 in Semester 1 and Semester 2 at Clayton and Malaysia. It needs FIT9133 or FIT9136 and unlocks 1 unit.
- Credit points
- 6
- Offered in 2024
- Semester 1, Semester 2
- Clayton, Malaysia
- Assessment
- No exam
- 8 tasks
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT5196
Prerequisites
Pass these before you enrol.
Prohibitions
You can't enrol if you have passed any of these.
After FIT5196
1 unit list FIT5196 as a prerequisite or corequisite.
Enrolment rules
Prerequisites:
For C6007 students who commenced in 2020: None
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit introduces tools and techniques for data wrangling. It will cover the problems that prevent raw data from being effectively used in analysis and the data cleansing and pre-processing tasks that prepare it for analytics. These include, for example, the handling of bad and missing data, data integration and initial feature selection. It will also introduce text mining and web analytics. Python and the Pandas environment will be used for implementation.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| Second semester | Clayton | Flexible |
| Second semester | Malaysia | On campus |
Assessment
- Assessment 1Assignment35%
- Assessment 2Assignment35%
- Assessment 3Other30%
- Quiz 1Other10%
- Quiz 2Other10%
- Group assignment 1Assignment35%
- Group assignment 2Assignment40%
- Learning Activity ParticipationOther5%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Parse data in the required format;
- 2
Assess the quality of data for problem identification;
- 3
Resolve data quality issues ready for the data analysis process;
- 4
Integrate data sources for data enrichment;
- 5
Document the wrangling process for professional reporting;
- 6
Write program scripts for data wrangling processes.
Workload and teaching
- Applied sessions24 hours
- Seminars24 hours
- Lectures24 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 teaching activities.
Learning resources
Technology resources
The Programming environments: Python 3 has been added. Python 3 - https://www.python.org/downloads/ and Jupyter Notebook - https://jupyter.org/
Where it fits
FIT5196 is part of 1 area of study in the 2024 handbook.
Contacts
- Chief Examiners
- Dr Jackie Rong
- Unit Coordinators
- Dr Sailaja Rajanala
Common questions
What are the prerequisites for FIT5196?
You need FIT9133 or FIT9136 before you enrol. Enrolment rules also apply.
What can I take after FIT5196?
FIT5196 is a prerequisite or corequisite for 1 unit, including EPM5032.
When is FIT5196 offered?
In 2024, FIT5196 runs in Semester 1 and Semester 2 at Clayton and Malaysia.
How much work is FIT5196?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5196 have an exam?
No. FIT5196 has 8 assessment tasks and no exam.
Which majors and minors include FIT5196?
FIT5196 is part of Computational science.