FIT5196 - Data wrangling
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FIT5196 - Data wrangling
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.
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Offerings |
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S1-01-CLAYTON-FLEXIBLE Location: Clayton Teaching period: First semester Attendance mode: A combination of on-campus and online teaching (FLEXIBLE) |
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S2-01-CLAYTON-FLEXIBLE Location: Clayton Teaching period: Second semester Attendance mode: A combination of on-campus and online teaching (FLEXIBLE) |
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S2-01-MALAYSIA-ON-CAMPUS Location: Malaysia Teaching period: Second semester Attendance mode: Teaching activities are on-campus (ON-CAMPUS) |
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Requisites Prerequisite
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6 CP 
Programming foundations in python
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Rules
Enrolment Rule
Prerequisites:
For C6007 students who commenced in 2020: None
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Contacts |
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Chief Examiner(s) Dr Jackie Rong
Email: [email protected] Offering(s):
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Learning outcomes |
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On successful completion of this unit, you should be able to: |
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1. Parse data in the required format; |
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2. Assess the quality of data for problem identification; |
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3. Resolve data quality issues ready for the data analysis process; |
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4. Integrate data sources for data enrichment; |
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5. Document the wrangling process for professional reporting; |
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6. Write program scripts for data wrangling processes. |
Teaching approach
Active learning
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Assessment Assessment 1 Value %: 35 |
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Assessment 2 Value %: 35 |
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Assessment 3 Value %: 30 |
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Scheduled teaching activities Applied sessions Total hours: 24 hours Offerings:
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Lectures Total hours: 24 hours Offerings:
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Seminars Total hours: 24 hours Offerings:
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Workload requirements
Workload
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.
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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/
https://www.anaconda.com/distribution/
2023-11-02