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.

Areas of study: Data science

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

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. Communicate data wrangling processes, decisions, and outcomes effectively in written and oral forms.
6. Write program scripts for data wrangling processes.

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.

Student experience, not official rules. Nothing here changes what the Handbook says.