ITO5196 · 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.

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. TP03 - Document the wrangling process for professional reporting;TP06 - Communicate data wrangling processes, decisions, and outcomes effectively in written and oral forms.
6. Write program scripts for data wrangling processes.

Workload

A minimum of 122 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

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