FIT5196 - Data wrangling - 2019

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.

At the completion of this unit, students 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.

In-semester assessment: 100%

Minimum total expected workload equals 144 hours per semester comprising:

1. Contact hours for on-campus students:
   • Two hours/week lectures
   • Two hours/week tutorials
2. Contact hours for Monash Online students:
   • Two hours/week online group sessions
   • Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.
3. Additional requirements (all students):
   • A minimum of 8 hours per week of personal study (22 hours per week for Monash Online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

See also Unit timetable information