This unit is a duplicate unit of FIT5147. The ITIxxxx units have been created for the Monash Indonesia offering of the Master of Data Science due to the different teaching mode.

On successful completion of this unit, you should be able to:

1. perform exploratory data analysis using a range of visualisation tools;
2. describe the role of data visualisation in data science and its limitations;
3. critically evaluate and interpret a data visualisation;
4. distinguish standard visualisations for qualitative, quantitative, temporal and spatial data;
5. choose an appropriate data visualisation;
6. implement static and interactive data visualisations using R and other tools.

029999

This unit introduces statistical and visualisation techniques for the exploratory analysis of data. It will cover the role of data visualisation in data science and its limitations. Visualisation of qualitative, quantitative, temporal and spatial data will be presented. What makes an effective data visualisation, interactive data visualisation, and creating data visualisations with R and other tools will also be presented.

On-campus

In-semester assessment: 100%

Minimum total expected workload equals 144 hours per semester comprising:

1. Contact hours for on-campus students:
   • 1.5 hour/week workshop.
   • Two hours/week laboratories.
2. Additional requirements:
   • A minimum of 9 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

FIT

Some of the material relies on a basic knowledge of statistics (mean, standard deviation, median, linear regression, correlation) and a basic knowledge of geometry. A secondary / high-school level understanding of these concepts is sufficient.

Some experience in programming with R is required.