FIT5147 · Data exploration and visualisation

Overview

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.

Areas of study: Advanced data analytics 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. 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 and effective data visualisation;
6. implement static and interactive data visualisations using R and other tools.

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.