Units / FIT5147
FIT5147 · Data exploration and visualisation
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
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
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Clayton | Second semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Design Activity | Artefact | 15% | — |
| 2 | Data Exploration Project | Project | 35% | — |
| 3 | Visualisation Project | Project | 40% | — |
| 4 | Online Quiz | Quiz / Test | 10% | — |
| 5 | Programming Exercise 1: Tableau Public | Artefact | 5% | — |
| 6 | Programming Exercise 2: R | Artefact | 5% | — |
| 7 | Programming Exercise 3: D3 | Artefact | 5% | — |
| 8 | Data Exploration Project | Project | 35% | — |
| 9 | Visualisation Project | Project | 40% | — |
| 10 | Online Quiz | Quiz / Test | 10% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
Learning outcomes
- Perform exploratory data analysis using a range of visualisation tools;
- Describe the role of data visualisation in data science and its limitations;
- Critically evaluate and interpret a data visualisation;
- Distinguish standard visualisations for qualitative, quantitative, temporal and spatial data;
- Choose an appropriate and effective data visualisation;
- 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.
| Activity | Duration |
|---|---|
| Workshops | 24 hours |
| Applied sessions | 24 hours |
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