FIT5147 Data exploration and visualisation
Faculty of Information Technology
FIT5147 Data exploration and visualisation is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2024 in Semester 1 and Semester 2 at Clayton and Malaysia. It has no prerequisites.
- Credit points
- 6
- Offered in 2024
- Semester 1, Semester 2
- Clayton, Malaysia
- Assessment
- No exam
- 6 tasks
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
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Requisites
Before FIT5147
Prohibitions
You can't enrol if you have passed any of these.
After FIT5147
No unit lists FIT5147 as a prerequisite in the 2024 handbook.
Enrolment rules
Prerequisite: Some of the material relies on a basic knowledge of statistics (mean, standard deviation, median) and a basic knowledge of geometry. A secondary/high-school level understanding of these concepts is sufficient.
Some knowledge of programming with R is recommended.
Equivalent units
The same content under another code. Only one of them counts.
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.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| First semester | Malaysia | On campus |
| Second semester | Clayton | Flexible |
Assessment
- Programming Exercise 1: Tableau PublicOther5%
- Programming Exercise 2: ROther5%
- Programming Exercise 3: D3Other5%
- Data Exploration ProjectProject35%
- Visualisation ProjectProject40%
- Online QuizOther10%
Learning outcomes
When you finish 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.
Workload and teaching
- Workshops24 hours
- Applied sessions24 hours
- Teaching approachActive learning
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.
Learning resources
Technology resources
Access to computer facilities allowing programming in R, Python and D3.
Monash Online: able to run the Respondus lockdown browser.
Where it fits
FIT5147 is part of 3 areas of study in the 2024 handbook.
Contacts
- Chief Examiners
- Dr Sarah Goodwin
- Unit Coordinators
- Dr Ting Chai Wen
- Dr Michael Niemann
Common questions
What are the prerequisites for FIT5147?
FIT5147 has no prerequisites, but enrolment rules apply.
When is FIT5147 offered?
In 2024, FIT5147 runs in Semester 1 and Semester 2 at Clayton and Malaysia.
How much work is FIT5147?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5147 have an exam?
No. FIT5147 has 6 assessment tasks and no exam.
Which majors and minors include FIT5147?
FIT5147 is part of Atmospheric science, Computational science and Earth science.