Units / ADS2001
ADS2001 · Data challenges 3
2026 Handbook6 credit pointsLevel 2Faculty of Science
Last checked: 23 Aug 2026 UTCOverview
This is the third in a series of Data Challenges units which builds on industry-relevant data science case studies explored during the first two semesters of the course. Team-based learning is used to further develop and apply the suite of analytical skills required to discover the underlying answers to questions raised by large and complex sets of data. A broad range of problems will be presented from both STEM and humanities disciplines, including both academic and industry focused examples, and guest lectures will give you an insight into real problems faced by experts in the field. You will continue to discuss the technical and ethical elements of data, with a focus on data collection methods, scientific thinking, and issues surrounding privacy and to communicate project outcomes through a combination of written, oral and multimedia visualisations. You will also work in teams to apply the key principles, tools and techniques of data science to complex industry problems, and develop their capacity to communicate their analysis and ethical considerations to a range of potential stakeholders.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| 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 | Assignment | Written | 80% | — |
| 2 | Reflective journal | Portfolio | 20% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
- ADS1002 — Data challenges 2
Learning outcomes
- Apply scientific thinking to data-oriented projects and tasks;
- Demonstrate the ability to work in a team to achieve a goal;
- Analyse the ethical issues associated with data science decisions;
- Demonstrate a variety of approaches used to communicate complex ideas to potential stakeholders through a variety of techniques including role-play;
- Perform a range tasks to analyse and visualise data;
- Design approaches for collecting and analysing data for a range of industry problems.
Workload
• Three hours of online learning to be completed pre-studio; • One three-hour studio and • Approximately six hours of project work and reflective practice per week
| Activity | Duration |
|---|---|
| Studio activities | 36 hours |
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