ADS2002 · Monash University
Data challenges 4
- Credit points
- 6
- Level
- 2
- Workload
- —
- Faculty
- Science
What it covers
This is the fourth in a series of Data Challenges units which builds on industry-relevant data science case studies explored during the first three 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 your capacity to communicate your analysis and ethical considerations to a range of potential stakeholders.
Assessment
| Task | Type | Weight |
|---|---|---|
| Assignments | Written | 80% |
| Reflective journal | Portfolio | 20% |
When and where
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On-campus |
| Malaysia | On-campus |