FIT5145 · Monash University
Foundations of data science
- Credit points
- 6
- Level
- 5
- Workload
- ≈ 12 h / week
- Faculty
- Information Technology
What it covers
This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.
Assessment
| Task | Type | Weight |
|---|---|---|
| Data Analysis with R (Coding task I) | Artefact | 15% |
| Report on the Data Science Project | Written | 30% |
| Propose a Data Science Project | Written | 15% |
| Data Analysis with Tools and Scripting (Coding task II) | Artefact | 40% |
When and where
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| Malaysia | On-campus | |
| Second semester | Clayton | Flexible |
| Malaysia | On-campus |