Units / ITI5145
ITI5145 · Introduction to data science
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
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.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Indonesia | Monash Indonesia term 4 | Activities scheduled as a mix of on-campus and online activities (BLENDED) |
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 | Data Analysis with R (Coding task I) | Artefact | 15% | — |
| 2 | Propose a Data Science Project | Written | 15% | — |
| 3 | Data Analysis with Tools and Scripting (Coding task II) | Artefact | 40% | — |
| 4 | Business and data case study: Report and presentation | Written | 30% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prohibitions
prerequisite
- ITI9136 — Algorithms and programming foundations in Python
Learning outcomes
- Analyse the role of data in organisations, including curation and management issues;
- Apply basic tools for performing exploratory data analysis and visualisation;
- Apply basic tools for managing and processing big data;
- Apply basic predictive modeling and data analysis methods;
- Determine data storage and processing requirements for a data science project;
- Identify data resources and recognised standards of data science.
Workload
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period 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 activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
| Activity | Duration |
|---|---|
| Laboratories | 24 hours |
| Lectures | 24 hours |
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