Faculty notice: this unit is changing
No longer offered
BCS - Data science
Replace with FIT2132 (S2)
From: Re-enrolment and unit changes - Information Technology (undergraduate) · Last checked: 7 Oct 2026 UTC
Official wording from the faculty, shown as published. It is the page of the Faculty of IT on monash.edu, so confirm with your faculty that it applies to your campus and intake year. Replacements are decided by the faculty, not by this site.
Units / FIT1043
FIT1043 · Introduction to data science
2027 Handbook6 credit pointsLevel 1Faculty of Information Technology
Overview
This unit looks at processes and case studies 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. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.
Areas of study: Data science
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | In-Class examination | Quiz / Test | 10% | — |
| 2 | Data science assignment 1 | Exercise | 20% | — |
| 3 | Data science assignment 2 | Exercise | 20% | — |
| 4 | Semester 1: Scheduled final assessment (2 hours and 10 minutes) | Examination | 50% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prohibition
prerequisite
Prerequisite: MTH1010 or VCE Mathematics Methods or Specialist Mathematics units 3 & 4 with a study score of 25 or equivalent.
MTH1010Learning outcomes
- Detail the phases of the data science lifecycle and differentiate the roles involved in a data science project.
- Implement strategies for acquiring, cleaning, and organising data prior to analysis.
- Utilise basic data analysis models to extract insights and critique their effectiveness.
- Understand fundamental properties of Big Data and their influence on storage and processing and evaluate the strengths and weaknesses of Big Data tools for specific contexts.
- Examine data science projects by identifying and discussing inherent ethical, privacy, and data management issues, including their broader impacts.
- Apply commonly used data science software and programming languages to interpret results across a diverse range of scenarios.
Workload
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.
| Activity | Duration |
|---|---|
| Lectures | 24 hours |
| Applied sessions | 22 hours |
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