Faculty notice: this unit is changing
Final offering S2 2027
BCS - Data science and artificial intelligence, Data science BIT - Business information systems
Replace with FIT3229 from 2028 (S1)
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 / FIT3152
FIT3152 · Data analytics
2027 Handbook6 credit pointsLevel 3Faculty of Information Technology
Overview
There has been an explosion in the quantity and variety of data collected and routinely analysed by government, business and society at large over recent years. This has been described by some social commentators as the rise of "big data" and and the analysts and practitioners who investigate this data as "data scientists." This unit will introduce you to the analysis of big data and the role of the data scientist. Techniques covered include data management and transformation, visual analysis, social network analysis, statistical learning, clustering and natural language processing. You will be introduced to these methods using open source industry standard software. Data and case studies will be drawn from diverse sources. The general principles of analysis, investigation and reporting will be covered. You will be encouraged to critically reflect on the data analysis process within your own domain of interest.
Areas of study: Advanced computer science Business information systems Computational science Data science IT for business
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| Clayton | 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 1 | Project | 25% | — |
| 2 | Assignment 2 | Project | 20% | — |
| 3 | Assignment 3 | Project | 25% | — |
| 4 | Quiz and practical activity | Quiz / Test | 30% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
OR
ETC1000Business and economic statistics6 cpOR
ETF1100Business statistics6 cpOR
FIT2086Modelling for data analysis6 cpOR
ETC1010Introduction to data analysis6 cpOR
ETW1000Business and economic statistics6 cpOR
ETW1010Data modelling and computing6 cpOR
ETW2111Business data modelling6 cpOR
STA1010Statistical methods for science6 cpAND
prohibition
Learning outcomes
- Demonstrate the ability to transform real world problems into ones that can then be solved using data analytics techniques;
- Cleanse and prepare data for analysis;
- Analyse large data sets using a range of statistical, graphical and machine-learning techniques;
- Validate and critically assess the results of analysis;
- Interpret the results of analysis and communicate these to a broad audience.
Workload
Applied sessions are scheduled from week 2 to week 12. 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 |
|---|---|
| Applied sessions | 22 hours |
| Seminars | 24 hours |
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