ETF5932 Predictive analytics and machine learning
Faculty of Business and Economics
ETF5932 Predictive analytics and machine learning is a level 5, 6-credit-point, postgraduate unit from the Faculty of Business and Economics, offered in 2025 in Semester 1 at Caulfield. It needs FIT5197, ETW2001, ETC2420, ETC5510, ETC5242, ETX2250, ETC1010 or ETF5922 and unlocks 1 unit.
- Credit points
- 6
- Offered in 2025
- Semester 1
- Caulfield
- Assessment
- Exam 50%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2025 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before ETF5932
Prerequisites
Pass these before you enrol.
- FIT5197Statistical data modellingNo reviews yet
- ETW2001Foundations of data analysis and modelling
- ETC2420Statistical thinkingNo reviews yet
- ETC5510Introduction to data analysisNo reviews yet
- ETC5242Statistical thinkingNo reviews yet
- ETX2250Data visualisation and analyticsNo reviews yet
- ETC1010Introduction to data analysisNo reviews yet
- ETF5922Data visualisation and analyticsNo reviews yet
Prohibitions
You can't enrol if you have passed any of these.
After ETF5932
1 unit list ETF5932 as a prerequisite or corequisite.
Equivalent units
The same content under another code. Only one of them counts.
Overview
Many problems in business including sales and inventory forecasting, credit scoring, recommender systems in online commerce and fraud detection use advanced tools for data analytics. This unit covers some of the most popular tools that may include tree-based methods, boosting, bagging, support vector machines, neural networks and deep learning. The algorithmic details of each method, their implementation using popular software tools (such as R) and their application to real business problems will all be covered.
Offerings in 2025
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | Blended |
Assessment
- Within semester assessment50%
- Examination50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
understand different techniques used in business analytics and to be able to compare these from a statistical and computational point of view
- 2
frame problems in finance, marketing, economics and related areas so that they can be solved by modern tools in business analytics
- 3
implement machine learning methods in a modern software environment (for example, R) with potentially large datasets
- 4
explain and interpret the analyses undertaken in a clear and effective manner and be aware of the limitations of these analyses.
Workload and teaching
- Workshops12 hours
- Seminars24 hours
- Tutorials12 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Where it fits
ETF5932 is part of 1 area of study in the 2025 handbook.
Contacts
- Chief Examiners
- Dr Ruben Loaiza Maya
Common questions
What are the prerequisites for ETF5932?
You need FIT5197, ETW2001, ETC2420, ETC5510, ETC5242, ETX2250, ETC1010 or ETF5922 before you enrol.
What can I take after ETF5932?
ETF5932 is a prerequisite or corequisite for 1 unit, including ETC5555.
When is ETF5932 offered?
In 2025, ETF5932 runs in Semester 1 at Caulfield.
How much work is ETF5932?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETF5932 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.
Which majors and minors include ETF5932?
ETF5932 is part of Data analytics for business.