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Unit of study_
QBUS6810: Machine Learning for Business
2025 unit information
It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.
Unit details and rules
Managing faculty or University school:
Business (Business School)
| Study level | Postgraduate |
|---|---|
| Academic unit | Business Analytics |
| Credit points | 6 |
|
Prerequisites:
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(ECMT5001 or QBUS5001) and (a mark of 65 or greater in BUSS6002 or COMP5310) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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STAT5003 or COMP5318 |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Recognise how machine learning can help organisations optimise business processes and make effective decisions at scale.
- LO2. Formulate business decision problems as predictive machine learning problems.
- LO3. Select relevant machine learning algorithms and tools to solve a range of business prediction and data mining problems.
- LO4. Evaluate machine learning algorithms and techniques according to their statistical and computational properties.
- LO5. Extract business insights from large volumes of data using machine learning and data mining methods.
- LO6. Apply machine learning and data mining techniques using industry-standard computational tools.
- LO7. Collaborate effectively within data teams.
- LO8. Communicate data-driven results and insights effectively to a business audience.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2025
|
Normal day | Camperdown/Darlington, Sydney | |
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Semester 2 2025
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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