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This unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills in ML developed across the Business Analytics major. The problem-based approach to learning in this unit offers vital tools and techniques for business decision makers in the big data era through the use of very large and rich data sources. The unit casts the knowledge of statistical learning in modern machine learning context and exposes business students to a range of state-of-the-art machine learning topics with the emphasis on applications involving the analysis of business data.
Study level | Undergraduate |
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Academic unit | Business Analytics |
Credit points | 6 |
Prerequisites:
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Students must meet the entry requirements for the Bachelor of Advanced Studies (Advanced Coursework), including completion of a pass undergraduate degree and a relevant major including (QBUS3600 or ECMT3185) |
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Corequisites:
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None |
Prohibitions:
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None |
Assumed knowledge:
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Students are assumed to be familiar with Statistical Modelling, Optimisation and Machine Learning |
At the completion of this unit, you should be able to:
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 2 2024
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Normal day | Camperdown/Darlington, Sydney |
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Session | MoA ? | Location | Outline ? |
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Find your current year census dates
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.