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Unit of study_
QBUS6810: Statistical Learning and Data Mining
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.
| Code | QBUS6810 |
|---|---|
| Academic unit | Business Analytics |
| Credit points | 6 |
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Prerequisites:
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(ECMT5001 or QBUS5001 or STAT5003) and (a mark of 65 or greater in BUSS6002 or COMP5310 or COMP5318) |
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Corequisites:
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None |
| Prohibitions:
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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 outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 1, 2023 [Normal day] - Remote
- Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney
- Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney
- Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2021 [Normal day] - Remote
- Semester 2, 2021 [Normal day] - Remote
- Semester 2, 2021 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2022 [Normal day] - Remote
- Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney
- Semester 2, 2022 [Normal day] - Remote
- Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney
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