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Unit of study_
QBUS3600: Business Analytics in Practice
This capstone unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills 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 a 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. Machine Learning is a fundamental aspect of business analytics that automates analytical modelling and decision making. Students ensure their career-readiness by demonstrating their ability to apply concepts, theories, methodologies, and programming skills to authentic problems and challenges faced in the field of business analytics.
| Code | QBUS3600 |
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| Academic unit | Business Analytics |
| Credit points | 6 |
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Prerequisites:
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Student commencing from 2018: completion of at least 120 credit points including QBUS2310 and QBUS2810 and QBUS2820. 2018 continuing students: completion of at least 120 credit points including QBUS2310 and QBUS2810 |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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All other requirements for the major or program associated with this capstone must be completed prior to or concurrently with (if enrolment rules permit) this unit of study. Capstones must be completed at the University of Sydney Business School only. |
At the completion of this unit, you should be able to:
- LO1. demonstrate a deep understanding of different types of learning algorithms and identify the advantages and limitations of each method
- LO2. build a strong machine learning skill set for business decision making
- LO3. create machine learning models for studying relationships amongst business variables
- LO4. work with various data sets and identify problems within real-world constraints
- LO5. demonstrate proficiency in the use of statistical software, e.g. Python, for machine learning models implementation
- LO6. work productively and collaboratively in a team
- LO7. present and write insights and suggestions effectively, professionally and ethically.
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney
- Semester 2, 2024 [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] - Camperdown/Darlington, Sydney
- Semester 2, 2021 [Normal day] - Remote
- Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney
- Semester 1, 2022 [Normal day] - Remote
- Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney
- Semester 2, 2022 [Normal day] - Remote
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