Unit - The 爆料王

QBUS2310: Decision Analytics

Units QBUS2310

Unit of study_

QBUS2310: Decision Analytics

2027 unit information

The ability to formulate decision problems clearly and rigorously is a core capability for managers in any organisation. This unit develops prescriptive analytics skills through convex optimisation, a widely used class of decision models that enables reliable and efficient decision making. Students learn to translate problems in finance, marketing, operations, and supply chain into mathematical models by defining decision variables, objectives, and constraints, and by recognising when a model is convex so that its solutions are dependable. Core ideas such as convex sets and functions, optimality conditions, and duality provide a framework for justifying decisions and interpreting trade-offs. The unit also introduces how modern optimisation algorithms work, with emphasis on gradient-based and scalable methods used in data-driven applications, and how to select appropriate tools for problems of different sizes and structures. Using Python and contemporary optimisation software, students build and solve models, compare solution approaches, and communicate recommendations with clear assumptions, limitations, and managerial insight.

Unit details and rules

Managing faculty or University school:

Business (Business School)

Details

Study level	Undergraduate
Academic unit	Business Analytics
Credit points	6

Enrolment rules

Prerequisites: ?	(BUSS1104 or BUSS1020 or ECMT1010 or ENVX1001 or ENVX1002 or DATA1001 or DATA1901) and (QBUS1040 or MATH1061 or MATH1062)
Corequisites: ?	None
Prohibitions: ?	ECMT2620
Assumed knowledge: ?	None

Learning outcomes

At the completion of this unit, you should be able to:

LO1. select between basic models of management science according to the business context
LO2. create models for management decision making
LO3. identify potential limitations of the models and suggest solutions to overcome model weaknesses
LO4. make decisions based on the output of the management science model
LO5. use special software tools for solving and analysing management science models.

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 ?
Semester 1 2026	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2026	Normal day	Camperdown/Darlington, Sydney	View

Session	MoA ?	Location	Outline ?
Semester 1 2027	Normal day	Camperdown/Darlington, Sydney	Outline unavailable
Semester 2 2027	Normal day	Camperdown/Darlington, Sydney	Outline unavailable

Session	MoA ?	Location	Outline ?
Semester 1 2020	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2020	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2021	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2021	Normal day	Remote	View
Semester 2 2021	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2021	Normal day	Remote	View
Semester 1 2022	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2022	Normal day	Remote	View
Semester 2 2022	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2022	Normal day	Remote	View
Semester 1 2023	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2023	Normal day	Remote	View
Semester 2 2023	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2024	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2024	Normal day	Camperdown/Darlington, Sydney	View
Semester 1 2025	Normal day	Camperdown/Darlington, Sydney	View
Semester 2 2025	Normal day	Camperdown/Darlington, Sydney	View

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Modes of attendance (MoA)

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爆料王

QBUS2310: Decision Analytics

2027 unit information

Unit details and rules

Managing faculty or University school:

Business (Business School)

Details

Enrolment rules

Learning outcomes

Unit availability

Modes of attendance (MoA)

Disclaimer

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