QBUS6820: Semester 1, 2024

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Units QBUS6820 Semester 1 2024 [Normal day]

Unit of study_

QBUS6820: Prescriptive Analytics: From Data to Decision

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Prescriptive analytics is concerned with using quantitative tools to turn data into managerial and operational decisions, in both deterministic settings and under risk. This unit introduces mathematical optimisation modelling, with applications to problems in management, logistics, economics, science and engineering. Students will learn techniques for rigorously formulating complex decision-making problems as mathematical models, state-of-the-art computational tools to solve the models, how to incorporate measures of risk into models, and how to interpret outputs of models in the relevant decision-making context. It is expected that students have a good understanding of fundamental data analytics concepts such as vectors, matrices, probability, and the Python programming language.

Unit details and rules

Unit code	QBUS6820
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	None
Prerequisites ?	ECMT5001 or QBUS5001
Corequisites ?	BUSS6002
Assumed knowledge ?	Vectors, matrices, probability, Python
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Kusha Baharlou, k.baharlou@sydney.edu.au
Lecturer(s)	Kusha Baharlou, k.baharlou@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Written exam.	50%	Formal exam period	2 hours
Outcomes assessed:
Participation	Participation 1 mark for each week of participation in both the lecture and tutorial	10%	Ongoing	NA
Outcomes assessed:
Assignment	Assignment 1 A written assignment submitted through Canvas.	20%	Week 06 Due date: 27 Mar 2024 at 23:59	N/A
Outcomes assessed:
Assignment	Assignment 2 A written assignment submitted through Canvas.	20%	Week 11 Due date: 08 May 2024 at 23:59	N/A
Outcomes assessed:

Assessment summary

Two assignments: You will solve a series of problems using mathematical modelling. The assignment problems will be similar to tutorial questions.
Final exam: The exam will cover all unit content. It will test your understanding of theory, your ability to solve problems, and your capacity to draw insights from models.
Participation: You will gain 1 mark toward your final mark each week you participate in both the lecture and your tutorial, up to a maximum of 10 marks.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Linear programming	Lecture and tutorial (3 hr)
Week 02	Linear programming	Lecture and tutorial (3 hr)
Week 03	Linear programming	Lecture and tutorial (3 hr)
Week 04	Integer programming	Lecture and tutorial (3 hr)
Week 05	Integer programming	Lecture and tutorial (3 hr)
Week 06	Transportation and assignment problems	Lecture and tutorial (3 hr)
Week 07	Network flow models	Lecture and tutorial (3 hr)
Week 08	Network flow models	Lecture and tutorial (3 hr)
Week 09	Project Management	Lecture and tutorial (3 hr)
Week 10	Goal programming	Lecture and tutorial (3 hr)
Week 11	Multi-objective linear programming	Lecture and tutorial (3 hr)
Week 12	Non-linear programming	Lecture and tutorial (3 hr)
Week 13	Review	Lecture and tutorial (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

There are no required textbooks.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

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

LO1. select between prescriptive analytic models according to the business context
LO2. create prescriptive analytic models for decision-making
LO3. make decisions based on model outputs
LO4. use software tools to analyse and solve models

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Several changes have been made to simplify the assessment structure.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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