Course coordinator
Dr Zara Taba
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
The use of analytics to support decision making is the goal of prescriptive analytics. Optimisation and simulation techniques are often the focus of prescriptive analytics exercises and are the primary focus of this course. These methods - optimisation and simulation techniques - provide analysts with powerful frameworks to solve business problems and guide decision making. The course brings these techniques to life using the R software to solve practical business problems.
In his ground-breaking book, Super Crunchers, by Ian Ayres shows that decision making informed by analytics can (positively) transform business, government, and society. Prescriptive analytics, which focuses on the use of analytics to inform decision making lies at the heart of this transformation. This improved decision making by made possible by using optimisation and simulation techniques, which are the core methods of prescriptive analytics. The application area could be any area of business from accounting and finance to management and marketing (for example, portfolio optimisation in the case of finance or revenue optimisation in the case of marketing). This course places special emphasis on linear programming, which is one of the most commonly used optimisation techniques in business. This technique and others (for example, stochastic optimisation) are brought to life using Python and R. The course provides an introduction to the key concepts in optimisation and simulation, and offers instruction and insight on the application of these techniques to problems in business.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM2204 or BSAN2204
Incompatible
You can't enrol in this course if you've already completed the following:
BISM3209
Course contact
Course staff
Lecturer
Dr Zara Taba
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full name
- Student ID
- Course Code
Aims and outcomes
Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the value of using prescriptive analytics to support decision making
LO2.
Explain the basic concepts used in prescriptive analytics, especially optimisation and simulation techniques
LO3.
Outline and describe a range of approaches that are used to solve optimisation problems in business, including linear and stochastic programming
LO4.
Implement optimisation and simulation techniques in R, especially linear programming
LO5.
Evaluate the value of prescriptive analytics to business practice, especially the use of optimisation and simulation techniques.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation, Presentation, Project | A1 - Optimisation Project | 35% |
17/04/2025 5:00 pm |
| Paper/ Report/ Annotation | A2 - Simulation | 35% |
19/05/2025 5:00 pm |
| Paper/ Report/ Annotation | A3 - Quiz | 30% |
Week 11, Tue 2:00 pm
During Class |
Assessment details
A1 - Optimisation Project
- Mode
- Oral, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Presentation, Project
- Weight
- 35%
- Due date
17/04/2025 5:00 pm
- Learning outcomes
- L01, L02, L04, L05
Task description
This first assignment is an optimisation task for a business addressing the needs of different stakeholders. More details will be provided on Blackboard and discussed in class.
Please Note: The presentation will be recorded for marking purposes per UQ Policy.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A2 - Simulation
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 35%
- Due date
19/05/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Details will be discussed during class and provided on Blackboard.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
A3 - Quiz
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
Week 11, Tue 2:00 pm
During Class
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This third assignment item is a School-based quiz. Expectations for the assessment will be discussed in class.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Seminar |
Course Introduction and Assessment Overview Learning outcomes: L01, L02, L03, L05 |
Week 2 |
Seminar |
Introduction to Business Analytics and Decision-Making Learning outcomes: L02, L03 |
Week 3 |
Seminar |
Optimisation and Optimal Decision-Making Learning outcomes: L02, L03, L04 |
Week 4 |
Seminar |
Solving LP Problems in Python Learning outcomes: L02, L03, L04 |
Week 5 |
Seminar |
Sensitivity analysis Learning outcomes: L02, L03, L04 |
Week 6 |
Seminar |
Integer Programming Models Learning outcomes: L02, L03, L04 |
Week 7 |
Seminar |
Case studies in Integer IP Learning outcomes: L03 |
Week 8 |
Seminar |
Linear programming case studies- R peogramming Learning outcomes: L02, L03, L04 |
Week 9 |
Seminar |
Simulation Modelling for Decision-Making Part 1 Learning outcomes: L01, L02, L04 |
Week 10 (05 May - 11 May) |
Seminar |
Simulation Modelling for Decision-Making Part 2 Learning outcomes: L01, L02, L04 |
Week 11 |
Seminar |
Multi Criteria Decision Making Learning outcomes: L01 |
Week 12 |
Seminar |
The Future of Business Analytics and Introduction to stochastic programming Learning outcomes: L01, L05 |
Week 13 |
Seminar |
Course review Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.