Course coordinator
Dr Katie Williams
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
BSAN7215 is the capstone course of the Master of Business Analytics to be completed in the final semester of study. It requires students to apply their business analytics skills to a real-world business problem. Students will identify a business problem and related data, solve the problem by applying various analytics and visualisation techniques, and present the findings in a report to senior management. To do this successfully, students will draw on skills gained throughout the program and their existing business knowledge.
This capstone course in the Master of Business Analytics program is designed to apply your knowledge from all other courses that have been completed throughout your business analytics journey. In this course, you will work with an industry partner to apply your skills to a real-world scenario, using creative and critical thinking to solve a business problem faced by your client.
By utilising an agile approach, you’ll embark on a journey where continuous learning and reflection will be your guiding principles. Along the way, you’ll develop a unique deliverable to add to your portfolio, which may include a series ofﾠdashboards, a predictive model to unveil hidden patterns, as well as business insights and recommendations that are succinct and invaluable.
You will be working under the direction of two teams: the academic team who will be responsible as your facilitator in guiding you through your knowledge demonstration, and your industry partner who will provide business guidance on the feasibility of your project.
This course does not require you to learn any new material. It does, however, require you to demonstrate knowledge in the pre-requisite courses and other courses. As such, this course is recommended to be completed towards the end of your studies.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BSAN7208 + 7209 + 7210 + 7212
Restrictions
Restricted to students in their final semester of the MBusAn program
Course contact
Course staff
Lecturer
Dr Katie Williams
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
This capstone course aims to provide students with unique opportunities to approach and solve authentic business problems, working collaboratively with industry and academic stakeholders, using innovative business analytic skill sets.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply innovative analytics-based solutions for complex business problems in collaboration with industry partner and/or peers.
LO2.
Communicate effectively and professionally to justify project outcomes to relevant stakeholders.
LO3.
Evaluate and reflect on project implications to inform future business practices.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Product/ Design | Prototype | 20% |
21/03/2025 4:00 pm |
| Presentation | Presentation | 30% |
9/05/2025 4:00 pm |
| Paper/ Report/ Annotation | Industry Report | 30% |
30/05/2025 4:00 pm |
| Reflection | Reflective Essay | 20% |
20/06/2025 4:00 pm |
Assessment details
Prototype
- Mode
- Product/ Artefact/ Multimedia
- Category
- Product/ Design
- Weight
- 20%
- Due date
21/03/2025 4:00 pm
- Learning outcomes
- L01
Task description
For this assessment, you will create an initial prototype of the envisaged analytics solution that will be developed throughout the course. The prototype will help you test initial ideas and gather feedback from project supervisors and teaching team early in the development process.
The prototype must be accompanied by a written report that explains how the prototype addresses the project’s business problem, how it meets the requirements, and why the proposed interface helps users make data-driven decisions from the business data.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
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.
Presentation
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 30%
- Due date
9/05/2025 4:00 pm
- Learning outcomes
- L01, L02, L03
Task description
As an individual (or as a team, if allocated), you are asked to deliver an oral and visual presentation to communicate key aspects of your developed solution to other teams and industry partners.
Your industry supervisor should be the target audience in the presentation, who needs to be convinced that your analytics solution produces data-driven insights that address the business problem in an effective, technically sound, and creative way. You should do this by communicating effectively; that is, using concise, fluent and persuasive language, and structuring the presentation in a way that keeps the audience engaged. The final part of the assessment integrates a Q&A segment, when your industry partner will ask you questions and provide feedback. This provides a great opportunity to gather valuable feedback from them, which can be incorporated in the final industry report.
Please Note: The presentation will be recorded for marking purposes per UQ Policy.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Extensions or deferrals are not available for this presentation. An extension may be available for the submitted material only.
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.
10% Late Penalty applies to submitted material only. Late submissions are not accepted for in-class presentations. Failure to present at the scheduled time will result in a mark of zero for the presentation portion of this assessment.
Industry Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
30/05/2025 4:00 pm
- Learning outcomes
- L01, L02
Task description
A written report describing the analytics solution produced and how it was developed, providing justifications for the methods and approaches used in relation to the business problem identified earlier in the course.
The report must also discuss in detail the findings, insights and/or business recommendations produced by your analytics solutions, and how they can be applied by the industry partner to address the business problem and needs. This final report is intended to accompany the analytics solution developed, providing more detailed information about what your team has produced along the course of the project, how and why it was produced in that way.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submissions independent of AI tools.
Submission guidelines
To be submitted via Blackboard
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.
Reflective Essay
- Mode
- Written
- Category
- Reflection
- Weight
- 20%
- Due date
20/06/2025 4:00 pm
- Learning outcomes
- L03
Task description
For this assessment, you will need to develop an essay reflecting on your project outputs and learning experience while solving a complex business problem with data analytics.
AI Statement:
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task; however, students must clearly reference any use of AI in each instance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submissions independent of AI tools.
Submission guidelines
To be submitted via Blackboard
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.
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
Library resources are available 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 |
Kick-Off Meeting Kick-off meeting with teaching team and industry partner. The session includes an initial project planning discussion. Learning outcomes: L01 |
Multiple weeks From Week 2 To Week 3 |
Not Timetabled |
Review Content and Commence Ideation Self-Directed Learning: Review content from your previous courses covered in the Master of Business Analytics and identify theories and frameworks that are applicable to your project. Continue to work on Assessment 1. Learning outcomes: L01 |
Week 4 |
Seminar |
Prototype Soft-pitch Opportunity for students to pitch and discuss their prototype with the industry partner. Learning outcomes: L01, L02, L03 |
Week 5 |
Not Timetabled |
Reflect Self-Directed Learning: Students can apply feedback received from the Industry Partner and Academic team. Students can work towards Assessments 2 and 3. Learning outcomes: L01, L02, L03 |
Multiple weeks From Week 6 To Week 9 |
Not Timetabled |
Sprint Self-Directed Learning: Students continue to work on Assessment 2 and 3. Learning outcomes: L01, L02, L03 |
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Not Timetabled |
Presentations and Feedback Learning outcomes: L01, L02, L03 |
Week 11 |
Seminar |
Presentation Rebuttal Students to provide oral rebuttal to feedback from presentation. Students to continue solution development and incorporate feedback. Learning outcomes: L01, L02, L03 |
Week 12 |
Not Timetabled |
Sprint Self-Directed Learning: Project work Learning outcomes: L01, L02, L03 |
Week 13 |
General contact hours |
Final Consultation Opportunity for students to consult with academic team to clarify aspects of remaining assessments. Learning outcomes: L01, L02, L03 |
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.