Course coordinator
Professor Ashish Sinha
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
The application of business analytics can be the basis for competitive advantage; however, this advantage is achieved only if the leadership of a firm appreciates the value of business analytics and appropriately manages the analytics capability of the firm. This course is designed with two objectives in mind. Firstly, the course aims to provide students of business analytics with an appreciation of how a firm can organise to compete successfully on the basis of the analytics capability of the firm. Topics include developing an analytics culture and managing analytics teams, implementing and evaluating business analytics strategies, and industry case studies. Second, the course provides students with insights into careers in business analytics from the perspective of business analysts and data scientists, analytics managers, and chief analytics officers. In the professional practice week students are introduced to representatives of the key domestic and international associations for business analytics professionals.
This course will help students learn how to tackle complex, uncertain, ambiguous problems and by doing so learn:
- We are not looking for the right answer; we are helping someone make a decision.
- Data driven decision making is part of a bigger picture in which the decision takes place.
- The other factors around the analytical method are just as critical as the data and analytics.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(BISM2204 or BSAN2204) + 4 units from the Business Analytics major
Incompatible
You can't enrol in this course if you've already completed the following:
BISM4201
Course contact
Course staff
Lecturer
Professor Ashish Sinha
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 course is designed with two objectives in mind. Firstly, the course aims to provide students of business analytics with an appreciation of how a firm can organise to compete successfully on the basis of the analytics capability of the firm. Topics include evaluation and selection of the right analytics approaches, implementing and evaluating business analytics strategies, and industry case studies. Second, the course provides students with insights into careers in business analytics from the perspective of business analysts and data scientists, analytics managers, and chief analytics officers.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain and recognise suitable problems in data science and business analytics
LO2.
Explain the strategy and modelling for data science and business analytics projects
LO3.
Explain the main data analytic methods for solving data science and business analytics projects
LO4.
Explain how to use data analytic methods in real-life
LO5.
Demonstrate how business strategy based on data science and business analytics can inform and improve business performance.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Portfolio |
Food Truck Simulation
|
15% |
5/09/2025 4:00 pm |
| Practical/ Demonstration, Presentation |
Project Presentation
|
30% |
13/10/2025 4:00 pm
Presentation During Tutorial From Week 11-Week 13. You are required to make the presentation in-person in the respective tutorial. ALL PRESENTATION DECKS to be submitted by the 13/10/2025 @4pm |
| Paper/ Report/ Annotation, Essay/ Critique | Project Reflections | 15% |
31/10/2025 4:00 pm
Each student has to submit a 1500 word reflection on the SMART PRODUCT project. |
| Examination |
Final Exam -- Case Study
|
40% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
Assessment details
Food Truck Simulation
- In-person
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation, Portfolio
- Weight
- 15%
- Due date
5/09/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
In pre-assigned groups of 3-4 students, you will be running a Food Truck Simulation. Each student has to submit a 1500 word Reflections on the simulation. The simulation will be run in Week 4 Tutorial. All Students are expected to attend the tutorial.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
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 submission independent of AI tools.
Submission guidelines
Assessment items to be uploaded to Blackboard as a PDF
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
Project Presentation
- Identity Verified
- In-person
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Practical/ Demonstration, Presentation
- Weight
- 30%
- Due date
13/10/2025 4:00 pm
Presentation During Tutorial From Week 11-Week 13. You are required to make the presentation in-person in the respective tutorial. ALL PRESENTATION DECKS to be submitted by the 13/10/2025 @4pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L01, L02, L03, L04
Task description
Throughout the semester you will be designing a Smart product (AI- and Data-embedded product). You are required to present a suitable solution (Smart Product) to the problem identified using GenAI augmented Design Thinking. This will comprise 1) assessing the product-market fit using GenAI simulated Synthetic data, 2) Developing its GTM strategy, in particular the Business Model, and 3) the type of data and analytics deployed in this product.
Each group’s presentation will be 20 minutes long and will be presented in the tutorial and should be done via Powerpoint over Zoom to allow the recording of the presentations.
Students will be in groups of 4 (or 3 if a final group of 4 is not possible) and it is expected each student will present for 5 minutes (6.5 minutes in case there is a group of 3 students). Students will be presenting in the tutorial.
If you are unable to find a group of 4 to join, please let the tutor know and we will connect you with other students. You will be expected to have sorted out your group by week 3.
Note that although the presentation is in a group, marks will be individual based on your part of the presentation.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
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 submission independent of AI tools.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
Project Reflections
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Essay/ Critique
- Weight
- 15%
- Due date
31/10/2025 4:00 pm
Each student has to submit a 1500 word reflection on the SMART PRODUCT project.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Throughout the semester you will be designing a SMART PRODUCT (AI- and Data-embedded product). You are required to devlop a suitable solution (Smart Product) to the problem identified using GenAI augmented Design Thinking. This will comprise 1) assessing the product-market fit using GenAI simulated Synthetic data, 2) Developing its GTM strategy, in particular the Business Model, and 3) the type of data and analytics deployed in this product.
Each student has to submit a 1500 word reflection on the SMART PRODUCT project.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
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 submission independent of AI tools.
Submission guidelines
Upload on Blackboard!
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
Final Exam -- Case Study
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This will be describing a situation that I will ask you to analyse some aspect of it.
It will be related to work we’ve previously done and you will encounter similar types of examples along the journey.
AI Statement:
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
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 |
Lecture |
Welcome to BSAN4201 -- Analytics BSAN4201 overview and an introduction to analytics decision framework Learning outcomes: L01, L02 |
Week 2 |
Lecture |
Uncertainty and Business Analytics Strategy A lecture covering the course materials Learning outcomes: L01, L02 |
Tutorial |
Tute 1: Intro to data driven decision making Learning outcomes: L01, L02, L05 |
|
Week 3 |
Lecture |
Experimentation Fundamentals & Organisational Culture Introduction to data driven decision making tutorial Learning outcomes: L01, L02, L05 |
Tutorial |
Tute 2: Organisational Culture and Experimentation A lecture covering the course materials Learning outcomes: L01, L02, L05 |
|
Week 4 |
Lecture |
Data to Decisions Learning outcomes: L01, L02, L05 |
Tutorial |
Tute 4: Experimentation Simulation Running of a Simulation Learning outcomes: L04, L05 |
|
Week 5 |
Lecture |
Designing Disruptive Innovations Using Data and AI Learning outcomes: L04, L05 |
Tutorial |
Tute 4: Data to Decisions Learning outcomes: L03, L04 |
|
Week 6 |
Lecture |
Problem-Solution Fit for Smart Products & Design Thinking Learning outcomes: L03, L04 |
Tutorial |
Tute 5: Design Thinking for Smart Products using GenAI Learning outcomes: L03, L04 |
|
Week 7 |
Lecture |
Product-Market Fit Using Primary, Secondary and Synthetic Data Learning outcomes: L03, L04 |
Tutorial |
Tute 6: Product-Market Fit using Primary and Secondary Data Learning outcomes: L01, L02, L03, L04 |
|
Week 8 |
Lecture |
AI Monetization Framework & Smart Product Design Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tute 7: Agentic AI workflows and Synthetic Data Learning outcomes: L01, L02, L03, L04 |
|
Week 9 |
Lecture |
Integration, Strategy & Network Effects Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tute 8: Business Models of AI Products Learning outcomes: L01, L02, L05 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Lecture |
Accessibility, Affordability & Ethics through AI Learning outcomes: L01, L02, L05 |
Workshop |
Tute 9: Project Workshop Students to work on their projects in tutorial. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 11 |
Lecture |
Agentic Analytics and the Future AI-Led Analytics Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tute 10: In Person Presentations Students will be presenting their Group Projects in the Tutorial Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 |
Lecture |
Guest Speakers Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tute 11: In Person Presentations Students will be presenting their Group Projects in the Tutorial Learning outcomes: L01, L02, L03, L04 |
|
Week 13 |
Lecture |
Concluding Lecture To summarise the course and prepare for final assessment Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tute 13: Preparation for Exam Preparation for Exam |
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 for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.