Course coordinator
Dr Evan Shellshear
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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:
1.We are not looking for the right answer, we are helping someone make a decision.
2.Data driven decision making is part of a bigger picture in which the decision takes place
3.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
Dr Evan Shellshear
Tutor
Mr Ali Rad
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 | Portfolio presentation | 30% |
9/08/2024 4:00 pm |
| Practical/ Demonstration, Presentation |
Case study analysis
|
30% |
30/09/2024 - 14/10/2024
Presentation During Class |
| Paper/ Report/ Annotation | Take Home Case Study | 40% |
4/11/2024 - 8/11/2024
The Assessment will be released 9:00 AM on the Monday and is Due 5:00 PM on the Friday. |
Assessment details
Portfolio presentation
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation, Portfolio
- Weight
- 30%
- Due date
9/08/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The Portfolio Presentation will be based on you presenting yourself with an explanation as to why you are a suitable candidate to solve the Case Study to a manager.
The manager has never done a data science or business analytics project before and is likely to be a cynic of its value. It is based on the Case Study.
There are three parts to this:
- A tailored CV. The CV is to be two pages or less in length
- A tailored cover letter. The cover letter is to be be two pages or less in length,
- A portfolio presentation of relevant capabilities from university projects. The portfolio is to be three pages or less in length including images.
Your portfolio will emphasise previous work you have done (e.g. on Github, blogs, Uni projects, etc) and the CV and cover letter will be tailored to the job.
Don’t get into the detail of what you would do, how you would do it, etc. You need to convince someone who doesn’t see value in doing the project that it is worthwhile taking you on board and engaging you to do the work – they won’t understand detailed technical explanations.
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.
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.
Case study analysis
- Identity Verified
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Practical/ Demonstration, Presentation
- Weight
- 30%
- Due date
30/09/2024 - 14/10/2024
Presentation During Class
- Other conditions
- Peer assessed.
- Learning outcomes
- L01, L02, L03, L04
Task description
You will have to demonstrate the value of business analytics and your understanding of how to approach business analytics challenges successfully to a business problem provided to you.
You need to analyse a Case Study and present a suitable approach to solving the problem. After the third lecture you should be in a position to begin working on the presentation. The case study is deliberately vague and doesn’t contain all details required. You will be required to fill in these gaps (explained in lectures).
Each group’s presentation will be 15 minutes long and will be presented to the class and should be done via Powerpoint or Keynote or similar and will be done over Zoom to allow the recording of the presentations.
Students will be in groups of 3 (or 2 or 4 if a final group of 3 is not possible) and it is expected each student will present for 5 minutes (7.5 minutes per student in case there is a group with 2 students or 4 minutes in case there is a group of 4 students). Students will be presenting to the class.
If you are unable to find a group of 3 to join, please let us 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.
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.
Take Home Case Study
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
4/11/2024 - 8/11/2024
The Assessment will be released 9:00 AM on the Monday and is Due 5:00 PM on the Friday.
- 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.
It will be provided during exam week.
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 uploaded to Blackboard as a PDF
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
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 |
Lecture |
Welcome to BSAN4201 BSAN4201 overview and an introduction to data driven decision making Learning outcomes: L01, L02 |
Week 2 |
Lecture |
Data Science 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 |
Tutorial |
Tute 2: Data science & Business Analytics Strategy Introduction to data driven decision making tutorial Learning outcomes: L01, L02, L05 |
Lecture |
Modelling for Insights A lecture covering the course materials Learning outcomes: L01, L02, L05 |
|
Week 4 |
Tutorial |
Tute 3: Modelling for Insights Learning outcomes: L01, L02, L05 |
Lecture |
Sense Checking Learning Objectives Learning outcomes: L04, L05 |
|
Week 5 |
Tutorial |
Tute 4: Sense checking Learning outcomes: L04, L05 |
Lecture |
Predicting the Future Learning outcomes: L03, L04 |
|
Week 6 |
Tutorial |
Tute 5: Predicting the Future Learning outcomes: L03, L04 |
Lecture |
Optimisation and Strategic Decision Making Learning outcomes: L03, L04 |
|
Week 7 |
Tutorial |
Tute 6: Optimisation and Strategic Decision Making Learning outcomes: L03, L04 |
Lecture |
Communicating Your Results Learning outcomes: L01, L02, L03, L04 |
|
Week 8 |
Tutorial |
Tute 7: Communicating Your Results Learning outcomes: L01, L02, L03, L04 |
Lecture |
How Confident Are You of Your Answer? Learning outcomes: L01, L02, L03, L04 |
|
Week 9 |
Tutorial |
Tute 8: How Confident Are You of Your Answer? Learning outcomes: L01, L02, L03, L04 |
Lecture |
Why do analytics projects fail? Learning outcomes: L01, L02, L05 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Tutorial |
Tute 10: Why do analytics projects fail? Learning outcomes: L01, L02, L05 |
Lecture |
In Class Presentation Students to present in groups - individual marks Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 11 |
Tutorial |
In Class Presentations During Tutorial Learning outcomes: L01, L02, L03, L04, L05 |
Lecture |
King's Birthday Public Holiday - No Lecture this week. King's Birthday Public Holiday - No Lecture this week. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 |
Tutorial |
In Class Presentations Learning outcomes: L01, L02, L03, L04, L05 |
Week 13 |
Tutorial |
Final Tutorial Revision from Modelling from Insights book preparation for final assessment Learning outcomes: L01, L02, L03, L04 |
Lecture |
Concluding Lecture To summarise the course and prepare for final assessment Learning outcomes: L01, L02, L03, L04 |
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.