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
Business Analytics involves the application of statistics to understand and solve business problems. This course provides the statistical foundation for the Master of Business Analytics program, by introducing students to relevant statistical concepts required for data analysis. In addition, through a practical component, the course introduces students to applying appropriate statistical analysis through R.
This course will give you a foundational understanding of business data and the role of statistical analysis in creating value. This is a technical course in which we will empower you in analytics by introducing you to statistical computation using the R language, as well as relevant theory to help guide your understanding. Throughout the course, you will apply what you learn to a series of data sets from a range of business contexts. The assessments will be based around communicating your insights to a technical manager and other stakeholders.
The course is divided into four modules:
- Exploring Data
- Evidence from Data
- Model Building
- Model Evaluation
Each topic within a module begins by leading you through a business example, showing how statistical analysis is used to describe and model the data. You then put this into practice by conducting the analysis yourself using R. We have an Ed discussion board where you can share your findings and ask questions. There is then a formative quiz, for you to check your understanding. Each week we then get together for a live session, an important opportunity to reflect on the content and look at other examples together. Recordings of these sessions will be posted on Blackboard afterwards.
Check in to Blackboard regularly to see announcements. Blackboard also has all the details for completion and submission of your assessments.
Course requirements
Incompatible
You can't enrol in this course if you've already completed the following:
DATA7202 and ECON7300
Restrictions
Restricted to students in the MBusAn program
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
There will be a one hour online (via Zoom), live session at 6:00 pm (Brisbane time) Tuesday each week of semester. There will be a one hour online (via Zoom), tutorial session at 6:00 pm (Brisbane time) Thursday each week of semester. See Blackboard for details.
Aims and outcomes
The aim of this course is to provide foundational skills in the application of statistical methodology to understand and solve business problems.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Perform exploratory data analysis to describe data.
LO2.
Construct and evaluate models for inferential statistical analysis for real-world problem solving.
LO3.
Formulate data-driven insights and value propositions for stakeholders.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project | Exploratory Data Analysis | 30% |
28/03/2025 3:00 pm |
| Tutorial/ Problem Set |
Analytics Practice
|
20% |
23/05/2025 3:00 pm |
| Paper/ Report/ Annotation, Presentation | Technical Report and Stakeholder Presentation | 50% |
30/05/2025 3:00 pm |
Assessment details
Exploratory Data Analysis
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 30%
- Due date
28/03/2025 3:00 pm
- Learning outcomes
- L01
Task description
The objective of this assignment is for you to undertake an exploratory data analysis to summarise and highlight key aspects and special features of business data. You will consolidate your knowledge and develop skills in data analysis to solve business problems, provide insights and enhance your presentation and communication skills in a business context. This will further strengthen your ability to tackle problems in business data analysis and visualisation.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit via Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 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.
Analytics Practice
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
23/05/2025 3:00 pm
- Learning outcomes
- L01, L02
Task description
The modules contain a total of eight Analytics Practice tasks. Each task is an important opportunity to put the technical skills you are learning into practice by working with a new data set and using RStudio to answer five numerical questions. Correctly answering each question is worth 0.5 marks towards your overall grade (for a maximum of 20 marks). You can attempt each question as many times as you like before the due date. (There is no penalty for incorrect answers.)
You are encouraged to discuss how to answer the questions with your peers on Ed Discussion.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Complete via Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 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.
Technical Report and Stakeholder Presentation
- Mode
- Oral, Written
- Category
- Paper/ Report/ Annotation, Presentation
- Weight
- 50%
- Due date
30/05/2025 3:00 pm
- Learning outcomes
- L02, L03
Task description
The objective of this assessment is for you to work with a complex collection of data files in order to create a value proposition for a stakeholder. You will use techniques of data wrangling in R to generate and explore suitable variables to address a business question. You will then select and justify the relevant theory and advanced tools that can be applied to the variables. Your analysis should demonstrate the application the three core methods from Modules 3 and 4:
- Multiple linear regression
- Logistic regression and multiple logistic regression
- Time series
You will then present your insights from this analysis as a technical report to your supervisor and as a persuasive presentation to the stakeholders.
Please Note: The presentation will be recorded for marking purposes.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Further details of submission will be provided on Blackboard
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.
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 |
Introduction to Business Decision-Making and the Role of Data Understanding collective organisation as agreement, action and outcomes, and the business context and the importance of data in decision-making. Learning outcomes: L01 |
Practical |
Introduction to Business Decision-Making and the Role of Data Understanding collective organisation as agreement, action and outcomes, and the business context and the importance of data in decision-making. Learning outcomes: L01 |
|
Week 2 |
Seminar |
Framing the Question How to frame business problems in compelling ways that can be addressed through data analysis. Learning outcomes: L01 |
Practical |
Framing the Question How to frame business problems in compelling ways that can be addressed through data analysis. Learning outcomes: L01 |
|
Week 3 |
Seminar |
Introduction to Data and Exploratory Analysis The importance of understanding your data before diving into analysis. Learning outcomes: L01 |
Practical |
Introduction to Data and Exploratory Analysis The importance of understanding your data before diving into analysis. Learning outcomes: L01 |
|
Week 4 |
Seminar |
Hypothesis Testing in Business How to use hypothesis testing to validate or undermine business assumptions. Learning outcomes: L02 |
Practical |
Hypothesis Testing in Business How to use hypothesis testing to validate or undermine business assumptions. Learning outcomes: L02 |
|
Week 5 |
Seminar |
Linear Regression as a Predictive Tool Understanding the limitations and power of regression models. Learning outcomes: L02 |
Practical |
Linear Regression as a Predictive Tool Understanding the limitations and power of regression models. Learning outcomes: L02 |
|
Week 6 |
Seminar |
Multiple Regression and Model Building How to build models that capture key business variables without overcomplicating the analysis. Learning outcomes: L02 |
Practical |
Multiple Regression and Model Building How to build models that capture key business variables without overcomplicating the analysis. Learning outcomes: L02 |
|
Week 7 |
Seminar |
Logistic Regression and Predicting Outcomes Using data to predict categorical outcomes in business scenarios. Learning outcomes: L02 |
Practical |
Logistic Regression and Predicting Outcomes Using data to predict categorical outcomes in business scenarios. Learning outcomes: L02 |
|
Week 8 |
Seminar |
Time Series Analysis and Forecasting The importance of time series analysis in business forecasting. Learning outcomes: L02 |
Practical |
Time Series Analysis and Forecasting The importance of time series analysis in business forecasting. Learning outcomes: L02 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Seminar |
Model Selection and Validation How to select and validate the best model for your business problem. Learning outcomes: L02 |
Practical |
Model Selection and Validation How to select and validate the best model for your business problem. Learning outcomes: L02 |
|
Week 10 |
Seminar |
Integrating AI into Business Analytics How AI can enhance business analytics by making the analysis process more efficient. Learning outcomes: L03 |
Practical |
Integrating AI into Business Analytics How AI can enhance business analytics by making the analysis process more efficient. Learning outcomes: L03 |
|
Week 11 |
Seminar |
Communicating Results to Stakeholders The importance of translating statistical results into actionable business insights. Learning outcomes: L03 |
Practical |
Communicating Results to Stakeholders The importance of translating statistical results into actionable business insights. Learning outcomes: L03 |
|
Week 12 |
Seminar |
Case Study Integration and Review Integrating everything learned in the course through a comprehensive case study. Learning outcomes: L03 |
Seminar |
Case Study Integration and Review Integrating everything learned in the course through a comprehensive case study. Learning outcomes: L03 |
|
Week 13 |
Not Timetabled |
Final Reflections and Future Directions How to keep up with new tools and methods in a rapidly evolving field, while keeping in mind the two core reasons for all quantitative practices: collective organization via agreement and effective action. Learning outcomes: L03 |
Practical |
Final Reflections and Future Directions How to keep up with new tools and methods in a rapidly evolving field, while keeping in mind the two core reasons for all quantitative practices: collective organization via agreement and effective action. Learning outcomes: 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.