This course introduces students to R for the purpose of business analytics and to basic programming concepts used in R. The focus is on an introduction to R, including R infrastructure and interfaces, manipulating and exploring data, predictive models, data export and output, and optimising R code.
The book/film Moneyball by Michael Lewis has captured the popular imagination – showing the value of analytics of improving decision making and achieving a desired outcome. Moneyball illustrates the application of analytics to professional sports, but there are many more applications and none more important than the application of analytics to business. The application of analytics can significantly improve organisational outcomes across all aspects of business -- accounting/finance, people/talent, operations, marketing, social media, and supply chain. The course BSAN2204 Methods of Business Analytics is the first course in the business analytics major after the course BSAN2201 Principles of Business Analytics. The focus of R for Business Analytics is the methods of business analytics and their applications to solving business problems – preparing students for and equipping them with the skills they need to complete the business analytics major and to pursue careers with the analytics advantage.
You'll need to complete the following courses before enrolling in this one:
Maths B or Maths C or MATH1040
We recommend completing the following courses at the same time:
BISM2201 or BSAN2201
You can't enrol in this course if you've already completed the following:
BISM2204
Please use the BSAN2204 Microsoft Teams page for all course content related enquiries. More information on how to access the BSAN2204 Teams page will be provided in class.
Please only contact the course coordinator email for administrative enquiries.
The timetable for this course is available on the UQ Public Timetable.
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:
The primary aim of the course is to provide students with an overview of the methods of business analytics and to prepare students to successfully enter and complete the subsequent courses in the business analytics major. The methods include data visualisation, basic statistical analysis, predictive analytics/forecasting, data mining/machine learning, and text/web analytics. The course also provides an introduction to the use of R -- a mathematical computing environment for analytics used by millions around the world. The course leverages insights from the companion course -- Principles of Business Analytics -- to show how the methods of business analytics can solve real business problems.
After successfully completing this course you should be able to:
LO1.
Recognise and explain the role of R for business analytics.
LO2.
Explain the basic concepts used in R for managing and manipulating data.
LO3.
Apply R for basic business analytics tasks, including data visualisation and predictive analytics/forecasting.
LO4.
Compare and critically evaluate competing methods of business analytics.
LO5.
Demonstrate how business analytics can inform and improve managerial decision making.
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation | Data Exploration Report (A1) | 30% |
12/09/2025 4:00 pm |
| Computer Code, Paper/ Report/ Annotation | Predictive Analytics Report (A2) | 40% |
17/10/2025 4:00 pm |
| Computer Code, Examination, Practical/ Demonstration |
Practical Demonstration (A3)
|
30% |
Exam week 1 - Exam week 2
The Practical Demonstration (A3) will be scheduled during the Examination Period |
12/09/2025 4:00 pm
The Data Exploration Report (A1) will document the process of using R to explore a subset of the Million Song Dataset (MSD) using descriptive and visual methods of analysis. The descriptive methods should include measures of central tendency for continuous variables, measures of counts/proportions for categorial variables and bivariate statistics. The visual methods should include univariate and bivariate graphs.
Students must echo all R code used to generate their report and provide concise plain-text commentary explaining the R syntax and functions used (i.e., all code related to reading, processing and structuring the dataset, and code related to descriptive and visual methods of analysis). Additionally, the plain-text sections should further highlight the key insights that emerge from the descriptive and visual methods of analysis.
The Data Exploration Report (A1) must be written using R Markdown and submitted as a rendered HTML file (*.html).
The marking rubric will include criteria to assess the quality of the analysis, document presentation and adherence to submission guidelines.
A full briefing of the Data Exploration Report (A1) will be provided in class during lectures and tutorials which will outline the suggested document structure, length requirements, and submission guidelines.
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.
Submit as a .html file to Turnitin submission link on Blackboard.
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
17/10/2025 4:00 pm
The Predictive Analytics Report (A2) will extend the analysis presented in the Data Exploration Report (A1). The predictive analytics will comprise using a multiple linear regression model to estimate the drivers of song hotness within a subset of the Million Song Dataset (MSD). Attempts should be made to improve the model by dealing with missing data, nonlinearity, and interactions amongst the input variables. The report should conclude with an evaluation of the model, providing a critical assessment of its usefulness for prediction using model validation.
Students must echo selected R code, limited only to the code relevant to the methods predictive analytics processes (model fitting, handling missing data, evaluating model performance). For these code sections, students must provide concise plain-text commentary explaining the analytical rationale and interpretation of results (i.e., not the R code syntax or functions used). Code related to reading, processing and structuring the dataset should not be echoed to the rendered Predictive Analytics Report (A2).
The Predictive Analytics Report (A2) must be written using R Markdown and submitted as a rendered HTML file (*.html).
The marking rubric will include criteria to assess the quality of the analysis, document presentation and adherence to submission guidelines.
A full briefing of the Predictive Analytics Report (A2) will be provided in class during lectures and tutorials, which will outline the suggested document structure, length requirements, and submission guidelines.
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.
Submit as a .html file to Turnitin submission link on Blackboard.
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
Exam week 1 - Exam week 2
The Practical Demonstration (A3) will be scheduled during the Examination Period
The Practical Demonstration (A3) is an observed form of assessment. Students will receive a dataset and a briefing written in the form of an email from a hypothetical business analytics colleague/client asking for "request for a quick data summary and analysis". The task requires students to produce a minimally formatted report using R Markdown under observation that includes descriptive statistics, visualisations, a regression model with concise interpretations using the data provided. Students must complete their response to the client briefing under observation within the time constraint of an examination setting.
To mirror the tools and challenges of a professional work setting, students will be permitted to access the Internet and use AI tools during the assessment time to assist in the completion of the assessment. This assessment evaluates practical skills related to data exploration and predictive analytics, adaptability, and proficiency in using real-world tools under time constraints.
A full briefing of the Practical Demonstration (A3) will be provided in class during lectures and tutorials which will outline the suggested response structure, length, and submission guidelines. The Week 12 and 13 lectures and tutorial will cover a review of the topics relevant to the Practical Demonstration (A3).
This Practical Demonstration (A3) must be written using R Markdown and submitted as a rendered HTML file (*.html) before the end of the examination time.
The marking rubric will include criteria to assess the quality of the analysis, report presentation and adherence to submission guidelines.
AI Statement
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.
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| Materials | A computer device and access to the internet and the use of AI tools will be permitted. |
| Exam platform | Other |
| Invigilation | Invigilated in person |
The finalised .html file must submitted by the end of the examination session.
You may be able to defer this exam.
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. |
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment is available for this course.
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.
Find the required and recommended resources for this course on the UQ Library website.
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Lecture |
Introduction: Methods of Business Analytics Learning outcomes: L01, L02 |
Week 2 |
Lecture |
Introduction to Software: R and R Studio Learning outcomes: L01, L02 |
Tutorial |
Introduction to Tutorials Learning outcomes: L01, L02 |
|
Week 3 |
Lecture |
Assignment Briefing: Data Exploration Report (A1) Learning outcomes: L01, L02, L03 |
Tutorial |
Practical: Using Software: R and R Studio Learning outcomes: L01, L02 |
|
Week 4 |
Lecture |
Business Analytics Reports: Introduction to R Markdown Learning outcomes: L02, L03, L04 |
Tutorial |
Discussion: Data Exploration Report (A1) Learning outcomes: L01, L02, L03 |
|
Week 5 |
Lecture |
Exploratory Analysis: Describing and Visualising Data Learning outcomes: L02, L03, L04 |
Tutorial |
Practical: Using R Markdown Learning outcomes: L02, L03, L04 |
|
Week 6 |
Lecture |
Assignment Briefing: Predictive Analytics Report (A2) Learning outcomes: L03, L04, L05 |
Tutorial |
Practical: Describing and Visualising Data in R Learning outcomes: L02, L03, L04, L05 |
|
Week 7 |
Lecture |
Predictive Analytics: Introduction to Regression Modelling Learning outcomes: L03, L04, L05 |
Tutorial |
Discussion: Predictive Analytics Report (A2) Learning outcomes: L03, L04, L05 |
|
Week 8 |
Lecture |
Building Smarter Models: Dealing with Missing Data, Nonlinearity, and Interactions Learning outcomes: L03, L04, L05 |
Tutorial |
Practical: Using Regression in R Learning outcomes: L03, L04, L05 |
|
Week 9 |
Lecture |
Evaluating Models: Assessing the Usefulness of Predictive Models Learning outcomes: L03, L04, L05 |
Tutorial |
Practical: Dealing with Missing Data, Nonlinearity, and Interactions in R Learning outcomes: L03, L04, L05 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
No student involvement (Breaks, information) |
No live-lecture this week Monday Public Holiday (06/10/2025) |
Tutorial |
Practical: Model Validation and Prediction in R No Monday tutorials this week Learning outcomes: L03, L04, L05 |
|
Week 11 |
Lecture |
Assignment Briefing: Practical Demonstration (A3) Learning outcomes: L03, L04, L05 |
Tutorial |
Workshop: Predictive Analytics Report (A2) Finalisation/Troubleshooting Learning outcomes: L03, L04, L05 |
|
Week 12 |
Lecture |
Review of Topics: Data Exploration and Predictive Analytics Learning outcomes: L03, L04, L05 |
Tutorial |
Discussion: Practical Demonstration (A3) Learning outcomes: L03, L04, L05 |
|
Week 13 |
Lecture |
Course Reflection and Future Business Analytics Pathways Learning outcomes: L01 |
Tutorial |
Practice Session: Practical Demonstration (A3) Learning outcomes: L01 |
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:
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.