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 the course is the development of skills in using R – 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.
Course Changes in Response to Previous Student Feedback
Student feedback on BSAN2204 has generally been positive. Previous iterations of the course have variably had the business analytics report submitted as either two separate reports (as was the case in 2025) or as one overall report (as was the case in years prior). Feedback from students in 2025 suggests that the single report model (submitted towards the end of the teaching weeks) works best hence this model will be used going forward from 2026. In 2026 a new assessment conducted within tutorials has been introduced which tests students independent knowledge of the basic functions of R which are necessary to use to get started on completing the final business analytics report and for completing the final in-class practical demonstration. Feedback from students in 2025 suggests the need for an early piece of assessment which can sign post to students which of their R skill development areas they need to address before commencing the latter items of assessment in BSAN2204 (and indeed, the use of R more generally within the business analytics major).
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, Examination, Quiz |
R Skills Tutorial Quiz (A1)
|
20% |
Week 6 Mon - Week 6 Fri
During your Tutorial Class |
| Computer Code, Paper/ Report/ Annotation | Predictive Analytics Report (A2) | 50% |
23/10/2026 4:00 pm |
| Examination |
Practical Demonstration (A3)
|
30% |
End of Semester Exam Period 7/11/2026 - 21/11/2026
The Practical Demonstration (A3) will be scheduled during the Examination Period |
Week 6 Mon - Week 6 Fri
During your Tutorial Class
The R Skills Tutorial Quiz (A1) occurs during tutorials as a paper based assessment testing basic knowledge about R as a tool for business analytics taught in the first part of the of the course.
This assessment requires students to internalise the basics of R which necessary to independently start an R project, critique R syntax and/or troubleshoot basic R errors without a reliance on AI tools. The feedback students receive on this early piece of assessment is intended to sign post which of their R skill development areas they need to address before being able to confidently and independently get started on the final business analytics report (A2) and for completing the final in-class practical demonstration (A3).
A full briefing of the R quiz (A1) will be provided in class during lectures and tutorials, covering more specific requirements within each section (i.e., extending what is outlined above), providing guidance on how to study specific content areas covered by the quiz.
Secure assessment: This assessment is designed to protect academic integrity and assess your understanding of course concepts. You must only use the approved materials and tools for this assessment task. Use of prohibited materials in this task may be investigated and treated as academic misconduct. See assessment instructions for more details.
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed book examination - specified written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
You may be able to defer this exam.
23/10/2026 4:00 pm
The Predictive Analytics Report (A2) is a written HTML file (*.html) report using R Markdown that documents an analysis of a dataset provided in class: the Million Song Dataset (MSD).
The report includes the following sections:
The introduction and exploratory analysis section comprises reading and processing a subset of a data frame provided in class (the MSD.rdata file available on Blackboard) into training and test data, and producing tables of descriptive statistics of selected variables for the training data and generating visualisations for justified variables for the training data. The predictive analytics section comprises specifying several different competing linear regression models of the training data, using imputation methods to deal with missing data in both the training and test data and using split sample model cross validation methods to predict observations in the the test data. The recommendations section comprises making predictions (forecasts) of estimated "song hotness" for songs which do not exist in the MSD, with the aim to rank order songs for potential inclusion a song recommendation algorithm.
A full briefing of the Predictive Analytics Report (A2) will be provided in class during lectures and tutorials, covering more specific requirements within each section (i.e., extending what is outlined above), providing guidance on what code should (and should not) be echoed in the report using R Markdown, guidance on document structure/length, and discussing the submission guidelines to ensure they are followed.
The marking rubric will include criteria to assess the quality of the analysis, document presentation and adherence to submission guidelines.
Open assessment: In this assessment you can use resources such as your notes, course materials, and other tools, including AI, while completing the task. The focus is not on memorising information, but on understanding, applying judgement, and demonstrating your own thinking.
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.
End of Semester Exam Period
7/11/2026 - 21/11/2026
The Practical Demonstration (A3) will be scheduled during the Examination Period
The Practical Demonstration (A3) is an observed form of assessment. Students will be given access to a unique dataset on BlackBoard at the start of the assessment time 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 HTML file (*.html) report using R Markdown under observation that includes descriptive statistics, visualisations and 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, covering more specific requirements within each section (i.e., extending what is outlined above), providing guidance on what code should (and should not) be echoed in the report using R Markdown, guidance on document structure/length, and discussing the submission guidelines to ensure they are followed.
The marking rubric will include criteria to assess the quality of the analysis, report presentation and adherence to submission guidelines.
Secure assessment: This assessment is designed to protect academic integrity and assess your understanding of course concepts. You must only use the approved materials and tools for this assessment task. Use of prohibited materials in this task may be investigated and treated as academic misconduct. See assessment instructions for more details.
| 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 (laptop) and access to the internet and the use of AI tools will be permitted. |
| Exam platform | Other |
| Invigilation | Invigilated in person |
Submit as a .html file to Turnitin submission link on Blackboard during the exam time.
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.
Library resources are available 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 to Methods of Business Analytics Learning outcomes: L01 |
Week 2 |
Lecture |
Basics of R and R Studio Learning outcomes: L01, L02 |
Tutorial |
Introduction to Tutorials Learning outcomes: L01 |
|
Week 3 |
Lecture |
Report writing using R Markdown Learning outcomes: L01, L02 |
Tutorial |
Using R in R Studio Ekka Public Holiday - Wednesday 12th August 2026 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02 |
|
Week 4 |
Lecture |
Data Processing Learning outcomes: L01, L02 |
Tutorial |
Report writing using R Markdown Learning outcomes: L01, L02 |
|
Week 5 |
Lecture |
R Skills Tutorial Quiz (A1) - Lecture Briefing Learning outcomes: L01, L02 |
Tutorial |
Data Processing Learning outcomes: L02, L03 |
|
Week 6 |
Lecture |
Exploratory Analysis Learning outcomes: L02, L03 |
Tutorial |
R Skills Quiz (A1): IN-CLASS ASSESSMENT Learning outcomes: L01, L02 |
|
Week 7 |
Lecture |
Regression Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Predictive Analytics Report (A2) - Tutorial Briefing Learning outcomes: L03, L04, L05 |
|
Week 8 |
Lecture |
Dealing with Missing Data Learning outcomes: L03, L04, L05 |
Tutorial |
Exploratory Analysis Learning outcomes: L03, L04, L05 |
|
Week 9 |
Lecture |
Model Validation Learning outcomes: L03, L04, L05 |
Tutorial |
Regression Learning outcomes: L03, L04, L05 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Lecture |
Making predictions Learning outcomes: L03, L04, L05 |
Tutorial |
Dealing with Missing Data Labour Day Public Holiday - Monday 4th May 2026 - Check Blackboard for announcements about affected classes. Learning outcomes: L03, L04, L05 |
|
Week 11 |
Lecture |
Practical Demonstration (A3) - Lecture Briefing Learning outcomes: L03, L04, L05 |
Tutorial |
Model Validation Learning outcomes: L03, L04, L05 |
|
Week 12 |
Lecture |
Course Reflection and Future Business Analytics Pathways Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Making predictions Learning outcomes: L03, L04, L05 |
|
Week 13 |
Lecture |
No lecture this week |
Tutorial |
Building a Template R Markdown File (.Rmd) for A3 Learning outcomes: L03, L04, L05 |
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.