Course coordinator
Dr Thomas Magor
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
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 Louis 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 any many more applications and perhaps 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 R for 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.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
Maths B or Maths C or MATH1040
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
BISM2201 or BSAN2201
Incompatible
You can't enrol in this course if you've already completed the following:
BISM2204
Course contact
Course staff
Lecturer
Dr Thomas Magor
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
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.
Learning outcomes
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.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation | Project Report 1 | 50% |
11/04/2025 4:00 pm |
| Computer Code, Paper/ Report/ Annotation, Project | Project Report 2 | 50% |
23/05/2025 4:00 pm |
Assessment details
Project Report 1
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 50%
- Due date
11/04/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Project 1 will provide an initial analysis of the MSD (the "Million Song Dataset") using R. The project must begin by introducing the dataset by using descriptive analytics of the full dataset (i.e., describing all rows and columns of the dataset). Students must then select and provide a rationale for a specific subset of observations (rows) and variables (columns) of the MSD to be used for further analysis. Students must then provide descriptive statistics for all the variables in their chosen subset, a curated selection of data visualisations (univariate and bivariate displays) and one linear regression model. Throughout the project report, students must provide brief descriptions of the R functions used to generate their outputs.
It is expected that Project 1 will be written using standard word processing software (e.g., Microsoft Word).
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
Submit via submission link on Blackboard
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.
Project Report 2
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 50%
- Due date
23/05/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Project 2 will complete an extended analysis of the MSD, building upon the analysis presented in Project 1 written as an R Markdown file (.Rmd). Project 2 must include a re-writing of the initial analysis (corrected where necessary based on feedback), followed by applications of one or more techniques of model validation and dealing with missing data in their chosen subset of the MSD (the "Million Song Dataset"). Throughout Project 2, students must provide brief descriptions as plain text of the R functions used to generate their outputs, as well including the R syntax as R Markdown code chunks which generates the outputs included in the rendered report (.pdf).
For Project 2, students must separately submit both the R Markdown file (.Rmd) and the report file (.pdf) it generates.
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
Submit via submission link via Blackboard.
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 |
Course Introduction Learning outcomes: L01, L02, L03, L05 |
Week 2 |
Lecture |
Introduction to the Million Song Dataset Learning outcomes: L01, L02 |
Tutorial |
Tutorial Introduction Learning outcomes: L01, L02 |
|
Week 3 |
Lecture |
Descriptive Analytics Learning outcomes: L02, L03, L04 |
Tutorial |
Selecting a Subset of the MSD Learning outcomes: L02 |
|
Week 4 |
Lecture |
Data Visualisation Learning outcomes: L02, L03, L04 |
Tutorial |
Descriptive Analytics (MSD) Learning outcomes: L03, L05 |
|
Week 5 |
Lecture |
Linear Regression Learning outcomes: L02, L03, L04 |
Tutorial |
Data Visualisation (MSD) Learning outcomes: L03, L05 |
|
Week 6 |
Lecture |
Week 1 - 5 Recap Learning outcomes: L03, L04, L05 |
Tutorial |
Linear Regression (MSD) Learning outcomes: L03, L04, L05 |
|
Week 7 |
Lecture |
Introduction to R Markdown Learning outcomes: L01, L02, L05 |
Tutorial |
Project 1 Tutorial Workshop Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 8 |
Lecture |
Model Validation Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L02, L03, L04 |
Tutorial |
"Using R Markdown (for Project 2)" Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L04 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Lecture |
Dealing with Missing Data Learning outcomes: L02, L03, L04, L05 |
Tutorial |
Model Validation (MSD) Learning outcomes: L03, L04, L05 |
|
Week 10 |
Lecture |
Using Models for Prediction Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L03, L04, L05 |
Tutorial |
Dealing with Missing Data (MSD) Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L03, L04, L05 |
|
Week 11 |
Lecture |
Week 7 - 10 Recap Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Project 2 Tutorial Workshop Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 |
Lecture |
Project 2 Lecture Workshop Learning outcomes: L03, L04, L05 |
Tutorial |
Project 2 Tutorial Workshop Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 13 |
Lecture |
Course Review Learning outcomes: L01, L05 |
Tutorial |
Course Review Learning outcomes: L01, L05 |
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.