Course coordinator
Dr Zara Taba
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
The R environment provides data scientists with powerful methods for data visualisation that aid in communicating and implementing the knowledge gained from business analytics. This course provides students of business analytics with an overview of the types of data visualisation methods available in R. A range of topics are introduced including basic plots, higher-dimensional plots, visualising text, and creating applications in R.
Businesses today are awash in data and are increasingly looking for ways to leverage their data to improve their decision making, business processes, strategies, and financial performance. Data visualisation can play a crucial role in helping businesses to leverage the value of their data to achieve the outcomes they desire. For example, the visualisation of data can help business analysts to see patterns in data that are not discernible from directly interrogating the raw data and sometimes easily identified by analyses that aggregate and summarise broad patterns in the data. The visualisation of data allows for potentially large amounts of data to be presented in compact forms and in forms that analysts and other users can readily process, understand, and communicate to others. Acting on the results and making business decisions on the basis of an analysis often requires clear communication and shared understandings. For these reasons, data visualisation can be key to leveraging the power of "big data" to improve business decisions, processes, and strategies. This course provides an overview of the methods of data visualisation and exposure to the technologies and software used for data visualisation in business.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM2204 or BSAN2204
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
BISM2204 or BSAN2204
Incompatible
You can't enrol in this course if you've already completed the following:
BISM3204
Course contact
Course staff
Lecturer
Dr Zara Taba
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 broad aim of this course is to provide students with an introduction to data visualisation for business. Students should develop an appreciation of the value of data visualisation in identifying patterns in business data and in helping communication. Further, students should see the link between effective communication of analytics and management action. The course provides specific instruction on the methods of data visualisation and the key technologies, including the use of specialised computer software and many of the techniques of data visualisation.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Recognise and explain the role of data visualisation for business,
LO2.
Explain the key concepts in data visualisation for business,
LO3.
Apply the methods of data visualisation to business,
LO4.
Compare and critically evaluate the methods of data visualisation, and
LO5.
Demonstrate how data visualisation can inform and improve business performance.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz | Data Visualisation Quiz Assignment- part 1 | 10% |
21/03/2025 5:00 pm |
| Paper/ Report/ Annotation | Data Visualisation - Project Proposal | 20% |
28/03/2025 5:00 pm |
| Quiz | Data Visualisation Quiz Assignment - part 2 | 10% |
9/05/2025 5:00 pm |
| Paper/ Report/ Annotation | Data Visualisation - Project Report | 60% |
26/05/2025 5:00 pm |
Assessment details
Data Visualisation Quiz Assignment- part 1
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
21/03/2025 5:00 pm
- Learning outcomes
- L03
Task description
Witten responses to questions including R codes and results. Witten responses to questions including R codes and results.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
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.
Data Visualisation - Project Proposal
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
28/03/2025 5:00 pm
- Learning outcomes
- L01, L02, L05
Task description
This written paper is on the topic of data visualisation for business addressing the needs of different stakeholders. More details will be provided on Blackboard and discussed in class.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
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.
Data Visualisation Quiz Assignment - part 2
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
9/05/2025 5:00 pm
- Learning outcomes
- L03
Task description
Details will be provided on Blackboard and discussed in class.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
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.
Data Visualisation - Project Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
26/05/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
More details will be discussed during class and provided on Blackboard.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
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.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Nussbaumer Knaflic, Cole (2015), Storytelling with Data: A Visualization Guide for Business Professionals. John Wiley & Sons, Inc. | ||
| Wickham, Hadley (2016), ggplot2: Elegant Graphics for Data Analysis (2nd Edition). Springer. |
Recommended
| Item | Description | Further Requirement |
|---|---|---|
| An Introduction to R for Spatial Analysis & Mapping, Brunsdon and Comber (2019) | ||
| Chang, Winston (2019), R Graphics Cookbook: Practical Recipes for Visualizing Data (2nd Edition). O'Reilly Media, Inc. |
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 |
Course Introduction and assignments Briefing Learning outcomes: L01 |
Week 2 |
Seminar |
Principles of Data Visualisation Learning outcomes: L02, L03 |
Week 3 |
Seminar |
The Visualisation Context Learning outcomes: L02, L03 |
Week 4 |
Seminar |
Designing Visualisations for Impact Learning outcomes: L02, L03 |
Week 5 |
Seminar |
Focusing Audience Attention Learning outcomes: L02, L03 |
Week 6 |
Seminar |
Developing a Narrative and A2 Briefing Learning outcomes: L02, L03 |
Week 7 |
Seminar |
Visualisation Case Studies Learning outcomes: L01, L02, L03, L05 |
Week 8 |
Seminar |
Geographical Information Systems Learning outcomes: L01, L02, L03, L04, L05 |
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Seminar |
Infographics Learning outcomes: L02, L03, L04, L05 |
Week 10 |
Seminar |
Dashboards Learning outcomes: L02, L03, L05 |
Week 11 |
Seminar |
Shiny Learning outcomes: L02, L03 |
Week 12 |
Seminar |
Workshop Learning outcomes: L02, L03, L04, L05 |
Week 13 |
Seminar |
Revision Learning outcomes: L01, L02, L03, L04, 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.