Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
An important part of a business analysts' role is the appropriate and accurate visualisation of insights from analytics activities. This course provides a theoretical basis on which such visualisations are developed, and develops students' data representations skills, while also developing skills with tools and commercial powerBI, Tableau and QlikView.
This course focuses on the theories, methods, and technical tools that enable analysis, communication, and decision-making with data visualisations. It covers a range of topics from multiple disciplines such as perceptual psychology, data science, and human-computer interaction.ᅠ
The course is divided into 7 modules:
- Introduction to Visual Analytics
- Theories of Visualisation
- Design
- Evaluation
- Application cases
- Presentation
- Trends in Visualisation
This course offers a fully online student experience that engages students through interactive content built into a learning platform. The content engagement is further enhanced with live sessions, coding activities, and discussions on a social platform. Every week has a dedicated live session to dive deeper into a topic, with further examples and activities to practise the several activities in visualisation design and visual analytics.
The course takes an individual, project-based approach to understanding and practising the steps involved in visual analytics: defining a data problem, performing (visual) exploratory data exploration, designing a visualisation, bringing interactivity, and evaluating a data visualisation. Students will use tools such as Tableau and ggplot2 to explore and design (interactive) data visualisations.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BSAN7205 or DATA7001
Restrictions
MBusAn, MDataSc
Course contact
Course staff
Lecturer
Tutor
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
BSAN7208 Visual Analytics is designed to introduce students to data visualisation and analytics techniques, theory and practice. The course focuses on visualisation design, interaction and evaluation for various type of data to support evidence-based decision making.
Students will obtain practical skills to build efficient visualisation for data exploration andᅠ communication. The course uses various examples from business and other areas to highlight the crucial role of visualisation in analysing and presenting evidence to stakeholders.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand and apply the fundamental theories of data visualisations.
LO2.
Create efficient and appropriate visualisations to generate insights that support decision making utilising various technologies.
LO3.
Explore and critically evaluate data visualisations to be able to communicate key insights to various stakeholders.
Assessment
Assessment summary
Category | Assessment task | Weight | Due date |
---|---|---|---|
Paper/ Report/ Annotation | A1 - Problem Statement | 20% |
23/08/2024 4:00 pm |
Paper/ Report/ Annotation, Product/ Design | A2 - Interactive Prototype | 50% |
11/10/2024 4:00 pm |
Presentation | Video presentation | 30% |
25/10/2024 4:00 pm |
Assessment details
A1 - Problem Statement
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
23/08/2024 4:00 pm
- Learning outcomes
- L01
Task description
This assessment task is an individual report that draws upon several data sets in a specific business context to support one or more of the United Nations Sustainable Development Goals (see https://sdgs.un.org/goals). In that context, the report is to present an investment recommendation supported by data analysis and data visualisation techniques. The assignment requires that the student use at least three public data sets (see https://unstats-undesa.opendata.arcgis.com/#catalog), taken together as a whole, related to the identified business context. These three or more data sets are then analysed in support of the development of an investment recommendation that addresses one or more SDGs. You should write the report from the perspective of a business or a government entity considering such an investment.
The essay is to present an analysis and interpretation of the data using at least two different forms of data visualisation as identified in lectures. The essay is to provide a broad overview of the context of the problem, identify the sources of the data sets and their relevance to the analysis, present the analysis and interpret the results, and finally link the analysis to key supporting arguments for the investment recommendation made. You should state any assumptions you make about your analysis or recommendation.
Key to success with this assessment task is succinct analysis and strong links between the data visualisation and the key supporting arguments for the recommendation. The discussion is to be supported by citing research that supports the context, analysis, interpretation, and key arguments made in support of the recommendation.
REQUIREMENTS
Scope
This assessment task is an individual report that draws upon several data sets. Note that in this context, a data set is simply a ‘table’ of data. It might be a listing of invoices, or employees, or of visitor numbers. Students using a nominated open data set should use at least one other open data set not provided. The report is to present a management recommendation (or recommendations) supported by data analysis and data visualisation techniques.
The assignment requires that the student acquire at least three data sets. These data sets, taken together as a whole, relate to the identified context and the associated SDG related problem. Therefore, at least two data sets are to be analysed in support of the development of a management recommendation.
The report is to provide a broad overview of the context of the business, identify the sources of the data sets and their relevance to the analysis, present the analysis and interpret the results, and link the analysis to key supporting arguments for the recommendation made. The analysis is to support the development of a management recommendation for a course of action, in line with an evidence-based management culture.
The report is to present an analysis and interpretation of the selected data using at least three different types of data visualisation. Furthermore, at least two data sets should be evident in two different types of data visualisations presented. For example, a geo-spatial representation might have two data sets represented in it and a trend line might be used to relate two data sets to each other over time.
As part of this requirement, the report should refer to at least three (3) academic, quality, peer reviewed research papers that support the issues, analysis and recommendations outlined in the report.
The report should identify the sources of the data used.
Generative 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
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.
A2 - Interactive Prototype
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation, Product/ Design
- Weight
- 50%
- Due date
11/10/2024 4:00 pm
- Learning outcomes
- L02
Task description
In this assessment, you will create an interactive visualisation prototype. It will involve the design and implementation of a set of interactive visualisations and provide a critical evaluation.
The aims of this assignment are:
- to design a set of interactive visualisations to allow for effective visual data exploration and sense making,
- to provide a critical evaluation of your design
In Assignment 1, you created a set of visualisations that provide initial insight about the data and helped answer some of the questions that you are exploring.
Part 1: Visualisation and interaction design
In the first phase of this section, you will reproduce at least two of the first assignment visualisations, and at least one new visualisation, using the ggplot2 library in R. The aim is to demonstrate your understanding of the principles of the Grammar of Graphics to reproduce a visualisation idiom and create a new visualisation based on requirements. The two visualisations that you must reproduce can slightly differ from the ones you created with Tableau, but the resulting visualisation should be as close as possible to the original ones.
For the new visualisation(s) that you will create with ggplot2, you need to:
- Describe your use of ggplot2’s grammar of graphics (in terms of data, layer, scale, coordinate and facets)
- Explain how your design choices allow for efficient visualisation of the data you want to analyse (refer to guidelines and principles from Munzner, Bertin, Tufte etc…)
In the second phase of this section, you will bring interactivity to your visualisations. Your interactive visualisations should demonstrate at least three interactions (see Heer and Shneiderman’s taxonomy) and show how they help data exploration and sense-making in the report. You are encouraged to use the interactive libraries covered in Module 3.4.
In the third and last phase of this section, you will provide a short evaluation of your design. This evaluation should address issues such as quality of encodings, efficient use of data/ink ratio, and other considerations covered in Module 4.
Part 2: Report
Students will prepare a major report (maximum 8 pages) based on their design and evaluation of their data visualisations. The report is free form but must contain:
The ggplot2 code of the reproduced visualisations and the new visualisation(s) (Phase 1)
Which interactions were implemented and how they help data exploration and sensemaking (Phase 2)
A summary of your data visualisation evaluation (Phase 3)
Finalised visualisations that justify the final investment recommendations.
Generative 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
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.
Video presentation
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 30%
- Due date
25/10/2024 4:00 pm
- Learning outcomes
- L03
Task description
Each student is asked to prepare and record a presentation of 15 minutes summarising their work and demonstrating key points using the interactivity they built into their visualisations.
Students need to demonstrate the challenges they had in creating their visualisations and how they addressed them.
Students should also describe any limitations of their visualisations and analysis. Any future directions that would be needed to better analyse or communicate the data should also be described.
The key success factors in the presentation are communication (how well the key points of the investment were summarised for its stakeholders), issues identification, any assumptions made in identifying issues, and presentation style.
Generative 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
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 (22 Jul - 28 Jul) |
Not Timetabled |
Introduction to Visual Analytics Self-Directed Learning: This module introduces the general concepts involved in visual analytics. Learning outcomes: L01 |
Lecture |
Live session Live welcome session and course introduction. Learning outcomes: L01 |
|
Week 2 (29 Jul - 04 Aug) |
Not Timetabled |
Theories of Visualisation Self-Directed Learning: This module delves into theories of visualisation to explain how visualisation are effective at communicating data to the human perceptual system. Learning outcomes: L01 |
Lecture |
Live session In this live session, we will walk through some examples from Tufte and Few seeking feedback on the images, their flaws, the causes of problems. Learning outcomes: L01 |
|
Week 3 (05 Aug - 11 Aug) |
Not Timetabled |
Design process Self-Directed Learning: In this module we compare top-down design thinking for producing visualisations with bottom-up data exploration. Learning outcomes: L01 |
Lecture |
Live session In this week's live session, we will discuss the practice of data visualisation for exploration, how to identify various properties of the data, and how to select appropriate exploratory visualisations. We will use Tableau to construct some visualisations. Learning outcomes: L01, L02 |
|
Week 4 (12 Aug - 18 Aug) |
Not Timetabled |
Data types and visualisation idioms Self-Directed Learning: This module will explore what application domain the visualisation needs to model, the tasks the users might wish to perform, the types of encoding and interaction idioms needed, and the kind of algorithmic processing that is required. Learning outcomes: L01, L02 |
Lecture |
Live session In this week's live session, we will discuss Munzner's framework to data visualisation design. We will apply Munzner's framework to construct visualisations with Tableau. Learning outcomes: L01, L02 |
|
Week 5 (19 Aug - 25 Aug) |
Not Timetabled |
The grammar of graphics Self-Directed Learning: This module introduces the concept of Grammar of Graphics, a framework to describe and produce data visualisations in a systematic way. Learning outcomes: L01, L02 |
Lecture |
Live session In the live session we will discuss the solution to the activities proposed in the tutorials, and we will explore other sources of ggplot templates to create different visualisation types. Learning outcomes: L01, L02 |
|
Week 6 (26 Aug - 01 Sep) |
Not Timetabled |
The importance of interaction This week, we will explore the interaction models involved in data visualisation, the essential interactions to support the iterative process of visual data exploration, analysis, and presentation and how they are implemented in modern visualisation tools. Finally, we will learn how to implement interactivity and turn your ggplot2 visualisation into fully interactive visualisations. Learning outcomes: L01, L02 |
Lecture |
Live session This week in the live session, we will explore some exemplar interactive visualisations and will demonstrate how the interactions support sense making and understanding. Learning outcomes: L02 |
|
Week 7 (02 Sep - 08 Sep) |
Tutorial |
R Tutorial R tutorial |
Week 8 (09 Sep - 15 Sep) |
Not Timetabled |
Evaluation his module covers: - Tamara Munzner s framework used as an evaluation framework - the principles of Graphical Integrity and Graphical Excellent from Edward Tufte to help evaluate the graphical components of a data visualisation, - the rules of Thumb of Tamara Munzner and Stephen Few s criteria to evaluate exploratory data visualisations from a software perspective. Learning outcomes: L01, L03 |
Lecture |
Live session In this live session we will briefly discuss the paradox of baseline vs lie factor, and the redesign of the vote map by the ABC. Learning outcomes: L01, L03 |
|
Week 9 (16 Sep - 22 Sep) |
Not Timetabled |
Practical Examples This module consolidates what we have learned about producing good visualisations by demonstrating critiques of data visualisations Learning outcomes: L01, L03 |
Lecture |
Live session This week we will develop a critique of a visualisation together following the same approach presented in this module. Learning outcomes: L01, L03 |
|
Week 10 (30 Sep - 06 Oct) |
Not Timetabled |
From data to vis to insight Data-driven stories help decision-makers to let data overrule their initial judgments. We begin by describing advanced uses of signals in our evidence displays. Then we explore the elements of data-driven stories, and how animation can contribute or detract from their content. The content in this module helps a designer to develop visualisations that support evidence-based decision-making. Learning outcomes: L01, L02, L03 |
Lecture |
Live session This week we will explore examples of visual narratives in detail to understand their visual components and how they convey their messages. We will also examine examples of narrative structures using Cohn s schema to help identify their narrative categories and constituents. Learning outcomes: L01 |
|
Week 11 (07 Oct - 13 Oct) |
Tutorial |
R Tutorial R tutorial |
Week 12 (14 Oct - 20 Oct) |
Not Timetabled |
Trends and future direction in Visual Analytics This module explores trends and future directions in data visualisation and visual analytics. Learning outcomes: L01 |
Lecture |
Live session This week we will discuss what are the low-cost options to turn your visualisations into immersive experiences. Learning outcomes: L01, L02 |
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.