MXB262 Visualising Data
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB262 |
|---|---|
| Prerequisite(s): | SEB113 or MXB107 or MXB161 or MXB261 or MZB126 |
| Equivalent(s): | MAB481 |
| Assumed Knowledge: | Programming skills are assumed knowledge |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $555 |
| Domestic tuition unit fee | $3,324 |
| International unit fee | $4,296 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | MXB262 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | SEB113 or MXB107 or MXB161 or MXB261 or MZB126 |
| Equivalent: | MAB481 |
| Assumed Knowledge: | Some programming skills are assumed knowledge, although this is not critical for success in the unit as skills will be developed. Programming component of any prerequisite units is sufficient. |
| Coordinator: | Kate Helmstedt | kate.helmstedt@qut.edu.au |
Overview
Our world has an unprecedented amount of available data - especially in STEM, where generating and working with data is core to our fields. The ability to visualise data is critical for exploring and communicating science and engineering findings. Modern visualisation theory and techniques allow us to efficiently explore and communicate with data.
This unit introduces data visualisation concepts, theories, and techniques, along with practical experience exploring and dynamically visualising complex data. You will develop an understanding of the fundamental concepts in data visualisation through practical, real-world examples in contexts such as the environment, agriculture, industry, engineering, and healthcare. You will follow the visualisation pipeline from importing, to visualising, to communicating data. We focus on effective visual communication and high-quality, fit-for-purpose representations of 2D, multi-dimensional, network, and spatial data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Appreciate the need for data visualisation in a scientific context, including an understanding of its development and history
- Convey the basics of data visualisation methods and techniques, along with the central elements of the visualisation process
- Demonstrate an understanding of a wide range of techniques that can be used to visualise multi-dimensional data
- Manipulate data sets using numerical techniques and produce faithful representations of scientific data using computational approaches
- Have working knowledge of the best-practices for effective visual communication and technical data visualisation.
Content
This unit is composed of six topics:
- Introduction to data visualisation
- Visualising 2D and higher dimensional data
- Using networks for visualisation
- Visualising spatial data through various mapping techniques
- Effective choices for visualising complex data
- Applied case studies
Each topic will consist of:
- Theory -- why do people make the visualisation choices they make, and should we do the same?
- Practice -- learning to practically apply these visualisation techniques in the programming language R, using cutting edge tools and exploring a wide range of datasets
- Exploration -- you will find data, design messaging, and implement these visualisation techniques to make novel visualisations to communicate your messages
At the end of this unit, you will have developed a portfolio of visualisations that you have designed, ranging from simple to complex. These will illustrate your skills and expertise in data visualisation.
Learning Approaches
This unit engages you in your learning through a theory-to-practice approach. The delivery of topic content is followed by practical exercises to build expertise in scientific visualisation.
This unit involves programming in R, but very little prior programming experience is required. The programming components are designed to build slowly in complexity over the semester, so this unit offers an opportunity to build skills in the syntax and logical flow of programming languages.
This unit is available for you to study in either on-campus or online mode. You will be provided with learning resources including pre-recorded videos and readings that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts you will then apply in problem solving tasks, workshops, and projects. The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed.
Feedback on Learning and Assessment
You will receive feedback via generic comments to the cohort, as well as individual feedback during attendance at the computer workshop. In addition, there will be written feedback on the problem-solving tasks and projects.
Assessment
Overview
This unit is assessed through problem-solving tasks and two projects. Assessment is based on each topic, and assesses the technical ability to create novel visualisations from data, and an understanding and explanation of the theory driving visualisation choices.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will accumulate a portfolio of problem-solving tasks attempted during and after workshops. These will combine practical creation of novel visualisations in R, knowledge and understanding of theory, and critique of your own and existing visualisations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Case studies (written, individual)
Presentation of case studies
Students will present a short report critically assessing and evaluating case studies of existing data visualisations according to the theory learned in weeks 1-6.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Visualisation project (written, individual)
Presentation of visualisation project
Students will create novel data visualisations and prepare a written project to explain their motivations, justifications, and theories behind the practical application of data visualisation learned throughout the unit.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
The required software is either installed in the computer labs and/or freely available.
Students are not expected to purchase any software or other resources for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Unit Outline: Semester 1 2024, Online
| Unit code: | MXB262 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | SEB113 or MXB107 or MXB161 or MXB261 or MZB126 |
| Equivalent: | MAB481 |
| Assumed Knowledge: | Some programming skills are assumed knowledge, although this is not critical for success in the unit as skills will be developed. Programming component of any prerequisite units is sufficient. |
Overview
Our world has an unprecedented amount of available data - especially in STEM, where generating and working with data is core to our fields. The ability to visualise data is critical for exploring and communicating science and engineering findings. Modern visualisation theory and techniques allow us to efficiently explore and communicate with data.
This unit introduces data visualisation concepts, theories, and techniques, along with practical experience exploring and dynamically visualising complex data. You will develop an understanding of the fundamental concepts in data visualisation through practical, real-world examples in contexts such as the environment, agriculture, industry, engineering, and healthcare. You will follow the visualisation pipeline from importing, to visualising, to communicating data. We focus on effective visual communication and high-quality, fit-for-purpose representations of 2D, multi-dimensional, network, and spatial data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Appreciate the need for data visualisation in a scientific context, including an understanding of its development and history
- Convey the basics of data visualisation methods and techniques, along with the central elements of the visualisation process
- Demonstrate an understanding of a wide range of techniques that can be used to visualise multi-dimensional data
- Manipulate data sets using numerical techniques and produce faithful representations of scientific data using computational approaches
- Have working knowledge of the best-practices for effective visual communication and technical data visualisation.
Content
This unit is composed of six topics:
- Introduction to data visualisation
- Visualising 2D and higher dimensional data
- Using networks for visualisation
- Visualising spatial data through various mapping techniques
- Effective choices for visualising complex data
- Applied case studies
Each topic will consist of:
- Theory -- why do people make the visualisation choices they make, and should we do the same?
- Practice -- learning to practically apply these visualisation techniques in the programming language R, using cutting edge tools and exploring a wide range of datasets
- Exploration -- you will find data, design messaging, and implement these visualisation techniques to make novel visualisations to communicate your messages
At the end of this unit, you will have developed a portfolio of visualisations that you have designed, ranging from simple to complex. These will illustrate your skills and expertise in data visualisation.
Learning Approaches
This unit engages you in your learning through a theory-to-practice approach. The delivery of topic content is followed by practical exercises to build expertise in scientific visualisation.
This unit involves programming in R, but very little prior programming experience is required. The programming components are designed to build slowly in complexity over the semester, so this unit offers an opportunity to build skills in the syntax and logical flow of programming languages.
This unit is available for you to study in either on-campus or online mode. You will be provided with learning resources including pre-recorded videos and readings that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts you will then apply in problem solving tasks, workshops, and projects. The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed.
Feedback on Learning and Assessment
You will receive feedback via generic comments to the cohort, as well as individual feedback during attendance at the computer workshop. In addition, there will be written feedback on the problem-solving tasks and projects.
Assessment
Overview
This unit is assessed through problem-solving tasks and two projects. Assessment is based on each topic, and assesses the technical ability to create novel visualisations from data, and an understanding and explanation of the theory driving visualisation choices.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will accumulate a portfolio of problem-solving tasks attempted during and after workshops. These will combine practical creation of novel visualisations in R, knowledge and understanding of theory, and critique of your own and existing visualisations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Case studies (written, individual)
Presentation of case studies
Students will present a short report critically assessing and evaluating case studies of existing data visualisations according to the theory learned in weeks 1-6.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Visualisation project (written, individual)
Presentation of visualisation project
Students will create novel data visualisations and prepare a written project to explain their motivations, justifications, and theories behind the practical application of data visualisation learned throughout the unit.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
The required software is either installed in the computer labs and/or freely available.
Students are not expected to purchase any software or other resources for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.