MXB362 Advanced Visualisation and Data Science
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: | MXB362 |
|---|---|
| Prerequisite(s): | MXB262 |
| Equivalent(s): | MAB681 |
| 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 2 2024, Gardens Point, Internal
| Unit code: | MXB362 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB262 |
| Equivalent: | MAB681 |
| Coordinator: | Riley Whebell | r2.whebell@qut.edu.au |
Overview
Data visualisation is an essential element of modern computational and data science. It provides powerful tools for investigating, understanding, and communicating the large amounts of data that can be generated by computational simulations, scientific instruments, remote sensing, or the Internet of Things. The aim of this unit is to explore the issues, theories, and techniques of advanced data visualisation. This unit develops theoretical and practical understandings of the major directions and issues that confront the field. A selected number of advanced data visualisation techniques will be examined in detail through specific examples. The practicals will reinforce lecture content and extend your applied skills and knowledge in data visualisation, including specific methods. A focus of the unit is the development of real world data visualisation skills and experience, based on a major data visualisation case study.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the major issues and directions in contemporary data visualisation
- Produce high quality visualisation outputs such as images, animations and/or 3D models.
- Critically evaluate and apply advanced visualisation techniques and tools in a range of contexts.
- Research, select and discuss appropriate visualisation techniques for one or more scientific data sets.
- Communicate (in oral and written form) and reflect on case study outcomes in an audience appropriate manner.
Content
This unit is comprised of three modules, where the three modules together constitute the total credit points. In each of the modules, the following material is presented:
- Effective Visualisation
- Advanced Visualisation Tools
- Advanced Visualisation Techniques
Learning Approaches
This unit approaches learning through a theory-to-practice approach, with topic content delivery followed by practical exercises to build expertise and experience in scientific visualisation.
A blended learning approach is adopted and includes a two (2) hour lecture and a two (2) hour practical. The lectures will be used to convey theory, while the practicals will be utilised to demonstrate concepts and tools (e.g., advanced scientific data manipulation and visualisation techniques) and allow you to develop skills and knowledge through practical experience.
You will also conduct two visualisation case studies - an Effective Visualisation Case Study and a Major Visualisation Case Study. A scaffolded learning approach will be adopted to support students through significant points of the major case study (at proposal stage, progress report stage and final presentation and report stage). In the latter part of the semester, a learning based environment will be provided support student's completion of their major case studies. The learning based environment will replace the two (2) hour lecture.
Feedback on Learning and Assessment
Feedback in this unit will be provided through:
- formative exercises discussed in class
- whole class comments posted to QUT Canvas
- individual feedback based on criteria sheet descriptors
- individual feedback on formative and summative problem-solving tasks conducted during practicals.
Assessment
Overview
This unit is assessed through problem-solving tasks and case studies.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Presentation
Presentation of Case Studies
You will deliver two short presentations on your case studies: (1) Effective Visualisation Presentation (15%) and (2) Major Visualisation Presentation (15%).
Assessment: Report
Major Visualisation Case Study Reports
There will be three short reports associated with the Major Visualisation Case Study: (1) Proposal (10%) - due by week 5, (2) Progress Report (5%) - due by week 10, and (3) Final Report (40%) - due end of semester.
This is an assignment for the purposes of an extension.
Assessment: Problem Solving Task
Problem Solving Portfolio
You will accumulate a portfolio of problem solving tasks attempted during practicals.
This is an assignment for the purposes of an extension.
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.
You 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 2 2024, Online
| Unit code: | MXB362 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB262 |
| Equivalent: | MAB681 |
Overview
Data visualisation is an essential element of modern computational and data science. It provides powerful tools for investigating, understanding, and communicating the large amounts of data that can be generated by computational simulations, scientific instruments, remote sensing, or the Internet of Things. The aim of this unit is to explore the issues, theories, and techniques of advanced data visualisation. This unit develops theoretical and practical understandings of the major directions and issues that confront the field. A selected number of advanced data visualisation techniques will be examined in detail through specific examples. The practicals will reinforce lecture content and extend your applied skills and knowledge in data visualisation, including specific methods. A focus of the unit is the development of real world data visualisation skills and experience, based on a major data visualisation case study.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the major issues and directions in contemporary data visualisation
- Produce high quality visualisation outputs such as images, animations and/or 3D models.
- Critically evaluate and apply advanced visualisation techniques and tools in a range of contexts.
- Research, select and discuss appropriate visualisation techniques for one or more scientific data sets.
- Communicate (in oral and written form) and reflect on case study outcomes in an audience appropriate manner.
Content
This unit is comprised of three modules, where the three modules together constitute the total credit points. In each of the modules, the following material is presented:
- Effective Visualisation
- Advanced Visualisation Tools
- Advanced Visualisation Techniques
Learning Approaches
This unit approaches learning through a theory-to-practice approach, with topic content delivery followed by practical exercises to build expertise and experience in scientific visualisation.
A blended learning approach is adopted and includes a two (2) hour lecture and a two (2) hour practical. The lectures will be used to convey theory, while the practicals will be utilised to demonstrate concepts and tools (e.g., advanced scientific data manipulation and visualisation techniques) and allow you to develop skills and knowledge through practical experience.
You will also conduct two visualisation case studies - an Effective Visualisation Case Study and a Major Visualisation Case Study. A scaffolded learning approach will be adopted to support students through significant points of the major case study (at proposal stage, progress report stage and final presentation and report stage). In the latter part of the semester, a learning based environment will be provided support student's completion of their major case studies. The learning based environment will replace the two (2) hour lecture.
Feedback on Learning and Assessment
Feedback in this unit will be provided through:
- formative exercises discussed in class
- whole class comments posted to QUT Canvas
- individual feedback based on criteria sheet descriptors
- individual feedback on formative and summative problem-solving tasks conducted during practicals.
Assessment
Overview
This unit is assessed through problem-solving tasks and case studies.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Presentation
Presentation of Case Studies
You will deliver two short presentations on your case studies: (1) Effective Visualisation Presentation (15%) and (2) Major Visualisation Presentation (15%).
Assessment: Report
Major Visualisation Case Study Reports
There will be three short reports associated with the Major Visualisation Case Study: (1) Proposal (10%) - due by week 5, (2) Progress Report (5%) - due by week 10, and (3) Final Report (40%) - due end of semester.
This is an assignment for the purposes of an extension.
Assessment: Problem Solving Task
Problem Solving Portfolio
You will accumulate a portfolio of problem solving tasks attempted during practicals.
This is an assignment for the purposes of an extension.
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.
You 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.