MXB362 Advanced Visualisation and Data Science

Unit Outline: Semester 2 2025, Gardens Point, Internal

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:

1. Demonstrate knowledge of the major issues and directions in contemporary data visualisation
2. Produce high quality visualisation outputs such as images, animations and/or 3D models.
3. Critically evaluate and apply advanced visualisation techniques and tools in a range of contexts.
4. Research, select and discuss appropriate visualisation techniques for one or more scientific data sets.
5. Communicate (in oral and written form) and reflect on case study outcomes in an audience appropriate manner.

Content

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

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

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.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Resources

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.