DSB101 Introduction to Data Science and Visualisation
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: | DSB101 |
|---|---|
| Equivalent(s): | MXB262 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | DSB101 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXB262 |
| 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 analyse and visualise data is critical for exploring and communicating science and engineering findings. Modern data science and visualisation techniques allow us to efficiently explore and communicate data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Manipulate data sets using numerical techniques and produce faithful representations of scientific data using computational approaches
- Discuss the importance of data science and visualisation in a scientific contexts including approaches to visualise Aboriginal and Torres Strait Islander traditional knowledge.
- Explain the principles of data science and visualisation methods and techniques, along with the central elements of the visualisation process
- Create technical data visualisation based on effective visualisation communication theory and current best practices.
- Critically analyse data problems and fully justify visualisation and analysis decisions.
Content
This unit introduces data science and 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 science and 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 and analysing to visualising and communicating data. We focus on effective visual communication and high-quality, fit-for-purpose representations of 2D, multi-dimensional, network, and spatial data.
Learning Approaches
This unit engages you in your learning through a theory-to-practice approach. As a first-year unit your learning is carefully scaffolded with the delivery of topic content being followed by practical exercises to build your new expertise in data science and scientific visualisation.
This unit involves programming in R, but no 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. You are also reminded of the FREE peer tutoring support available through the Student Success Group.
Feedback on Learning and Assessment
You will receive feedback via comments to the entire cohort, as well as individual feedback during attendance at the workshops. In addition, there will be written feedback on the problem-solving tasks and projects.
Assessment
Overview
This unit is assessed through creation of a data science portfolio, an applied project, and a final exam. Assessment will follow from and build upon the practical and theoretical content from videos, lectures, and hands-on workshops. This assessment program is designed to give students a preview of the work data scientists and data communicators do in real jobs.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Portfolio
Students will accumulate a portfolio of case study tasks attempted during and after workshops, building upon the practical learnings in class. These will combine analysis with practical creation of novel visualisations in R, knowledge and understanding of theory, and critique of your own and existing data products.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Final project
Students will analyse a dataset of their choosing, create novel data visualisations, and prepare a written project to explain their motivations, justifications, and theories behind the practical application of data science and visualisation learned throughout the unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Final exam
Final exam to assess data science theory. During central examination period. Individual assessment.
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
All learning materials required for this unit will be made available via the unit Canvas site. You are not expected to purchase any software for this unit. The required software is either installed in the computer labs and/or freely available
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Portfolio, Final project, Final exam - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Final project, Final exam - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Portfolio, Final exam - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Portfolio, Final project - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Final exam
Unit Outline: Semester 1 2026, Online
| Unit code: | DSB101 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXB262 |
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 analyse and visualise data is critical for exploring and communicating science and engineering findings. Modern data science and visualisation techniques allow us to efficiently explore and communicate data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Manipulate data sets using numerical techniques and produce faithful representations of scientific data using computational approaches
- Discuss the importance of data science and visualisation in a scientific contexts including approaches to visualise Aboriginal and Torres Strait Islander traditional knowledge.
- Explain the principles of data science and visualisation methods and techniques, along with the central elements of the visualisation process
- Create technical data visualisation based on effective visualisation communication theory and current best practices.
- Critically analyse data problems and fully justify visualisation and analysis decisions.
Content
This unit introduces data science and 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 science and 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 and analysing to visualising and communicating data. We focus on effective visual communication and high-quality, fit-for-purpose representations of 2D, multi-dimensional, network, and spatial data.
Learning Approaches
This unit engages you in your learning through a theory-to-practice approach. As a first-year unit your learning is carefully scaffolded with the delivery of topic content being followed by practical exercises to build your new expertise in data science and scientific visualisation.
This unit involves programming in R, but no 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. You are also reminded of the FREE peer tutoring support available through the Student Success Group.
Feedback on Learning and Assessment
You will receive feedback via comments to the entire cohort, as well as individual feedback during attendance at the workshops. In addition, there will be written feedback on the problem-solving tasks and projects.
Assessment
Overview
This unit is assessed through creation of a data science portfolio, an applied project, and a final exam. Assessment will follow from and build upon the practical and theoretical content from videos, lectures, and hands-on workshops. This assessment program is designed to give students a preview of the work data scientists and data communicators do in real jobs.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Portfolio
Students will accumulate a portfolio of case study tasks attempted during and after workshops, building upon the practical learnings in class. These will combine analysis with practical creation of novel visualisations in R, knowledge and understanding of theory, and critique of your own and existing data products.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Final project
Students will analyse a dataset of their choosing, create novel data visualisations, and prepare a written project to explain their motivations, justifications, and theories behind the practical application of data science and visualisation learned throughout the unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Final exam
Final exam to assess data science theory. During central examination period. Individual assessment.
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
All learning materials required for this unit will be made available via the unit Canvas site. You are not expected to purchase any software for this unit. The required software is either installed in the computer labs and/or freely available
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Portfolio, Final project, Final exam - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Final project, Final exam - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Portfolio, Final exam - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Portfolio, Final project - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Final exam