DSB300 Data Science Capstone Project
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: | DSB300 |
|---|---|
| Prerequisite(s): | (DSB200 and 192cps of study) or (MXB344 or CAB420) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,704 |
| International unit fee | $5,640 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | DSB300 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (DSB200 and 192cps of study) or (MXB344 or CAB420) |
| Coordinator: | Paul Wu | p.wu@qut.edu.au |
Overview
The project addresses a research question or a practical problem through the application of Data Science theories, tools and techniques. It allows you to apply the knowledge of the research skills and practices used to undertake specific Data Science activities. It provides an opportunity to individualise your studies by concentrating on a specific problem domain.
Learning Outcomes
On successful completion of this unit you will be able to:
- Integrate and apply advanced knowledge of data science theory, methods and principles to understand and address industry-relevant issues and problems.
- Demonstrate and apply advanced problem solving, analysis, design and visualisation skills to a Data Science industry project to achieve stakeholder goals.
- Collaborate in a team to professionally and ethically project manage and conduct a data science industry-based project using industry standard tools, techniques and methodologies.
- Communicate professionally with a client demonstrating advanced skills in written, oral and visual presentation of technical content to specialist audiences.
- Critically reflect on industry feedback, self and team performance to identify strategies for further developing professional practice skills.
Content
You must develop and carry out a data science project. The project topic is decided by agreement between your group and your QUT supervisor. Projects relevant to your current or intended employment are encouraged.
Learning Approaches
This unit utilises a blended learning methodology, including online and face-to-face components. The two cornerstones of work integrated learning (WIL) at QUT, i.e., industry engagement and assessed authentic tasks are present in this unit.
Significant learning time will be devoted to working in your groups on formulating and implementing your solution to the industry problem. These solutions will draw upon your learnings from throughout your degree, including generative AI tools. Your activities will include communication, evaluation of information and complex problem solving, which will help you to work effectively within groups with consideration for interpersonal differences. The embedded support for project management in this unit will also further your career development and employability.
Feedback on Learning and Assessment
You can obtain feedback on your project plan, and oral presentation and reports from your supervisor and the academics who mark your works.
Assessment
Overview
All projects will be assessed according to the same set of assessment items specified below. You are expected to develop a written project proposal in week 3. You must produce a project management plan, project report and make an oral presentation, and write a final reflection.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Portfolio
Made up of two parts. One is a written project proposal (due week 3) providing a description of the industry problem and context, data if relevant, objectives, deliverables and methodology for the project. The second (due week 13) comprises a reflection on professional development (including roles, industry engagement, challenges and lessons learned) and technical reflection (including critical assessment, response to queries, and future steps). Students will practice using GenAI to help them learn about their industry problem domain and reflect upon this in comparison to traditional sources of information.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Professional Plans
Written project plan providing a description of background, scope, objectives, work breakdown milestones and deliverables.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Written project report providing a detailed description of the project and its outcomes. Oral presentation of outcomes to industry and academics and peers.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
The project report is eligible for the 48-hour late submission period and assignment extensions, but the oral presentation is not.
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 related to this unit will be made available in the Canvas site.There are no additional resources you are required to purchase.
Risk Assessment Statement
All commencing FoS students are required to complete the Mandatory Safety Induction
There are no extraordinary 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, Project (applied) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Portfolio, Professional Plans, Project (applied) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Portfolio, Project (applied) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Professional Plans, Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Professional Plans, Project (applied) - 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: Professional Plans, Project (applied) - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Portfolio
Unit Outline: Semester 2 2026, Online
| Unit code: | DSB300 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (DSB200 and 192cps of study) or (MXB344 or CAB420) |
Overview
The project addresses a research question or a practical problem through the application of Data Science theories, tools and techniques. It allows you to apply the knowledge of the research skills and practices used to undertake specific Data Science activities. It provides an opportunity to individualise your studies by concentrating on a specific problem domain.
Learning Outcomes
On successful completion of this unit you will be able to:
- Integrate and apply advanced knowledge of data science theory, methods and principles to understand and address industry-relevant issues and problems.
- Demonstrate and apply advanced problem solving, analysis, design and visualisation skills to a Data Science industry project to achieve stakeholder goals.
- Collaborate in a team to professionally and ethically project manage and conduct a data science industry-based project using industry standard tools, techniques and methodologies.
- Communicate professionally with a client demonstrating advanced skills in written, oral and visual presentation of technical content to specialist audiences.
- Critically reflect on industry feedback, self and team performance to identify strategies for further developing professional practice skills.
Content
You must develop and carry out a data science project. The project topic is decided by agreement between your group and your QUT supervisor. Projects relevant to your current or intended employment are encouraged.
Learning Approaches
This unit utilises a blended learning methodology, including online and face-to-face components. The two cornerstones of work integrated learning (WIL) at QUT, i.e., industry engagement and assessed authentic tasks are present in this unit.
Significant learning time will be devoted to working in your groups on formulating and implementing your solution to the industry problem. These solutions will draw upon your learnings from throughout your degree, including generative AI tools. Your activities will include communication, evaluation of information and complex problem solving, which will help you to work effectively within groups with consideration for interpersonal differences. The embedded support for project management in this unit will also further your career development and employability.
Feedback on Learning and Assessment
You can obtain feedback on your project plan, and oral presentation and reports from your supervisor and the academics who mark your works.
Assessment
Overview
All projects will be assessed according to the same set of assessment items specified below. You are expected to develop a written project proposal in week 3. You must produce a project management plan, project report and make an oral presentation, and write a final reflection.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Portfolio
Made up of two parts. One is a written project proposal (due week 3) providing a description of the industry problem and context, data if relevant, objectives, deliverables and methodology for the project. The second (due week 13) comprises a reflection on professional development (including roles, industry engagement, challenges and lessons learned) and technical reflection (including critical assessment, response to queries, and future steps). Students will practice using GenAI to help them learn about their industry problem domain and reflect upon this in comparison to traditional sources of information.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Professional Plans
Written project plan providing a description of background, scope, objectives, work breakdown milestones and deliverables.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Written project report providing a detailed description of the project and its outcomes. Oral presentation of outcomes to industry and academics and peers.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
The project report is eligible for the 48-hour late submission period and assignment extensions, but the oral presentation is not.
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 related to this unit will be made available in the Canvas site.There are no additional resources you are required to purchase.
Risk Assessment Statement
All commencing FoS students are required to complete the Mandatory Safety Induction
There are no extraordinary 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, Project (applied) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Portfolio, Professional Plans, Project (applied) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Portfolio, Project (applied) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Professional Plans, Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Professional Plans, Project (applied) - 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: Professional Plans, Project (applied) - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Portfolio