Unit rationale, description and aim

 In an era where technology enables continuous innovation, computing professionals must be able to design, implement, and evaluate evidence-based projects that generate meaningful insights and drive positive change in organisations and communities. This unit serves as the second stage of a two-part capstone sequence, building on the work undertaken in Project A: Research Essentials. Both units are designed to foster problem-based, self-directed learning and applied research capability. 

In this unit, students apply the knowledge and skills developed throughout their studies to implement, evaluate, and communicate a research or industry-aligned project. They will employ a combination of theoretical, analytical, and computational methods to develop and test models, analyse data, and interpret findings in response to their defined research objectives or hypotheses. 

The unit culminates in the submission of a comprehensive project report and presentation, demonstrating the student’s ability to deliver a technically sound, ethically responsible, and professionally communicated solution. 

The aim of this unit is to enable students to implement, evaluate, and communicate an advanced research or applied computing project, integrating technical expertise, analytical reasoning, and professional practice. 

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

DTSC641 Data Science Project A Research Essentials

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Apply advanced analytical, computational, or desig...

Learning Outcome 01

Apply advanced analytical, computational, or design techniques to implement the proposed research or technical solution, demonstrating autonomy, initiative, and professional integrity.
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11

Critically evaluate project outcomes using appropr...

Learning Outcome 02

Critically evaluate project outcomes using appropriate metrics, validation methods, or performance benchmarks, and assess the reliability, limitations, and ethical implications of findings.
Relevant Graduate Capabilities: GC1, GC2, GC8

Synthesise and interpret results to generate evide...

Learning Outcome 03

Synthesise and interpret results to generate evidence-based insights that contribute to disciplinary knowledge or professional practice within computing.
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC11, GC12

Communicate the project outcomes and their broader...

Learning Outcome 04

Communicate the project outcomes and their broader significance through a comprehensive written report and professional presentation tailored to academic and industry audiences.
Relevant Graduate Capabilities: GC1, GC2, GC3, GC7, GC9, GC10, GC11, GC12

Content

Topics will include:

  • Data modelling and analysis
  • Model Evaluation and interpretation of Results
  • Deployment and Communication of Findings

Assessment strategy and rationale

Assessments in this unit are designed to reflect authentic professional and research practice within computing disciplines. As the second stage of the capstone sequence, students are required to implement, evaluate, and communicate the research or applied project they planned in Project A: Research Essentials. 

The assessment strategy follows a progressive and evidence-based structure that mirrors the life cycle of a professional computing project. Students begin by submitting a progress report that demonstrates sustained engagement, critical reflection, and refinement of the project plan in response to supervisor feedback. They then complete a technical implementation and results report, evidencing their ability to apply advanced analytical or computational methods to achieve project outcomes. The unit culminates in a comprehensive final report and presentation, where students synthesise results, evaluate their significance, and communicate findings to academic and professional audiences. 

Each assessment integrates formative feedback from supervisors to ensure iterative improvement and ongoing reflection. Assessment security and authenticity are maintained through regular supervision, milestone tracking, and oral verification of understanding. This structure promotes autonomy, accountability, and research integrity—key attributes of professional computing practice at the master’s level. 

To pass the unit, students must demonstrate achievement of every unit learning outcome and obtain a minimum mark of 50% 

Overview of assessments

Assessment Task 1: Project Progress  Stud...

Assessment Task 1: Project Progress

 Students submit a mid-project progress report documenting the implementation of their proposed methodology, preliminary findings, and any modifications made to the research design since Capstone A. The report is accompanied by a supervisor review confirming progress and engagement. 

Submitted under secure conditions with authorship verified through supervisor consultation and authenticated submission. 

Weighting

10%

Learning Outcomes LO1, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11, GC12

Assessment Task 2: Report Students implement and...

Assessment Task 2: Report

Students implement and document the approved research or technical solution developed in Capstone A. The report includes experimental design, data analysis or system performance evaluation, results interpretation, and discussion of limitations. This task assesses the student’s ability to apply advanced analytical, computational, and methodological skills to achieve project objectives. 

Administered under secure conditions. Students complete a short viva or recorded discussion verifying their contribution, methods, and understanding of the results. 

Weighting

60%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11

Assessment Task 3: Final Project Presentation St...

Assessment Task 3: Final Project Presentation

Students prepare and present a comprehensive final report that integrates findings, evaluates outcomes against objectives, and reflects critically on project success, limitations, and ethical considerations. A formal presentation or defence communicates results to both academic and professional audiences. 

Conducted under secure conditions. Authorship and understanding are confirmed through the presentation or oral defence component 

Weighting

30%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC3, GC7, GC8, GC9, GC10, GC11, GC12

Learning and teaching strategy and rationale

The teaching approach within this unit places students at the centre of their learning and supports their development as independent, reflective professionals. Learning is structured through a combination of interactive learning materials, supervisory guidance, and self-directed project work that enables students to apply, evaluate, and communicate advanced disciplinary knowledge. 

Students engage with asynchronous online materials and interactive sessions designed to consolidate technical, analytical, and ethical understanding relevant to their project. These are complemented by supervisor consultations and peer-to-peer collaboration, where students discuss progress, share insights, and receive formative feedback. 

This approach encourages autonomy and critical reflection while fostering problem-solving, project management, and professional communication skills. By mirroring authentic industry and research practice, the unit ensures students develop the capability to deliver and defend complex computing solutions that align with professional and vocational expectations. 

Representative texts and references

Representative texts and references

Alby, T. (2023) Data Science in Practice. Chapman & Hall.

Gray, D. & Shellshear, E. (2024) Why Data Science Projects Fail: The harsh realities of implementing AI and analytics, without the hype. Chapman & Hall.

Han, J., Pei, J., & Kamber, M. (2022). Data Mining: Concepts and techniques (4th ed.). Morgan Kaufmann.

Jamsa, K. (2021). Introduction to Data Mining and Analytics. Jones & Bartlett Learning, LLC.

Memon. Q.A. & Khoja, S.A. (2020). Data Science Theory, Analysis and Applications. CRC Press.

Saavedra, D.M. (2022) How To Think About Data Science. Chapman & Hall.

Sharaff, A. & Sinha, G.R. (Eds.) (2022) Data Science and Its Applications. Chapman & Hall.

VanderPlas, J. (2026). Python Data Science Handbook: Essential tools for working with data. O'Reilly Media.

Williams, S. (2020). Data Action: Using data for public good. The MIT Press.

Yu, B. & Barter, R.L. (2024). Veridical Data Science: The practice of responsible data analysis and decision making. MIT Press.

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