Unit rationale, description and aim
In an era where technology drives innovation and transformation, computing professionals must be able to design and manage evidence-based projects that address real-world challenges and enable sustainable growth and agility in organisations.
This unit serves as the first stage of a two-part capstone sequence designed to foster independent inquiry, applied research, and problem-based learning. Students will engage with the relevant research literature, identify an area of investigation, and complete the exploratory and planning phases of a research or industry-aligned project. Drawing on knowledge and skills developed throughout their studies, students will integrate technical, analytical, and ethical perspectives to define a research problem, formulate appropriate methodologies, and prepare a structured plan for project implementation in the subsequent capstone unit.
The aim of this unit is to equip students with the knowledge, critical thinking skills, and research capability required to undertake and plan a substantial, inquiry-based project within their discipline of study—whether in data science, artificial intelligence, or a related computing field.
Campus offering
No unit offerings are currently available for this unit.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.
Critically review, evaluate, and synthesise schola...
Learning Outcome 01
Design and justify an appropriate research methodo...
Learning Outcome 02
Conduct and interpret exploratory or preliminary a...
Learning Outcome 03
Develop and communicate a comprehensive project pr...
Learning Outcome 04
Content
Topics will include:
- Research preparation including
- Research ethics and principles
- Types of scientific research
- Research questions and hypotheses
- Literature review
- Problem identification and articulation of the research question
- Data Collection and Preparation
- Undertake data wrangling
- Develop a project timeline
- Exploratory data analysis and visualisation
- Data requirements and acquisition
- Statistical modelling
- Risk assessment and mitigation
- Development of project plan
Assessment strategy and rationale
Assessments in this units are designed to replicate contemporary industry practice in data design projects. In these environments data scientist will be required to evaluate and synthesise evidence to develop projects to support the needs of clients. Consequently, students begin this process with a literature review which assists them to acquire the skills of developing a broad picture of current evidence to inform project development and from this generate specific research questions and form a preliminary hypothesis. The second assessment requires students to compare their hypothesis with initial evidence outputs generated from the project. Finally, for assessment three, students will continue to develop their skills of reflection and project refinement through development of a plan to complete the analysis (in data science project B) and report on risks and mitigation strategies to inform future directions of the project. Students must pass this assessment to pass the unit. Formative feedback will be provided to students to allow iterative development of their project plan. In mirroring workplace practice and using a project-based approach, this assessment authentically represents requirements of them in the workplace and supports work readiness.
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: Literature Review and Problem...
Assessment Task 1: Literature Review and Problem Definition
Requires students to conduct a critical review of relevant academic and professional literature to identify knowledge gaps and define a focused research or industry problem within their computing discipline. Students must demonstrate understanding of the ethical, social, and technical context of the chosen topic and articulate a clear research question or problem statement..
Administered under secure conditions with authenticity verified through a short oral clarification or supervisor discussion to confirm the student’s understanding and authorship.
20%
Assessment Task 2: Exploratory Data Analysis (ED...
Assessment Task 2: Exploratory Data Analysis (EDA) Report
Students conduct exploratory or preliminary analysis relevant to their proposed project — such as dataset exploration, algorithm testing, literature synthesis of technical methods, or system feasibility analysis. The task assesses the ability to apply research and analytical techniques to generate initial insights that will inform methodological design in Capstone B.
Assessed under secure conditions; students will discuss their analysis process and findings with their supervisor or assessor to confirm originality and understanding.
30%
Assessment Task 3: Research Methodology and Proj...
Assessment Task 3: Research Methodology and Project Plan
Students prepare a comprehensive project proposal that synthesises insights from their literature review and exploratory analysis. The proposal should outline objectives, scope, methodology, ethical considerations, risk management, milestones, and resources for the continuation of the project in Capstone B. Students must communicate their plan clearly to academic and professional audience
Submitted via the Learning Management System (LMS) under secure conditions with authorship verified through supervisor confirmation and a short presentation or discussion.
50%
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 researchers. Learning is structured through a combination of interactive learning materials, self-directed study, and guided project supervision. Students engage in research design, data exploration, and practical exercises that foster critical thinking, problem solving, and collaboration.
Each student (or small group, where applicable) will be allocated an academic supervisor who provides ongoing guidance and feedback throughout the research process. The supervisor supports students in refining their research focus, developing appropriate methodologies, and adhering to ethical and professional standards in data science.
This integrated model enables students to build conceptual understanding, research capability, and applied data-driven problem-solving skills, aligned with vocational and professional practices in computer and data science.