Curriculum details

Data is widely considered the world’s ‘new oil’, and data science is a powerful discipline that enables organisations to extract actionable insights from large volumes of data. This unit introduces students to the core concepts and practices of data science using Python — a widely adopted programming language known for its clarity, flexibility, and strong support for data analysis.

Students will first build a solid foundation in Python programming, including data types, data structures, control structures, functions, and file operations. They will then progress to using Python’s data science libraries — such as NumPy, Pandas, and Matplotlib — for tasks such as data wrangling, exploration, and visualisation. Real-world datasets from areas such as health, business, sustainability, and community services will be used to provide practical and inclusive context, ensuring representation of diverse perspectives and experiences.

The unit emphasises ethical data handling, reproducibility, and responsible analysis, while fostering critical thinking and hands-on experience. It also aligns with other units in the MINFTN program, providing foundational programming and data analysis skills that support more advanced study in areas such as machine learning, data mining, and business intelligence. The aim of this unit is to equip students with foundational knowledge and practical skills in Python and data science, enabling them to contribute to societal benefit and the common good.

Machine learning involves enabling computational systems to learn from data and perform a wide range of tasks without being explicitly programmed. These models and algorithms underpin many aspects of contemporary digital life, including email filtering, search engines, social media platforms, virtual assistants, healthcare applications, and recommendation systems.

This unit offers a technical and practice‑oriented introduction to core machine learning models and algorithms. Key topics include pattern discovery, classification, regression, feature extraction, and data visualisation. Students will also engage in hands‑on implementation using Python and the Scikit‑Learn library to address real‑world challenges, including applications in digital health.

The unit further examines issues of machine bias and the potential consequences of inequitable or harmful algorithmic outcomes. Overall, the unit aims to equip students with foundational knowledge and practical skills to design ethical, effective, and responsible.

Databases are a critical technology for organisations, enabling the efficient recording, processing, and management of data while maintaining data quality and security. This unit equips students with foundational knowledge and practical skills in database design and implementation. Students will develop the ability to identify and model organisational information requirements, construct conceptual data models, verify structural integrity through normalisation techniques, and select appropriate database technologies to meet organisational needs.

The unit introduces both relational and non‑relational data storage approaches and associated toolsets. Students will learn to implement and use relational database technologies and Structured Query Language (SQL) in a cloud environment using Microsoft Azure. The unit also covers core database administration tasks and ethical considerations related to the management and protection of organisational data. Designed in alignment with Microsoft’s curriculum, the unit provides a pathway to the DP‑900: Microsoft Azure Data Fundamentals certification.

The aim of this unit is to prepare students to select, design, implement, and administer large‑scale database solutions that address organisational data and information management requirements while upholding data privacy, security, and the dignity of the individuals served by the organisation.

The explosion in data and digital technologies has opened new ways of obtaining data-driven insights. Organisations increasingly require professionals who can work with modern data ecosystems, streaming data sources, and visualisation tools to extract, analyse, and communicate insights.

This unit introduces advanced techniques in data analytics, including big data processing and visualisation using industry-standard and open-source tools. Students will use platforms such as Power BI, Google Looker Studio, Tableau, and Python-based libraries and big data technologies (e.g., Hadoop, Spark, cloud-native platforms) to transform raw data into meaningful insights. Students will also explore data ethics, geospatial analysis, and advanced dashboarding techniques.

The aim of this unit is to equip students with job-ready skills in applied analytics, big data techniques, data storytelling, and dynamic reporting—enabling them to deliver strategic, ethical, and environmentally responsible decisions in complex real-world scenarios.

Deep learning drives many real-world AI applications, including computer vision, natural language processing, and large language models (LLMs), and is a core approach within modern artificial intelligence (AI). Graduates, therefore, need the capability to design AI projects responsibly, select appropriate methods, and evaluate solutions that are effective, safe, and aligned with the common good.

This unit develops conceptual and practical skills in contemporary AI, with a strong emphasis on deep learning. Students begin with a recap of core machine learning concepts, then progress to designing and developing deep learning models and exploring other emerging AI technologies. They will work through the full model lifecycle, including development, training, inference, testing, and evaluation. Using tools such as Python and Keras, students implement and assess AI solutions across key areas, including computer vision, natural language processing, large language models (LLMs), anomaly detection, and reinforcement learning. The unit also integrates Microsoft Azure AI Fundamentals to familiarise students with industry standards, design principles, and common AI solution patterns. Throughout, students examine foundational AI use cases that promote stewardship and the common good, with sustained attention to ethics, safety, and human dignity.

This unit aims to equip students with advanced AI knowledge and skills to design, develop, and evaluate AI-enabled applications responsibly, promoting the common good and upholding human dignity.

Organisations today face the challenge and opportunity of leveraging vast amounts of data to derive actionable insights. Data mining is a critical process for discovering patterns, relationships, and trends within large and complex datasets to support strategic and operational decision-making.

This unit builds on foundational studies in data science and programming, and provides a comprehensive exploration of the knowledge discovery process, incorporating essential topics such as data pre-processing, classification, clustering, association rule mining, anomaly detection, sentiment analysis, and sequential pattern mining. Emphasis is placed on applying both descriptive and predictive data mining techniques to solve real-world problems, using visual data mining tools (e.g. RapidMiner and Orange) and Python programming.

Case studies and applied projects will help students design, implement, and evaluate end-to-end data mining workflows, applying ethical principles and critically reflecting on issues of data privacy, bias, and explainability.

The primary aim of this unit is to develop industry-ready professionals capable of applying data mining techniques and tools to support informed and ethical decision-making across diverse domains.

To make data meaningful and informative, it must be transformed from raw and often messy formats into structured, reliable, and analysable forms.

This unit introduces the end-to-end process of data wrangling, which includes data discovery, cleaning, transformation, integration, and validation to prepare data for analysis and modelling. Students will also explore the fundamentals of machine learning, including how prepared data is used to build and evaluate simple predictive models. By combining data preparation with introductory modelling, students will learn to generate insights that support data-driven decision-making in real-world contexts across business and community sectors.

The aim of this unit is to equip students with the practical and conceptual skills to prepare, analyse, and model data for meaningful insights.

To effectively work with datasets, data scientists need be able to apply established techniques of statistical analysis to the information they work with. Statistical modelling is the application of statistical analysis techniques to datasets. It is a mathematical representation of observed data, allowing relationships between data to be identified, predictions about future sets of data made, and visualization of data to aid understanding. Statistical modelling techniques fall into two groups; supervised learning includes regression and classification models; unsupervised learning includes clustering algorithms and association rules. By exploring case studies and industry-relevant examples, students will have the opportunity of gaining an in-depth understanding of the range and application of both supervised and unsupervised statistical data modelling techniques.

The aim of this unit is to facilitate the development of skills required to analyse datasets.

The ability to anticipate outcomes and make data-driven decisions is fundamental to success across industries. Predictive modelling and analytics leverage historical and current data through statistical, machine learning, and deep learning techniques to estimate future behaviour, identify patterns, and inform strategic decisions.

In this unit, students will explore predictive approaches across diverse data types and apply appropriate techniques to solve real-world problems. The unit emphasises reproducible modelling practices, feature engineering, model evaluation, and the communication of analytical results to stakeholders. Students will develop both technical proficiency and critical insight into the opportunities, limitations, and ethical considerations of predictive analytics in practice.

The aim of this unit is to equip students with the knowledge and practical skills to design, implement, and communicate predictive models that support informed decision-making and innovation across a range of domains.

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.

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.

As artificial intelligence and emerging technologies transform global industries and societies, professionals must demonstrate not only technical mastery but also ethical judgment, cultural awareness, and leadership integrity. This unit prepares postgraduate students to lead responsibly in the governance and implementation of advanced digital innovations.

Students critically evaluate ethical theories, governance frameworks, and professional standards relevant to AI, data analytics, and automation. They explore how values, power, and culture shape technological design, policy, and impact, drawing on diverse perspectives including Aboriginal and Torres Strait Islander knowledges to promote inclusion and social responsibility.

Through research-informed inquiry, collaborative dialogue, and applied policy analysis, students engage with real-world case studies and professional dilemmas to build advanced capability in ethical leadership and decision-making. Learning activities encourage reflective practice, strategic thinking, and intercultural understanding across local and global contexts.

The aim of this unit is to develop ethical leaders and reflective practitioners who can integrate ethics, empathy, and professional responsibility into technology governance and innovation. Graduates will be equipped to influence policy, guide responsible design, and foster trust and fairness in digital transformation.