Graduate Certificate in Artificial Intelligence (Online)

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.

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.

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.

Artificial Intelligence systems increasingly support high-stakes decisions, yet failures often stem not from model accuracy but from weaknesses in system architecture, data pipelines, security, and lifecycle governance. AI systems inherit traditional software vulnerabilities—such as insecure data flows, dependency risks, adversarial threats, and inadequate validation—and these risks are amplified when AI is deployed without strong Software Development Lifecycle (SDLC) practices. Effective AI development therefore requires early risk identification, secure-by-design engineering, robust data quality controls, and continuous monitoring to prevent societal, cultural, and organisational harm.
This unit positions AI as a socio-technical system that must be engineered and governed holistically across its lifecycle. Students examine how AI models and workflows interact with organisational processes and user needs, and how responsible system design depends on anticipating risks at the earliest stages. The unit highlights key international standards underpinning trustworthy AI, including ISO/IEC 42001 AIMS, ISO/IEC 5259 Data Quality for AI, and global incident-reporting frameworks. Students also explore inclusive and culturally aware co-design approaches, including principles informed by Australian Aboriginal and Torres Strait Islander perspectives, to support cultural safety and relational accountability. Through hands-on work with MLOps, secure cloud deployment, and automated testing, students learn to operationalise AI systems that are secure, transparent, reliable, and aligned with responsible innovation principles. The aim of this unit is to develop students’ capability to design, implement, and manage intelligent systems that are scalable, secure, transparent, and responsible, grounded in recognised AI lifecycle and governance standards.