Graduate Certificate in Data Analytics (Online)

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.

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.

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.

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.