COURSE CURRICULUM

The Master of Data Science is structured as a 96-credit-point course divided into three parts designed to build your expertise progressively: foundation studies, core studies and applied studies. Part A, the foundation studies, provides an essential orientation to data science at the graduate level and is intended for students whose previous qualifications are not in a related discipline. These units equip you with the fundamental concepts, tools and methods needed to engage confidently with more advanced material. Part B, the core studies, develops a deeper understanding of both the theoretical and practical dimensions of data science. In this component, you will draw on best practices in data analysis, modelling, visualisation and computational techniques, gaining insight into the challenges and opportunities that shape contemporary data-driven work. The core units help you build critical reasoning and technical skills that can be applied across a wide range of real-world contexts. Part C, the applied studies, focuses on professional or scholarly experiences that contribute directly to your career development. You may choose either a coursework pathway, which includes advanced study and an industry experience studio project, or a research pathway that involves completing a thesis. Students who are considering further study through a higher degree by research are encouraged to take the research option to strengthen their academic preparation. Entry points into the course depend on your previous qualifications. Those admitted at entry level 1 complete the full 96 credit points across all three parts, while those with relevant prior study may enter at entry level 2 and complete 72 credit points, covering only the core and applied studies. Students may also choose to decline credit and undertake the full program if they prefer a more comprehensive learning experience. Overall, the structure of the course provides a clear and flexible progression, ensuring you develop both foundational knowledge and advanced expertise in data science.