COURSE CURRICULUM

To graduate from the Master of Data Science at the University of Southern Queensland (UniSQ), students must successfully complete 16 units of study, structured to provide both theoretical foundations and practical expertise in data science. This postgraduate degree integrates advanced coursework and research opportunities to help students deepen their understanding of data-driven technologies and analytical methods, building upon core concepts in information and communication technology (ICT), programming, and statistics.The program’s eight core courses are designed to establish a solid foundation in essential data science disciplines. Students will study Big Data Management, Machine Learning, and Data Mining, which focus on handling, analysing, and extracting insights from large and complex datasets. Foundations of Programming ensures students develop the coding skills necessary to implement computational models and algorithms effectively. The IS/ICT Project Management course introduces students to professional project management principles, enabling them to lead and coordinate data-related initiatives efficiently. Advanced statistical courses such as Multivariate Analysis for High-Dimensional Data and Statistics for Quantitative Researchers provide the analytical frameworks necessary for data interpretation and evidence-based decision-making. The Advanced ICT Professional Project offers hands-on experience in solving real-world data challenges, allowing students to apply their theoretical knowledge in practical contexts.In addition to the core courses, students can select four specialisation units that align with their career interests, choosing from areas such as Artificial Intelligence, Machine Learning, or Data Analytics. This flexibility allows for a tailored learning experience suited to various industry pathways. Furthermore, four elective courses enable students to broaden their expertise or delve deeper into specific areas of data science, ICT, or related fields.This degree’s structure ensures a balance between technical proficiency, analytical capability, and professional practice, preparing graduates for leadership roles in data science, analytics, and emerging technology sectors.