COURSE CURRICULUM

The program provides a comprehensive foundation in applied mathematics, statistics, and data analytics, preparing students for careers in data-driven decision-making and research. In the first year, students complete 48 credit points in core courses that include Optimisation for Decision Making, Applied Analytics, Database Concepts, and Data Wrangling. These courses equip students with essential skills in mathematical modelling, data management, and analytical thinking. Additionally, students select 24 credit points from a range of advanced courses such as Essential Mathematics, Applied Bayesian Statistics, Regression Analysis, Multivariate Analysis Techniques, Forecasting, Time Series Analysis, Machine Learning, and Statistical Data Science. This allows students to tailor their learning to specific interests and career goals. A further 24 credit points can be chosen from additional electives, enabling a deeper focus on areas such as system dynamics, sports analytics, experimental design, and statistical computing.In the second year, students undertake an Applied Research Project, worth 12 credit points, to apply their knowledge and analytical skills to real-world problems. They also complete 60 credit points from a selection of advanced courses, which reinforce and extend the first-year learning. Options include courses in Data Visualisation and Communication, Analysis of Categorical Data, Advanced Optimisation, and Machine Learning, among others. Students may also choose to complete a Minor Thesis, which provides an opportunity to conduct a significant research investigation and develop advanced problem-solving capabilities.Overall, the program emphasizes both theoretical understanding and practical application, ensuring that graduates are capable of handling complex datasets, performing rigorous statistical analyses, and communicating insights effectively. By combining core mathematical skills with specialised electives and research experience, students develop a versatile skill set that is highly valued in industries such as finance, business analytics, health data, sports analytics, and government research, enabling them to make informed, data-driven decisions and contribute meaningfully to their chosen fields.