COURSE CURRICULUM

The course structure for this major is designed to build strong foundations in computing, statistics, and data-driven problem solving, progressively advancing toward professional-level data science and software applications. In Level 1, students complete core introductory units including CITS1401 Computational Thinking with Python (6 points), which develops basic programming and problem-solving skills, CITS1402 Relational Database Management Systems (6 points), which introduces data storage and database design, PHIL1001 Ethics for the Digital Age: An Introduction to Moral Philosophy (6 points), which explores ethical issues in technology use, and STAT1400 Statistics for Science (6 points), which builds essential statistical reasoning skills. Students may also complete a bridging unit such as MATH1721 Mathematics Foundations: Methods (6 points) depending on prior preparation. In Level 2, core studies include CITS2402 Introduction to Data Science (6 points), STAT2401 Analysis of Experiments (6 points), and STAT2402 Analysis of Observations (6 points), focusing on statistical modelling, experimental design, and real-world data analysis. In Level 3, advanced units such as CITS3200 Professional Computing (6 points), CITS3401 Data Warehousing (6 points), CITS3403 Agile Web Development (6 points), STAT3064 Statistical Learning (6 points), and STAT3405 Introduction to Bayesian Computing and Statistics (6 points) prepare students for professional roles in data science, analytics, and computational research. This structure ensures a strong balance between computing, statistics, and applied problem-solving skills.