This course equip students with statistical methods for analysing categorical data generated from qualitative response variables , where traditional quantitative methods such as regression and ANOVA are unsuitable. Students will examine real-world examples across diverse fields including alcohol use, drug safety, education research, endangered species conservation, gender equality, and health risk assessment. For implementation, student will use statistical software (Python, R, or SAS) combined with AI-assisted prompt engineering to optimise their interaction with analytical tools and large language models. This approach enhances both computational efficiency and methodological rigor.