COURSE CURRICULUM

This course structure is designed to provide students with a comprehensive foundation in both the theoretical and practical aspects of data science and applied statistics. Core modules focus on key analytical and computational skills, beginning with Applied Regression and Experimental Data Analysis, which develops knowledge of modelling and interpreting data in research contexts, and Applied Statistics, which strengthens understanding of statistical methods and their real-world applications. Machine Learning is included as a compulsory element, equipping students with skills in predictive modelling, pattern recognition, and algorithmic approaches to data. Data Visualisation forms another vital component, allowing students to translate complex data into accessible and meaningful visual formats for effective communication and decision-making. Alongside these, students have the opportunity to tailor their studies by selecting from optional modules worth 60 credits, enabling them to specialise in areas of interest or align with career goals. The program also emphasises employability and professional development through the Research Skills and Employability module. Finally, a major research dissertation forms the core capstone element of the degree, offering students the chance to undertake in-depth independent research. Altogether, the course balances technical expertise, applied practice and research skills essential for careers in data science and related fields.