COURSE CURRICULUM

This Data Science programme provides students with advanced technical knowledge and practical skills in handling, analysing, and interpreting complex datasets to support business and organisational decision making. The course develops expertise in Big Data and Business Intelligence, where students learn to design and implement databases, analytics solutions, and large-scale data management systems while exploring technologies such as data warehouses, legacy data integration, and business intelligence applications. Through the Computing Masters Project, students undertake an independent research or industry-focused investigation, producing substantial outputs such as software applications, analytical tools, systems, or research findings while strengthening project management and critical evaluation skills. The programme places a strong emphasis on data presentation through modules in Data Visualisation and Interactive Visualisation, enabling students to transform complex information into meaningful visual representations using traditional and advanced charting techniques, interactive graphics, and modern tools such as D3.js for web-based visual analytics. Machine Learning develops the ability to create predictive models from data using statistical methods, algorithms, and computational techniques that support intelligent decision making in real-world applications. Research Methods prepares students for postgraduate research by developing skills in academic investigation, evidence generation, evaluation techniques, and research ethics, while Statistical Methods for Data Analytics provides practical understanding of regression, correlation, normalisation, and quantitative and qualitative data analysis methods required for professional analytical work. Assessment is designed to evaluate technical knowledge, research ability, and workplace skills through assignments, tests, case studies, presentations, reports, literature reviews, research proposals, and a dissertation. Learning takes place through lectures, tutorials, guest speakers, case studies, and practical activities that encourage critical thinking, reflection, creativity, and problem solving, ensuring graduates are well prepared for careers in data science, analytics, machine learning, and business intelligence.