COURSE CURRICULUM

This data science programme with Advanced Practice combines academic knowledge with practical industry experience to prepare students for careers in data analytics, business intelligence and artificial intelligence-related fields. The Advanced Practice module provides an opportunity to gain valuable real-world experience through vocational internships with external organisations, research or development internships on campus and employer-led projects that align with students' skills and career aspirations. Students receive preparatory support for internship applications and are guided throughout the placement period by an academic supervisor who provides both academic and pastoral support. The Big Data and Business Intelligence module develops expertise in designing and implementing databases, analytics systems and big data applications to address business challenges. Students study the system development lifecycle while exploring data warehouses, legacy system integration, data management and techniques for modelling and visualising large datasets for different business needs. Data Security and Information Governance introduces the principles of information security, privacy protection and governance frameworks used in modern organisations, while examining legislative compliance requirements and the role of physical and software-based security technologies in protecting sensitive information. Machine Learning and Deep Learning provides a strong foundation in computational modelling, predictive analytics and intelligent systems by combining concepts from computer science, statistics and probability theory. Students explore machine learning algorithms, neural networks and recent developments in deep learning technologies used across multiple industries. The Masters Project allows students to undertake an independent work-based or research-focused project tailored to their professional interests and career goals, encouraging reflection, innovation and the application of acquired knowledge to real-world problems. Statistics for Data Science develops practical understanding of statistical methods including correlation analysis, regression techniques, data classification and normalisation, equipping students with the analytical skills required to interpret results and support evidence-based decision-making. Together, these modules prepare graduates for successful careers in the growing field of data science and analytics.