COURSE CURRICULUM

The taught stage of the MSc Data Science programme at Aston University provides a structured and advanced curriculum designed to build strong foundations in artificial intelligence, statistical modelling, and machine learning through 90 credits of core study and additional optional specialisation modules. AI: Vision and Reality introduces the broader context of artificial intelligence, helping students understand its capabilities, limitations, and real-world applications. Applied Machine Learning focuses on practical techniques for developing predictive models, applying algorithms to real datasets, and solving complex analytical problems across different domains. Bayesian Theory and Data Analysis develops probabilistic reasoning skills, enabling students to model uncertainty, interpret data rigorously, and apply Bayesian methods to real-world problems. Neural Networks and Deep Learning explores modern AI architectures, including deep learning models used in image recognition, natural language processing, and advanced pattern detection. Statistical Machine Learning integrates statistical principles with machine learning methods, strengthening students’ ability to design, evaluate, and optimise data-driven models. The Aston Global Advantage module runs throughout the programme and enhances employability by developing professional skills such as communication, teamwork, leadership, and career readiness. Students also choose one optional module to tailor their learning: Data Visualisation develops skills in presenting complex data clearly and effectively through visual analytics techniques, while Multi Agent Systems and Strategic Decision Making explores computational models of decision-making in multi-agent environments. A further optional choice allows specialisation in AI in Biomedical Imaging, which applies AI techniques to medical image analysis, or Natural Language Processing, which focuses on computational methods for understanding and generating human language. Together, these modules provide a balanced blend of theoretical understanding, mathematical foundations, and applied technical skills. Graduates are well prepared for careers in data science, machine learning engineering, AI development, analytics, and research, or for further academic progression into doctoral study and advanced computational research.