COURSE CURRICULUM

The taught stage of the MSc Data Science programme provides a comprehensive blend of mathematical, computational, analytical, and strategic knowledge required for modern data science careers. Students begin with Fundamentals of Digital Technologies, where they develop essential mathematical, programming, and algorithmic foundations through Python-based projects while exploring concepts in computer science, data science, and artificial intelligence. Bayesian Theory and Data Analysis introduces advanced Bayesian statistical methods, enabling students to design analytical models, develop computational techniques, evaluate assumptions critically, and generate meaningful insights from complex datasets. Applied Machine Learning focuses on the complete machine learning pipeline, teaching students how to analyse data, select and optimise models, evaluate performance, and transform real-world problems into effective machine learning solutions. The AI: Vision and Reality module provides a broader understanding of artificial intelligence by exploring its historical development, major achievements, limitations, philosophical foundations, and ethical implications, allowing students to critically evaluate current and emerging AI technologies. A significant element of the programme is the Interdisciplinary Collaborative Project, where students work in multicultural and multidisciplinary teams to address real-world client challenges. Through the application of design thinking, sustainability principles, creativity, leadership, and teamwork, students gain valuable experience in collaborative problem-solving while developing professional and interpersonal skills. Machine Learning for Data Science and AI builds advanced expertise in statistical machine learning by introducing supervised and unsupervised learning methods, including regression, decision trees, clustering techniques, and support vector machines. Students gain practical experience implementing, evaluating, and refining algorithms to analyse complex data and support evidence-based decision-making. Strategic Management complements the technical modules by developing students’ ability to analyse competitive environments, assess organisational strengths and weaknesses, formulate strategic solutions, and make informed decisions while considering ethics, sustainability, and corporate responsibility. Together, these modules provide a strong balance of theoretical understanding and practical application, equipping students with expertise in data analysis, machine learning, artificial intelligence, statistical modelling, and strategic thinking. Graduates emerge with the technical skills, analytical capabilities, and collaborative experience needed to pursue successful careers in data science, artificial intelligence, analytics, research, consulting, and other data-driven industries.