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The MSc Applied Data Science equips aspiring data scientists with the advanced technical knowledge, analytical thinking, and practical competencies required to extract insight and value from complex data. It provides a rigorous, research-led immersion into the end-to-end data science lifecycle, strikes a balance between theory and practical skills, emphasising on technical know-how, leadership skills, innovation and application.

The programme is structured around four taught modules and a capstone project designed to build advanced mastery. It begins with the attainment of advanced scripting skills and a systematic understanding of mathematics, and machine learning alongside research methods, professional practices, and leadership to deliver data-driven transformation. This is followed by two specialised modules that deep dive into the data science lifecycle through the CRISP-DM framework and the advanced techniques in deep learning and Artificial Intelligence. The programme culminates in an individual capstone data science project addressing a real-world problem.

Data Science has emerged as a vital scientific discipline with transformative applications across sectors such as AgriTech, FinTech, HealthTech, EdTech, Iinsurance, and Transport. In the modern information age, the ability to manage the full data lifecycle, from capturing and storing to processing, analysing, and visualising insights, is critical for understanding complex human and system behaviours. There is an urgent societal and market need for a new generation of data scientists equipped with specialist skills in data mining, machine learning, AI, and advanced analytics.

This programme aims to bridge this gap by providing a systematic understanding of the theoretical concepts and practical methodologies of Data Science, informed by the forefront of academic research. The curriculum is designed to equip students with deep subject-specific practical skills in scripting, mathematical modelling, and machine learning, while fostering a critical awareness of emergent insights in Artificial intelligence. Beyond technical proficiency, the programme aims to develop high-level practical and transferable skills, including the ability to act autonomously in planning complex data science projects, demonstrate inclusive leadership in digital transformation, and communicate sophisticated conclusions clearly to a diverse range of stakeholders.

Ultimately, the programme seeks to foster originality in the application of knowledge, empowering graduates to critically evaluate methodologies and propose innovative hypotheses in complex, data-driven environments to deliver strategic value to organisations.

QAA Framework for Higher Education Qualifications of UK Degrees


Relevant QAA Subject Benchmark Statement - Computing:


Digital and technology solutions specialist standard

Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each course unit/module can be found in the departmental or programme handbook. The accuracy of the information contained in this document is reviewed annually by the University of º¬Ð߲ݴ«Ã½ and may be checked by the Quality Assurance Agency.