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Course summary

This course (previously Business Intelligence and Analytics MSc) addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course stretches the artificial intelligence (AI), machine learning (ML) and decision science themes to business intelligence, data science and business analytics. You'll focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, using applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You'll also gain a greater understanding of the impact technological advances have on nature and practices adopted within data science, business intelligence and analytics, and how to adapt to these changes. Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools, and methods for data science. These include data warehousing and mining, distributed data management, and the technologies, architectures, and appropriate AI and ML techniques. The second theme will enhance your knowledge of algorithms and the quantitative techniques including AI, ML, and Operational Research (OR) suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area. Teaching approaches include lectures, tutorials, seminars, and practical sessions. You will also learn through extensive coursework, class presentations, group research work, and the use of a range of industry-standard software such as R, Python, Simul8, Palisade Decision Tools, Tableau, and Oracle. Modules are typically assessed through practical coursework, which may also include an in-class test.

Entry requirements

A minimum of a lower second class honours degree (2:2) in a scientific or engineering discipline with some exposure to the use of IT, or in an area of computer science or IT with a strong interest in quantitative analysis. If you do not have a formal qualification, but you are already in employment, you may be considered if your role involves the data mining and decision support techniques and technologies deployed in the course. If your first language is not English you should have an IELTS 6.5 with at least 6.0 in writing. Applicants are required to submit one academic reference.

Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

No additional fees or cost information has been supplied for this course, please contact the provider directly.

Sponsorship information

Please visit our website to read about funding options: If you are an international applicant, please visit this page to see scholarships available:

Data Science and Analytics at University of Westminster, London - UCAS