The Data Analytics MSc is open to all learners who meet the following prerequisites:

*A BSc, BEng or BA Honours degree (2:2 or above) or equivalent professional qualification in any subject. *Applicants with other appropriate professional qualifications and/or experience will be considered on an individual basis.

Additionally, learners are expected to have an IELTS proficiency score of at least 6.0 (no less than 5.5, or equivalent) if English is not their first language.

This fully online programme offers you the freedom to grow your career on a timeline that works best for you. Its British Computer Society (BCS) accreditation and wide range of topics in the field of data analytics and related fields will enable you to stand out against the competition and meet the demands of a growing industry.

As you progress through the programme, you’ll learn how to:

• Master research skills: Develop essential research skills for computing, including scientific methods, literature review, project management, and effective presentation techniques.

• Analyse data and statistics: Explore statistical methods vital for data analysis and their applications across various sectors such as business, health, and structured/unstructured data analysis.

• Visualise data insights: Learn to transform complex datasets into valuable insights through advanced data visualisation theories, best practices, and practical application of cutting-edge tools.

• Handle large databases: Discover alternative approaches to data management with a focus on building robust organisational information systems capable of handling large-scale data requirements.

• Understand data mining: Uncover meaningful patterns within large datasets using data mining techniques, evaluate the quality of discovered knowledge, and understand its social impact across various application areas.

• Harness big data analytics: Gain insights into the challenges and opportunities presented by big data analytics, exploring fundamental characteristics, architectures, processing techniques, and domain applications.

• Embrace machine learning: Explore the wide-ranging applications of machine learning, including recognition, classification, optimisation, and practical implementations to improve existing AI systems.