Taught course

Data Analytics

University of Brighton · School of Computing, Engineering and Mathematics

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Entry requirements

Normally an honours degree at 2:1 or above or equivalent work experience. Candidates with a 2:2 may be considered depending on individual grades. Applicants are expected to provide evidence of familiarity with and aptitude for mathematics and statistics concepts and methods.

Months of entry


Course content

Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.

Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.

Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.

You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.

Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments.

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification
    part time
    24-72 months
    • Campus-based learningis available for this qualification

Course contact details

School of Computing, Engineering and Mathematics
01273 644644