Course summary

• Learn to use industry-leading software including SAS and HaDoop.
• Gain hands-on knowledge of data storage, data mining and statistical modelling techniques.
• Study aspects related to the distribution and integration of data with emphasis on the business context.
• Have the opportunity to work abroad - with the possibility of Erasmus funding being available for European placements.
• Prepare for jobs with salaries ranging from £35,000-£80,000.

Big data analysis is essential for companies to understand their customers better, develop new products and cut operational costs. This course has been developed in consultation with our industry contacts to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. This course offers a two year work experience route including a placement of up to 12 months.

How you learn

All our courses are designed around a set of key principles based on engaging you with the world, collaborating with others, challenging you to think in new ways, and providing you with a supportive environment in which you can thrive.

This course explores statistics, computing and management to help you develop the skills and expertise needed by a data scientist. You learn about data analytics (including statistical modelling and data mining), systems development (including big data and distributed systems), data integration, how data is stored and utilised, and the core skills around professionalism and research.

Our partnerships with business inform the course design, ensuring the content is relevant, up-to-date and meets the needs of industry. These partnerships also enable the inclusion of cutting edge software such as SAS.

You learn through

• teaching sessions
• learning materials
• recommended readings
• problem-based learning
• group projects
• contemporary software platforms

Future careers

This course prepares you for a career in

• statistical analysis and machine learning techniques
• database and programming technologies
• statistical theory
• marketing and insight
• credit risk
• data and technology
• digital analytics
• data science.