Data is the currency of all but the most theoretically-based scientific research, and it also underpins our modern world, from the flow of data across international banking networks and the spread of memes across social networks, to the complex models of weather forecasting. The constant generation of data from our digital society feeds into our everyday lives, affecting how we receive healthcare to influencing our shopping habits. In order to handle, make sense of, and exploit large volumes of available data requires highly skilled human insight, analysis and visualisation. The professionals working in this field are called ‘data scientists’, who blend advanced mathematical and statistical skills with programming, database design, machine learning, modelling, simulation and innovative data visualisation. These professionals are in high demand in both public and private sectors in the UK and worldwide. This programme aims and learning outcomes are built around two guiding principles:

• To provide comprehensive understanding of the fundamental mathematical and statistical concepts underlying data science, and how they are implemented in algorithms and machine learning techniques to solve a variety of data processing and analysis problems.
• To provide training in the practical skills relevant to data science, central of which is the ability to write clean and efficient code in industry-recognised languages (in particular, Python and R), but also includes data handling, manipulation, mining and visualisation techniques.

Upon completion of the programme you will be able to demonstrate (and apply) an understanding of a wide range of theoretical and practical skills enabling you to enter a variety of disciplines and industries. You will be able to:

• Understand and be able to critically assess the various strengths and weaknesses inherent to different data science methodologies.
• Design creative strategies and solutions to tackle unfamiliar data science problems and critically assess outputs and results through appropriate statistically robust validation and other performance assessment techniques.

• Effectively communicate problems, methods, results and conclusions through oral and written presentation to both expert and non-expert audiences.
• Have an appreciation of both the underlying research behind data science techniques (e.g. cutting-edge algorithms and computational techniques) and their relevance and application across a broad range of disciplines. management such as educational institutions, hospitals, retail chains, banks, or government.