Statistical Data Science Masters/MSc

Entry requirements

A 2:1 Honours degree in Mathematics and/or Statistics or a programme with advanced mathematical and/or statistical components.

This programme is aimed at students who have previously completed an undergraduate degree with significant mathematical or statistical content and who wish to pursue postgraduate studies to a master level.

Months of entry

September

Course content

Our MSc Statistical Data Science programme uniquely merges statistical theory with state-of-the-art data science techniques, addressing the high demand for professionals skilled in complex data analysis. It focuses on developing proficiency in both statistical methodologies and computational tools.

In the MSc Statistical Data Science programme, you will engage in an in-depth exploration of both foundational and advanced statistical principles, theories, and concepts, complemented by thorough training in data analysis skills. The curriculum is designed to include a wide range of programming techniques and machine learning methodologies, equipping you with the necessary tools to effectively apply these in real-world scenarios.

Central to the programme is the development of your ability to perform critical analysis of complex problems using a diverse set of methods from statistics and data science. This data-driven approach to problem-solving prepares you to generate innovative insights across various domains, positioning you at the forefront of the statistical data science field. Additionally, the programme emphasizes the enhancement of written presentation skills, ensuring that you can communicate complex statistical concepts clearly and professionally in written form, alongside nurturing an ability to work independently.

By addressing the growing shortage of highly skilled professionals in statistics and data science – a need acutely felt across sectors such as economics, medicine, finance, insurance, and industrial management – this programme aims to produce graduates who are well-prepared to integrate and apply their knowledge of statistics and data science. Our graduates will possess the unique capability to analyze, interpret, and provide well-reasoned solutions to complex challenges, thus making significant contributions to their respective industries.

• To understand the mathematical foundation for statistics and data science and carry out critical analysis: This aims to equip you with a robust grasp of the mathematical foundations that form the core of statistics and data science.

• To critically analyse complex problems, applying fundamental principles to uncover insights and inform decision-making in various contexts.

• To use programming languages and software packages essential for data analysis, especially those used in machine learning and AI: This involves training you in developing and deploying advanced algorithms for data analysis. Mastery of these tools is crucial for effectively manipulating, analyzing, and visualizing data, allowing for more insightful and accurate interpretations of complex data sets.

• To model real-world problems in a statistical framework, and tackle them with suitable statistical and data science techniques. This component focuses on the application of statistical and data science methods in diverse real-world settings. It prepares you to adapt and apply your skills to new challenges and environments, ensuring you can provide valuable insights even in areas or situations that are initially unfamiliar to you, thereby showcasing your versatility and real-world problem-solving capabilities.

Information for international students

English language

For students whose first language is not English, one of the following English language qualifications is required:

• IELTS 6.0 with no less than 5.5 in any band
• TOEFL: 80 overall with no less than 19 in Reading, 19 in Listening, 21 in Speaking and 19 in Writing
• Pearson Test of English (PTE) including online: Academic 64 with no less than 59 in all four skills
• Cambridge English (exams taken from 2015): Advanced – minimum overall score of 169, with no less than 162 in any component

Fees and funding

Funding opportunities are available.