University College London
Founded in 1826, UCL is London’s leading multidisciplinary university, with more than 13,000 staff and 42,000 students from 150 different countries. We are a diverse community in the heart of London, with the freedom and courage to challenge, question and think differently. Through a progressive approach to teaching and research, our world leading academics, curious students and outstanding staff continually pursue excellence, break boundaries and make an impact on real world problems.
UCL contributes to the Martingale Scholarships through its two departments of Mathematics and Statistical Science. Mathematics was one of a small number of disciplines established at UCL’s foundation in 1826. The Department of Statistical Science, founded in 1911, was the first statistics department to be established anywhere in the world. Today the two departments, Mathematics and Statistical Science, work closely together as constituent parts of UCL’s Institute for Mathematical and Statistical Sciences (IMSS).
The departments have a vibrant, diverse community of over 100 academic staff, hundreds of postgraduate students and thousands of undergraduates. They welcome a regular stream of distinguished visiting academics from home and abroad. The broad range of research interests cover many areas of modern pure and applied mathematics, including pure and applied analysis, statistical methodology, geometry and topology, number theory, inverse problems, fluid dynamics, mathematical modelling, numerical analysis, and financial mathematics.
Following the latest Research Excellence Framework assessment, mathematical sciences at UCL is currently ranked 8 in the UK in terms of research power. For the mathematics unit of submission, 97.9% of UCL’s papers were graded as 4* world leading or 3* internationally excellent, the second highest of any at UCL.
The Masters courses being offered through Martingale
MSc in Mathematical Modelling
The MSc in Mathematical Modelling provides an ideal foundation for students wishing to advance their mathematical modelling skills. The programme teaches students the basic concepts which arise in a broad range of technical and scientific problems and illustrates how these may be applied in a research context to provide powerful solutions.
The programme is aimed at students with a background in a highly numerate discipline who wish to advance their mathematical modelling skills. Successful students will be well placed to satisfy the growing demand for mathematical modelling in commerce and industry, and will learn and practise the skills necessary to pursue further research. This MSc enables students to consolidate their mathematical knowledge and formulate basic concepts of modelling before moving on to case studies in which models have been developed for issues motivated by industrial, biological or environmental considerations.
MSc in Data Science
Data science brings together computational and statistical skills for data-driven problem solving. The MSc in Data Science programme will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking. The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students will take one compulsory module and up to two additional modules in Computer Science, with the remaining modules (including the research project) taken mainly from within Statistical Science.
MSc in Statistics
Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, commerce and medicine. The quantitative skills training provided by the MSc in Statistics can lead to new and exciting opportunities in industry, healthcare, government, commerce or research. The programme takes a broad-based approach to statistics, providing up-to-date training in the major applications and an excellent balance between theory and application. It covers modern ideas in statistics including applied Bayesian methods, generalised linear modelling and object-oriented statistical computing, together with a grounding in traditional statistical theory and methods.