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MSc Computational Applied Mathematics

  • DeadlineStudy Details:

    1 year full time

Course Description

Computational Mathematics, in particular the physical applied areas and the theory and implementation of numerical methods and algorithms, have wide-ranging applications in both the public and private sectors.

More recently, in this era of ubiquitous and cheap computing power, there has been an explosion in the number of problems that require us to understand processes by modelling them, and to use data sets that are large. The subject of Computational Mathematics has become increasingly prominent.

There is high demand also for computational modellers and data scientists. This programme concentrates on the overlap and synergy between these fields.

The School of Mathematics has connections with a wide number of industrial partners, organisations, charities and government departments. As an MSc student in the School you will have opportunities to engage with these external organisations through regular employability events, careers workshops, and our annual analytics challenge (whose recent partners have included Edinburgh Airport and the Data and Marketing Association). There is also the opportunity to undertake a dissertation project with an external partner.

Entry Requirements

A UK 2:1 degree, or its international equivalent, in a numerate discipline such as mathematics, engineering, computer or physical sciences.

Previous study of applied mathematics, probability and differential equations at university level will be required.

Applicants should have studied a university level course with a substantial programming element, or have an equivalent level of programming experience.

You can increase your chances of a successful application by exceeding the minimum programme requirements.

Fees

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Student Destinations

This programme will provide training in the tools and techniques of mathematical modelling and scientific computing. It will also provide students with skills for problem solving using modern techniques of applied mathematics.

Key graduate employment sectors include:

  • software engineering
  • data science
  • banking and finance

In addition to entering employment, many of our graduates progress on to study a PhD in a variety of topics including:

  • applied and computational mathematics
  • computational biology
  • biomedical engineering
  • physics

Students have gone on to study their PhDs at a variety of highly-ranked universities such as ETH Zurich, Edinburgh, Bristol and St Andrews.

Module Details

Compulsory courses have previously included:

  • Applied Dynamical Systems
  • Numerical Linear Algebra
  • Python Programming
  • Numerical Partial Differential Equations
  • Research Skills for Computational Applied Mathematics

Optional courses have previously included:

  • Applied Stochastic Differential Equations
  • Statistical Methodology
  • Stochastic Modelling
  • Fundamentals of Optimization
  • Statistical Programming
  • Bayesian Theory
  • Introductory Probability and Statistics
  • Industrial Mathematics
  • Data Analytics with High Performance Computing*
  • Numerical Ordinary Differential Equations and Applications
  • Time Series
  • Large Scale Optimization for Data Science
  • Optimization Methods in Finance
  • Biomedical Data Science
  • Bayesian Data Analysis
  • Mathematics in Action
  • Machine Learning in Python
  • Data Assimilation

*delivered by the School of Informatics

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