menu icon
Book your open day visit nowClick to book open day
MSc Statistics and Computational Finance

MSc Statistics and Computational Finance

Different course options

Study mode

Full time

Duration

1 year

Start date

16-SEP-24

Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Modelling / Simulation Systems Computational Mathematics / Cybernetics Financial Modelling Statistics Finance / Accounting (General)

Course type

Taught

Course Summary

Train to work as a professional statistician and gain skills and experience working at the interface between statistics and finance.

Our course emphasises data analysis and will provide you with contemporary statistical ideas and methodologies that are attractive to prospective employers. The skills you gain are useful for a wide range of financial data analysis and in a range of other sectors where data analysis is required, for example sociology, health science, medical science, or biology. This experience is also an ideal foundation for further academic study; many of our students choose to progress to PhD.

Our Statistics and Probability Group has a thriving research culture. Our group works with mainstream statistics to develop new methodology and apply it to real-world problems. The team produces world-class research, publishing in top journals. Our graduate students have full access to this expertise, as well as being exposed to forefront research carried out across the globe through our regular seminars and working groups.

Assessment and feedback

All taught modules are assessed by a combination of closed book written exams, coursework, projects, and presentations.

The closed book written exam assesses your subject-specific knowledge through both theoretical and practical questions and open-ended problems.

The coursework and projects often require the use of software, giving you an opportunity to develop your technical skills. They will test your subject knowledge and analytical, theoretical skills as well as the practical aspects of application, implementation, and interpretation.

Developing and delivering digital presentations will enhance your communication skills for a range of audiences, from the public to subject experts.

The independent study module relies on your own research, so you'll continue to develop your critical reasoning and digital literacy skills, including programming. As this module is assessed with a dissertation, your training is rounded off by consistently working on your written communication skills.

Careers and skills

The big data analysis skills you develop on this course provide attractive employment opportunities in a growing number of industries where such skills are in high demand. The course is also a good foundation for continuing your studies at PhD level.

Career opportunities

  • Quantitative analyst
  • Auditor
  • Account manager for a bank
  • Trainee chartered accountant
  • Management associate
  • Software developer

Modules

This module aims to introduce statistics theory and methodology which are relevant to insurance and actuarial science. At the end of the module you should be able to: have developed a knowledge and good understanding of models for analysing insurance data; have a good degree of familiarity with distributions and inferential techniques in the analysis of insurance data; have a reasonable degree of familiarity with the main statistical theory in the analysis of insurance data; know what sorts of methodologies should be applied to model insurance risk in different periods; use statistical software to analyse insurance data by various methodologies.

Tuition fees

UK fees
Course fees for UK students

For this course (per year)

£15,890

International fees
Course fees for EU and international students

For this course (per year)

£32,260

Entry requirements

Undergraduate degree: 2:1 or equivalent in Mathematics or in a subject with a substantial mathematics component. We may accept a 2:2 undergraduate degree supported by relevant professional qualifications.