Statistics and Computational Finance MSc
Campus West, United Kingdom
Statistics and Computational Finance MSc
University of York
Statistics and Computational Finance
Program Overview: Statistics and Computational Finance MSc
The Statistics and Computational Finance MSc at the University of York trains you to work as a professional statistician with specialised skills at the interface between statistics and finance. The program emphasises data analysis and equips you with contemporary statistical ideas and methodologies that are highly attractive to prospective employers in industry, academia, and government.
Core modules provide a strong grounding in statistical data analysis and modelling techniques, alongside essential computational skills such as Python and R programming that are sought after by finance-based employers. You will also choose an option module and a dissertation topic aligned with your own interests, creating a flexible programme tailored to your career goals.
Delivered by the Department of Mathematics, this program is supported by the university's Statistics and Probability Group, which has a thriving research culture developing new methodology and applying it to real-world problems. The skills you gain are transferable across financial data analysis, health science, medical science, biology, and many other sectors where data analysis is required.
Key Program Highlights
- Strong emphasis on practical data analysis using Python and R programming languages
- Flexible curriculum with elective module and personalised dissertation topic
- Taught within a Russell Group university ranked 169th globally (QS 2026)
- Excellent career prospects in finance, insurance, technology, and government sectors
Curriculum and Modules
The MSc Statistics and Computational Finance curriculum combines core taught modules with an elective option and a substantial independent dissertation. Core modules build your expertise in statistical modelling, financial mathematics, and computational techniques, while the dissertation allows you to conduct original applied research in your chosen area of specialisation.
Covers the derivation of theoretical results for fitting regression models using least squares, local least squares, and maximum likelihood approaches. You will learn to select appropriate models, carry out statistical inferences, and assess model fit using residual plots and diagnostic techniques with computer software.
Provides a comprehensive introduction to financial time series models and their applications to modelling and prediction of financial data. You will learn to fit time series models, derive theoretical results, carry out predictions, and assess goodness of fit using appropriate software.
Focuses on fundamental concepts underlying continuous-time models of option pricing, principally the Black-Scholes model. Covers both European and exotic options such as barrier options, exploring the relationship between martingale theory and partial differential equation approaches.
Introduces probability theory and stochastic processes as the mathematical language for finance. Covers martingale theory, random walks, Brownian motion, the Poisson process, and an overview of Ito stochastic calculus with financial applications.
Equips you with knowledge and skills to implement numerical solutions for models arising in mathematical finance. Practical computational work involves programming in relevant software to solve real-world financial modelling problems.
Statistics and Computational Finance Dissertation
60 CreditsA substantial piece of independent research on a selected topic in Statistics or Computational Finance, resulting in a dissertation of approximately 45 pages. You will receive weekly guidance from a specialist project supervisor throughout the summer period.
Elective Modules
Admission Requirements
The Statistics and Computational Finance MSc welcomes applicants with strong quantitative backgrounds. All applications are processed through Uni4Edu, where our team will guide you through every step of the process and ensure your application is complete and competitive.
Academic Requirements
- Undergraduate Degree2:1 (Upper Second Class) or international equivalent in Mathematics or a subject with a substantial mathematics component
- Alternative QualificationA 2:2 undergraduate degree may be accepted if supported by relevant professional qualifications
- Mathematical BackgroundStrong foundation in mathematics, including probability, statistics, and calculus
- Supporting DocumentsFull academic transcripts showing degree classifications, grades, and modules taken
- ReferencesAcademic references supporting your suitability for postgraduate study in statistics and finance
English Language Requirements
- IELTS (Academic)Overall 6.0, with minimum 5.5 in each component
- PTE AcademicEquivalent scores accepted; contact Uni4Edu for specific requirements
- TOEFL iBTEquivalent scores accepted; contact Uni4Edu for specific requirements
Required Documents
Application Deadlines
For personalized admission guidance, document verification, and application support, please contact Uni4Edu
Scholarships and Funding
The University of York offers a range of scholarships and funding opportunities for postgraduate students. Some are funded directly by the university, while others come from Research Councils, alumni, businesses, and charitable organisations. Contact Uni4Edu for personalised guidance on funding options available to you.
York Masters Opportunity Scholarship
A university-funded scholarship designed to support talented students pursuing taught masters programmes at the University of York. Eligibility criteria and award amounts may vary by year.
Department of Mathematics Scholarships
The Department of Mathematics may offer specific funding opportunities for outstanding postgraduate students. These awards recognise academic excellence and potential for research contribution.
External Scholarships and Studentships
Varies by awardAdditional funding is available through Research Councils, the Commonwealth Scholarship Commission, Chevening Scholarships, and other international funding bodies. Uni4Edu can help identify scholarships relevant to your nationality and profile.
For detailed tuition fee information, please contact Uni4Edu — we will guide you through the costs and available funding options for this program.
Career Prospects
Graduates of the Statistics and Computational Finance MSc are well-prepared for careers across finance, insurance, technology, and the public sector. The program's emphasis on data analysis and computational skills in Python and R makes graduates highly competitive in the growing field of quantitative analysis. Many graduates also choose to continue to PhD-level research.
Typical Career Roles
Sectors and Employers Hiring Graduates
Rankings and Recognition
The University of York is a prestigious Russell Group institution with consistently strong performance in national and international rankings. It is recognised for its research excellence and teaching quality, having achieved Gold in the Teaching Excellence Framework (TEF) and a top-ten position for research in the UK.
| Subject | Ranking Body | Rank |
|---|---|---|
| Mathematics (UK) | EduRank | #15 |
| Mathematics (World) | EduRank | #137 |
| Natural Sciences | QS World Rankings | #257 |
| Research Quality (REF) | Times Higher Education | Top 20 UK |
How to Apply
Applying for this program is easy with Uni4Edu. Our team will guide you through every step of the process — from document preparation to final enrolment.
Contact Uni4Edu
Reach out to our team via email or phone. We will assess your profile and confirm your eligibility for this program.
Prepare Your Documents
Our advisors will provide you with a personalised checklist of required documents and help you prepare your application package.
Submit Your Application
Uni4Edu will submit your application on your behalf and keep you updated on its progress throughout the review period.
Receive Your Offer
Once accepted, we will help you understand your offer, arrange visa support if needed, and guide you through the enrolment process.
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