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MSc Statistics and Computational Finance

Explore contemporary statistical methodology and related data analysis

Year of entry: 2022 (September)


1 year full-time

Start date

September 2022 (term dates)

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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.

Research excellence

In the 2014 Research Excellence Framework assessment, 80 per cent of research activity in the Department of Mathematics was designated 'world-leading' or 'internationally excellent'

Course content

This course will equip you with the necessary skills to:

  • translate problems from the workplace into contemporary statistical ideas and methodologies
  • solve problems using your advanced knowledge in statistical modelling and computational finance
  • interpret and communicate your results.

Unique to our degree, you can choose one of two distinct pathways. Pathway A provides a thorough mathematical grounding in the theory of option pricing, while Pathway B provides a simpler introduction to the same material.

The core modules we offer will give you a strong grounding in statistical data analysis and modelling techniques, as well as much-needed computational skills required by finance-based employers, such as C/C++ and R languages. Additionally, you'll choose option modules that are aligned with your own interests, resulting in a flexible programme tailored to meet your needs.


Core modules

Core modules may include:

Core modules (both pathways):

Additional core modules (pathway A):

Additional core modules (pathway B):

Option modules

Options may include

Option modules (pathway A)

Option modules (pathway B)

Our modules may change to reflect the latest academic thinking and expertise of our staff.
Our course structures are changing in September 2023. Find out more about how this course may be affected.


Your Masters will culminate in a dissertation on a selected topic in Statistics or Computational Finance.

It will be a piece of independent work that you complete over the summer. You will have guidance from a project supervisor with weekly supervision, scheduled at a time to suit you.

Our recent students' dissertations have investigated topics including:

  • Feature Selection in Trading Behaviour in Financial Markets - A Big Data Analysis
  • Modelling the UK and USA GDP Data: Estimation and Prediction
  • The day of the week effect in different countries' stock markets

The York approach

Every course at York is built on a distinctive set of learning outcomes. These will give you a clear understanding of what you will be able to accomplish at the end of the course and help you explain what you can offer employers. Our academics identify the knowledge, skills, and experiences you'll need upon graduation and then design the course to get you there.

Students who complete this course will be able to:

  • Translate problems from the applied workplace into the appropriate contemporary statistical ideas and methodologies
  • Use advanced knowledge in statistical modelling and computational finance to provide solutions to these problems
  • Interpret and communicate results
  • Use common and advanced computational finance methods and statistical methodologies which are applicable not only to finance but also to other scientific areas where data analysis is needed
  • Carry out data analysis, statistical modelling in the particular topic selected in the dissertation; the interrelations between different methods and models in finance, together with experience of their use through dissertation
  • Use commonly used statistical techniques and software to do data analysis, make sensible investments in financial markets, provide consultancy in financial investments, and effectively communicate independent works in both oral and written form
  • Use advanced statistical techniques and software to do data analysis, make use of advanced computational finance methods to plan and make sensible and calculated investments in financial markets
  • Critically evaluate different statistical and/or econometric methods to find a suitable one for a given circumstance. Students should also be able to formulate and solve a small research problem and gain the skills of data assembly and analysis, and of writing up and presenting a substantial research report


Fees and funding

Annual tuition fees for 2022/23

Study modeUK (home)International and EU
Full-time (1 year) £13,940£26,930

Students on a Student Visa (formerly Tier 4 Visa) are not currently permitted to study part-time at York.

Fees information

UK (home) or international fees? The level of fee that you will be asked to pay depends on whether you're classed as a UK (home) or international student. Check your fee status

Find out more information about tuition fees and how to pay them.

Funding information

Discover your funding options to help with tuition fees and living costs.

We'll confirm more funding opportunities for students joining us in 2022/23 throughout the year.

If you've successfully completed an undergraduate degree at York you could be eligible for a 10% Masters fee discount.

Funding opportunities

Living costs

You can use our living costs guide to help plan your budget. It covers additional costs that are not included in your tuition fee such as expenses for accommodation and study materials.

University of the Year shortlisted

“York is everything an outstanding university should be”

Find out how we made the shortlist for University of the Year in The Times and The Sunday Times Good University Guide 2022.

Our University of the Year nomination

Teaching and assessment

You’ll work with world‐leading academics who’ll challenge you to think independently and excel in all that you do. Our approach to teaching will provide you with the knowledge, opportunities, and support you need to grow and succeed in a global workplace.

Teaching format

Our teaching is informed by the latest research, meaning you can focus on the latest ideas and models.

We use a wide range of teaching methods to suit different learning styles including:

  • Lectures
  • Problem classes
  • Seminars
  • Tutorials

For some modules you may also attend practical classes, computer laboratories or workshops.

Lectures are used to describe new concepts you will have to learn and problems classes put them into practice. Seminars are small, interactive sessions which allow us to focus on your individual needs. You'll be able to use our Virtual Learning Environment to supplement lectures and seminars.

While you're working on your project and your dissertation you'll have regular meetings with your academic supervisor who will offer advice and support. We will give you a supervisor with specialist knowledge of the area you're investigating.

Teaching location

You will be based in the Department of Mathematics in James College on Campus West. Most of your small group teaching will take place in the Department's dedicated MSc seminar room (the Dusa McDuff room), with larger classes taking place close by in James College, Derwent College and elsewhere on Campus West.

About our campus

Our beautiful green campus offers a student-friendly setting in which to live and study, within easy reach of the action in the city centre. It's easy to get around campus - everything is within walking or pedalling distance, or you can always use the fast and frequent bus service.

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 general 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.

Tutor talking to a student during exam
Maths student at desk looking upwards

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

Transferable skills

  • Confidence with high-level financial statistical analysis
  • Logical thinking
  • Analysis of problems
  • Problem-solving
  • Flexible thinking, the ability to learn and apply complex ideas quickly and precisely
  • Digital literacy
  • Time management
  • Communication skills
  • Research skills

Our computational skills are transferable and employers recognise the value of the programming knowledge you will have developed to a high level during this course.

Entry requirements

Typical offer
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.
International pre-masters programme Pre-masters from our International Pathway College
Other international qualifications Equivalent qualifications from your country

English language

If English isn't your first language you may need to provide evidence of your English language ability. We accept the following qualifications:

Minimum requirement
IELTS 6.0, minimum 5.5 in each component
C1 Advanced and C2 Proficiency 169, minimum 162 in each component
Duolingo 100, minimum 90 in all other components
LanguageCert B2 Communicator High Pass with 33/50 in each component
PTE Academic 55, minimum 51 in each component
TOEFL 79, minimum 17 in Listening, 18 in Reading, 20 in Speaking and 17 in Writing
Trinity ISE III Pass in all components

For more information see our postgraduate English language requirements.

If you've not met our English language requirements

You may be eligible for one of our pre-sessional English language courses. These courses will provide you with the level of English needed to meet the conditions of your offer.

The length of course you need to take depends on your current English language test scores and how much you need to improve to reach our English language requirements.

After you've accepted your offer to study at York, we'll confirm which pre-sessional course you should apply to via You@York.


You can apply and send all your documentation electronically through our online system. You don’t need to complete your application all at once: you can start it, save it and finish it later.

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Next steps

Contact us

Get in touch if you have any questions

Dr Marina Knight

Learn more

Department of Mathematics

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