MSc Statistics and Computational Finance
Explore contemporary statistical methodology and related data analysis
Year of entry: 2025 (September)
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.
Learn more about the study of finance at York.
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.
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 Python and R languages. Additionally, you'll choose an option module and dissertation topic that are aligned with your own interests, resulting in a flexible programme tailored to meet your needs.
Modules
Core modules
- Survival Analysis and Generalised Linear Models
- Statistical Pattern Recognition
- Time Series
- Multivariate Data Analysis
- Computational Finance with Python
Option modules
You will study one option module. Examples can be found below. Some option module combinations may not be possible. The options available to you will be confirmed after you begin your course.
- Decision Theory and Bayesian Statistics
- Mathematical Methods of Finance
- Mathematical Finance in Discrete Time
Our modules may change to reflect the latest academic thinking and expertise of our staff, and in line with Department/School academic planning.
Learning outcomes
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.
Learning outcomes for this course
- Use, with a high level of confidence and sophistication, the appropriate modern statistical (incl. probabilistic) methodology and associated tools that underpin a wide range of applied problems, particularly in finance, big data analysis but also more generally in science and industry.
- Recognise and critically evaluate different statistical (incl. probabilistic) methods in order to find a suitable strategy for solving an unfamiliar problem open to investigation.
- Use logical reasoning as a basis for the critical analysis of ideas or statements which have a statistical and financial context, and develop independently their own ideas using well-founded reasoning.
- Independently conduct a piece of applied research in a relevant specialised area, for example take into account recent statistical methodology, apply it and interpret conclusions on real data sources.
- Communicate advanced statistical and mathematical analyses and associated conclusions clearly, in writing or in a presentation, at a level appropriate for the intended audience.
- Create mathematical documents, presentations and computer programmes by accurately and efficiently using a range of digital technologies and programming tools.
Fees and funding
Annual tuition fees for 2025/26
| Study mode | UK (home) | International and EU |
|---|---|---|
| Full-time (1 year) | £16,810 | £33,700 |
Students on a Student 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 2025/26 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
Chevening Scholarships
We are pleased to work with Chevening Scholars to offer funding for our Masters programmes. Chevening Scholarships provide one year of fully-funded postgraduate study in the UK for international (including EU) students. The scholarships are open to early and mid-career professionals who have the potential to become future leaders.
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. This includes access to our short videos designed to reinforce your knowledge on certain topics, and are accompanied by a set of dedicated study notes.
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.
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
| Qualification | Typical offer |
|---|---|
| Undergraduate degree | 2:2 or equivalent in Mathematics or in a subject with a substantial mathematics component. |
| 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:
| Qualification | Minimum requirement |
|---|---|
| IELTS (Academic and Indicator) | 6.0, minimum 5.5 in each component |
| Cambridge CEFR | B2 First: 169, with no less than 162 in each component |
| Oxford ELLT | 6, minimum of 5 in each component |
| Oxford Test of English Advanced | 126, minimum 116 in each component |
| Duolingo | 105, minimum 95 in all other components |
| LanguageCert SELT | B2 with 33/50 in each component |
| LanguageCert Academic | 65 with a minimum of 60 in each component |
| Kaplan Test of English Language | 444-477, with 410-443 in all other components |
| Skills for English | B2: Pass with Merit overall, with Pass 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 haven't 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.
Next steps
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