Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £12,850
Overseas: £31,500
EU/EEA/Swiss students
What you'll study
This programme is divided in two parts.
In the first semester, you will complete the compulsory modules that provide a foundation for your career in finance. You will develop a detailed understanding of the financial industry as well as the mathematical, computational and data analysis techniques to work as a practitioner.
In the second semester, you will decide on your specialism. This part of the programme has been designed to prepare you for specific career paths, so what you study will depend on what you wish to do in the future.
Our specialisms, also known as streams, can be found below. We've highlighted the skills you will develop and the potential career paths for each stream.
Investment Management and Data Analytics Stream
You will learn to use mathematical and statistical techniques, as well as the latest technologies, to extract clear insights from financial markets and for other strategic operations.
Potential career opportunities: Financial services companies such as investment managers, investment banks, alternative asset managers, family offices, wealth managers, trading firms and fintech start-ups.
Investment Banking and Risk Management Stream
You will learn to use mathematical, statistical, and computational skills to assess, model, and hedge risk.
Potential career opportunities: Investment banks, investment management companies and financial consulting firms
Quantitative Pricing, Development and Research Stream
You will learn to use mathematical and statistical techniques as well as to implement complex financial models for derivatives pricing using a variety of programming languages.
Potential career opportunities: the more quantitative areas of banking and financial markets, such as financial computing and fintech. This stream is excellent preparation for academic research in financial mathematics.
Structure
In Semester A, you will take 4 Shared Core Modules.
- Financial Instruments and Markets
- Foundations of Mathematical Modelling in Finance
- Machine Learning with Python
- Programming in C++ for Finance
In Semester B, you can choose one of three streams:
Investment Management and Data Analytics Stream
- Advanced Machine Learning
- Digital and Real Asset Analytics
- Two additional elective modules from the list below
- Advanced Derivatives Pricing and Risk Management
- Digital and Real Asset Analytics
- Two additional elective modules from the list below
- Advanced Derivatives Pricing and Risk Management
- Advanced Computing in Finance
- Two additional elective modules from the list below
Full electives list
- Advanced Derivatives Pricing and Risk Management
- Advanced Machine Learning
- Advanced Computing in Finance
- Bayesian Statistics
- Bond Market Strategies
- Computational Statistics with R
- Continuous-time Models in Finance
- Credit Ratings
- Digital and Real Asset Analytics
- Financial Data Analytics
- Neural Networks and Deep Learning
- Private Equity and Venture Capital
- Risk Management for Banking
- Systematic Trading Strategies
Compulsory/Core modules
This module provides students with a wide-ranging knowledge of financial instruments and markets. It focuses on issues related to the role of a financial system, the functions of different types of financial institutions, and the understanding of financial products commonly traded in each financial market (including the equity market, money market, bond market and derivatives markets with applications using Excel/VBA). Additionally, you will gain an understanding of modern portfolio construction and management. This module will give you the practical knowledge that is essential for a career in investment banking or financial markets.
This module gives students a basis in probability theory needed for modelling asset price dynamics. You will start with a brief review of basic probability theory and then you will be introduced to stochastic processes that underlie many models in finance, such as random walks, Brownian motion, geometric Brownian motion, and Poisson process. You will also get an overview of Ito stochastic calculus and its applications to finance. By the end of this introductory course, you will have achieved a sufficient level of competence in mathematical methods to facilitate further studies in Mathematical Finance.
This module will introduce you to some of the most widely-used techniques in machine learning (ML). After reviewing the necessary background mathematics, we will investigate various ML methods, such as linear regression, polynomial regression, neural networks, classification with logistic regression, support vector machines and decision trees. The module covers a very wide range of practical applications, with an emphasis on hands-on numerical work using Python. At the end of the module, you will be able to formalise a ML task, choose the appropriate method to process it numerically, implement the ML algorithm in Python, and assess the method's performance.
This module will provide you with the necessary skills and techniques needed to investigate a variety of practical problems in mathematical finance. It is based on C++, the programming language of choice for many practitioners in the finance industry. You will learn about the basic concepts of the procedural part of C++ (inherited from the earlier C language), before being introduced to the fundamental ideas of object-oriented programming. The module is very 'hands on', with weekly sessions in the computer laboratory where you can put your theoretical knowledge into practice with a series of interesting and useful assignments.
Each MSc student is required to complete a 60 credit project dissertation. Project selection takes place in Semester B with work on the project starting thereafter and continuing through the summer. An MSc project should help prepare students for independent practical work and PhD research. A typical MSc project dissertation consists of about 30¿50 word-processed pages covering a specific research-level topic in pure or applied mathematics. The work usually requires the student to understand, explain and elaborate on results from research articles or analyse a dataset and may also involve programming and computation.
Elective modules
The module aims to introduce you to the Bayesian paradigm. The module will show some of the problems with frequentist statistical methods and demonstrate that the Bayesian paradigm provides a unified approach to problems of statistical inference and prediction. In the Module you will learn to make Bayesian inferences in a variety of problems, and apply Bayesian methods in real-life examples.
This module will introduce students to the elementary mathematics and analytics of investment for digital and real assets. This module will develop, from a practical approach, an understanding of the analytics of several asset classes that are currently included in investment portfolios, such as commodities, real estate, art and cryptoassets, and how these assets' statistical properties fit in the context of the portfolio. The module focuses on the concepts and characteristics of digital and real assets. It will introduce students to the mathematics of the Theory of Storage for commodities, the mathematics of indexes and uses in the real estate and art markets, trading algorithms, and cryptocurrency investment strategies such as staking, De-Fi, and non-fungible tokens. This module is particularly useful for students considering a career in financial mathematics, finance, investment management, investment banking, consultancy or asset management.
This module builds on the earlier module 'Machine Learning with Python', covering a number of advanced techniques in machine learning, such as different methods for clustering, dimensionality reduction, matrix completion, and autoencoders. Although the underlying theoretical ideas are clearly explained, this module is very hands-on, and you will implement various applications using Python in the weekly coursework assignments.
This module introduces you to several state-of-the-art methodologies for machine learning with neural networks (NNs). After discussing the basic theory of constructing and calibrating NNs, we consider various types of NN suitable for different purposes, such as recurrent NNs, autoencoders and transformers. This module includes a wide range of practical applications; you will implement each type of network using Python (and PyTorch) for your weekly coursework assignments, and will calibrate these networks to real datasets.
This module covers the advanced programming techniques in C++ that are widely used by professional software engineers and quantitative analysts & developers. The most important of these techniques is object-oriented programming, embracing the concepts of encapsulation, inheritance and polymorphism. We then use these techniques to price a wide range of financial derivatives numerically, using several different pricing models and numerical methods. On completion of this module, you will have acquired the key skills needed to apply for your first role as a junior 'quant' or software developer in a financial institution.
This module covers a number of advanced topics in the pricing and risk-management of various types of derivative securities that are of key importance in today's financial markets. In particular, the module covers models for interest rate derivatives (short-rate and forward-curve models), and looks at the multi-curve framework. It then considers credit risk management and credit derivatives (both vanilla and exotic). Finally, it also discusses credit valuation adjustment (CVA) and related concepts.
This module introduces modern methods of statistical inference for small samples, which use computational methods of analysis, rather than asymptotic theory. The techniques covered in the module include non-parametric tests, bootstrap, and cross-validation. Most of these methods are now used regularly in modern business, finance, and science. Finally, the module includes the implementation of all the proposed methods with the statistics software R.
This module will provide students with a general understanding of current applications of data analytics to finance and in particular to derivatives and investment banking. It will introduce a range of analytical tools such as volatility surface management, yield curve evolution and FX volatility/correlation management. It will also provide you with an overview of some standard tools in the field such as Python, R, Excel/VBA and the Power BI Excel functionality. Students are not expected to have any familiarity with coding or any of the topics above, as the module will develop these from scratch. It will provide you with the understanding of a field necessary to prepare for a career in finance in roles such as trading, structuring, management, risk management and quantitative positions in investment banks and hedge funds.
The use of systems for trading and investing has grown exponentially over the last twenty years, gradually replacing the discretionary judgement of human beings. This course will help you understand why systems have become so important in financial markets, and provide an overview of key concepts needed to understand and develop strategies for systematic trading and investing.
The module is designed to give an insight into the risk management process and how capital is allocated. We identify the main sources of risk experienced by financial institutions such as credit, market, liquidity, and operational risks. Methods for quantifying and managing risk are explored in detail with an emphasis on understanding factors affecting Value at Risk (VAR) calculations. Finally, we see how reporting standards, regulation and innovation have transformed the way financial institutions operate and what can we learn from recent risk management failures.
Bond markets are a critical part of the global financial system. This module explores global bond markets from a practitioner perspective. The module is designed to help students learn key bond market mathematics, identify value and understand the key risks. The module will explore how bond market strategies can be employed to capture value, create portfolios and meet specific investment objectives. The course also links core material with topical issues in global bond markets, showing students the critical importance of bond markets for the banking system, the wider financial system, the economy and government policymaking.
This module provides an overview of credit ratings, risk, analysis and management, putting considerable emphasis on practical applications. The module gives training to students and professionals wishing to pursue a career in credit trading, financial engineering, risk management, structured credit and securitisation, at an investment bank, asset manager, rating agency and regulator; as well as in other sectors where knowledge of credit analysis is required, such as insurance companies, private equity firms, pension, mutual and hedge funds. Further, it gives a unique set of perspectives on the recent developments following the financial crisis of 2007, and the intense criticism of the rating agencies and the banking industry.
Assessment
- 67% Modules
- 33% Research project
You will be assessed through a combination of:
- tests (some computer-based)
- written examinations
- coursework
- a final project and written dissertation - you may also be required to present your work and attend a viva (oral examination)
Research project
Examples of possible projects include:
- Pricing exotic options using the Kou jump-diffusion model
- The constant elasticity of variance model for pricing options
- Solving PDEs in computational finance with the alternating direction implicit (ADI) method
- Factor investing, smart beta and enhanced beta portfolios
- Portfolio Optimisation methods
- Switching processes in financial markets
- Pricing passport options
- Modern computational methods for derivative pricing
- Optimizing material properties with machine learning
- Fixed Income Trading and Macroeconomic Data
- Financial time series analysis
- Designing options with given risk profile
- The application of a 3-factor HJM model for pricing inflation-linked bonds
- Credit valuation adjustment (CVA) for interest rate swaps: Investigation of wrong-way risk using Monte Carlo / OpenCL
- The Heston model and its numerical implementation on a GPU using CUDA C/C++
- Jump-diffusion models for equity prices
- The LIBOR market model for interest rate derivatives
- Option pricing using finite-difference methods on CPUs and GPUs
Teaching
You will learn primarily through a combination of lectures and tutorials, in addition to a significant amount of independent study and research.
Modules which cover computer programming will be taught in our dedicated computing lab, which is equipped with Bloomberg terminals.
You are assigned an Academic Adviser who will guide you throughout your time at Queen Mary. The School of Mathematical Sciences also has a dedicated Student Support Officer to provide you with advice and guidance, with a focus on non-academic issues.
Where you'll learn
Facilities
- Our recently refurbished, £18m Mathematical Sciences building with high-quality teaching rooms, private and group study areas and a new social hub
- A shared office and dedicated computer lab with Bloomberg terminals for MSc students
- Library access to 8,000 mathematical books and subscriptions to a large number of mathematical journals
- On-campus accommodation for all new full-time postgraduate students from outside London
Campus
Teaching is based at Queen Mary’s main Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. As well as the Mathematical Sciences building, this includes the new Graduate Centre, providing 7,700 square metres of learning and teaching space.
The campus is 15 minutes by tube from Central London, where you will have access to many of the University of London’s other facilities, such as Senate House.
About the School
School of Mathematical Sciences
Research in the School of Mathematical Sciences covers a range of subjects in pure and applied mathematics, and is consolidated into research groups reflecting the School's key strengths.
We've invested approximately £18 million in our building to provide state-of-the-art facilities for staff and students. We hold an Athena Swan Bronze award.
School of Economics and Finance
- Around 1,000 master’s students from all over the world
- Teaching by research-active academics as well as visiting city professionals
- Ranked 4th in the UK for research output in economics and econometrics in the Research Excellence Framework in 2021
- Wide range of elective modules as well as professional development modules on programming languages, trading platforms such as Bloomberg, etc. (see Programme structure)
- Student investment fund (QUMMIF) provides practical skills in financial analysis and trading as well as opportunities to network with fellow students, academics, and city professionals
Career paths
You will be equipped to undertake a wide range of careers in the banking and finance sector in roles that require a high level of numeracy, problem-solving and computing expertise, as well as in marketing, public services, consultancy, industry and commerce.
Typical roles would be in areas such as quantitative analysis, software development, derivatives trading, risk management, investment management, sales, marketing and consultancy.
Students who have graduated from this programme have gone into a variety of roles including IT Audit Analyst, Quant Developer, Data Analyst, Quantitative Investment Strategist, Market Risk Associate, Quantitative Analyst, Tax Associate and more. Recent employers include EY, PwC, London Royal Asset Management, Macquarie Group, Spreadex and Schonfield.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £31,500
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Part-time study
September 2025 | 2 years
- Home: £6,450
- Overseas: £15,750
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A good 2:2 (55% or above) or above at undergraduate level in Mathematics or a subject with a strong Mathematics component such as Physics, Engineering or Computer Science .
Other routes
Applicants with a 2:2 degree (50% or above) will be considered on an individual basis, provided they can demonstrate substantial work experience in a relevant field.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught and research programmes in the School of Mathematics falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.