Actuarial Science and Data Analytics MSc
Part of: Business and Management and Mathematical Sciences
The way actuaries work is changing. In today’s technology-driven world, you need advanced data analysis skills to effectively measure risk and make smart financial decisions.
With our masters, you’ll develop these skills, future proofing your career as an actuary. You’ll graduate with an excellent working knowledge of programming, machine learning and computational statistics.
You'll also get a head start on your professional qualifications. Our IFoA accredited programme will provide you with exemptions to all three Core Practices, CP1, CP2 and CP3.
- Learn from the experts! Our staff includes Actuarial Fellows, IFoA Independent Examiners, and specialists in data science.
- Set yourself up for long term career success! You’ll master key theoretical knowledge and in-demand data science skills.
- Graduate from an Institute and Faculty of Actuaries (IFoA) accredited programme.
- Earn exemptions from all IFoA Core Practices: CP1, CP2 and CP3.
Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £12,850
Overseas: £33,500
EU/EEA/Swiss students
What you'll study
This isn’t your traditional masters degree in Actuarial Science. We have developed a course that not only helps you qualify as an Associate Actuary, but also teaches you the in-demand data analysis skills that employers look for.
Our students come from a variety of different backgrounds, so we allow you to tailor the programme to your needs. Depending on your educational and professional background, what you choose to study might look a bit different. We’ve included a few of our typical pathways below:
I have prior training in Actuarial Science
If you’ve already completed all or most of the IFoA Core Principles, you can jump straight into studying for the Core Practices CP1, CP2 and CP3. This is what’s at the heart of this programme.
With the Actuarial Risk Management I and II modules, you will cover the same content as Actuarial Practice (CP1). After that, your final dissertation project will meet the requirements of Modelling Practice (CP2) and Communication Practice (CP3) from the IFoA syllabus.
I have no prior training in Actuarial Science
If you’re new to the subject, it is also possible to work towards selected exemptions from the IFoA Core Principles. Successful completion of our Actuarial Mathematics and Actuarial Statistics modules will provide you with exemptions to the IFoA’s CM2 and CS2 examinations respectively.
I am interested in developing data analysis skills
This is one of the standout features of our MSc. In addition to Actuarial Mathematics, we will also train you as a data analyst. Depending on your interests and background, you’ll choose from modules in financial engineering, asset and liability modelling, advanced machine learning, financial data analytics, neural networks and deep learning and computational statistics with R.
By developing essential skills in both actuarial science and data analytics, you’ll be ready to work in a wide variety of well-paid roles.
Professional recognition
This degree is currently accredited by the Institute and Faculty of Actuaries (IFoA). Successful students gain exemptions from up to three of the seven Core Principles examinations under the new IFoA Curriculum 2019.
Structure
- Four compulsory modules
- Four elective modules (chosen from 10)
- Final project and dissertation
Compulsory/Core modules
This module will start by providing an understanding of actuarial advice and how it can be used to meet the needs of stakeholders in both public and private institutions. The module will provide a deep understanding of the actuarial control cycles with their applications. We will also study risk governance, risk identification and classification, risk measurement and responses to risk. We consider scenario analysis, stress-testing and stochastic modeling in the evaluation of risk. The module will focus on capital management and monitoring and it will end with an overview of the general business environment.
This module will start by introducing students to contract design of financial products. We will consider the process of gathering and using appropriate data for recommending actuarial solutions. We will then move to modelling -we will learn how to analyse mortality and morbidity data, including factors that contribute to the variation in mortality and morbidity by region and in different social and economic environment. We will also study the cost and the pricing of providing benefits on contingent events. We will finally consider investment management (valuation of individual investments and valuation of portfolios of investments). The module also will provide understanding of the process of implementing and monitoring of actuarial solutions.
Each Actuarial Science and Data Analytics MSc student is required to complete a 60 credit project dissertation. Students may find a potential supervisor and fill out an Approval Form by the end of Semester B. The module organiser will support this process and ensure that all students are allocated supervisor and project. The supervisor and project must be approved by the Actuarial Science and Data Analytics MSc Programme Director, and the process for this, which may involve an interview with the student, takes place as approval forms are submitted. A typical MSc project dissertation consists of about 30 word-processed pages, covering a specific research-level, industry applied topic in Actuarial Science and Data Analytics. The dissertation will follow the CP2 and CP3 (Core Practices) syllabuses of IFoA examinations. The project will consist of two parts: modelling and communication. The modelling part ensures that the student is able to critically analyse and model commonly used data in actuarial work, maintaining an audit trail, using analytical and statistical methods (performing computation, simulations, or analysis) and generate innovative outputs . The communication part ensures that the student is able to communicate effectively in writing to both actuarial and non-actuarial audiences. An oral presentation of results may be required. The student usually will work on case studies in order to understand and explain actuarial questions. Results from one or more journal articles need to be applied. An MSc project may also involve collaboration with a collaborator based in industry. An MSc project should help prepare students for working as qualified actuaries and even following PhD research.
This module will present methods for time series analysis.These will allow the student to understand better how to use and extract information from historical business data series. In particular, the student will learn how to extract the pivotal concepts of time series data, including the trend and cyclic components of a data series, calculate the autocorrelation, learn about autoregressive and moving average models, and cointegration.The module will develop the notions around realistic business examples and an implementation of the methods will be provided using the statistic software R..
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.
Elective modules
The ability to store, manipulate and display data in appropriate ways is of great importance to data scientists. This module will introduce you to many of the most widely-used techniques in the field. The emphasis of this module is primarily on the interactive use of various IT tools, rather than on programming as such, although in a number of cases you will learn how to develop short programs (scripts) to automate various tasks.
This module introduces a selection of numerical methods for solving applied mathematical problems. One of the most fascinating aspects of physical theories is the fact that highly complex behaviour can arise from the repeated application of simple rules - consider for example the motion of bodies under gravity, which combine to give us the rich structure of our Universe. Such systems are the focus of much current research, and whilst they are too complex for an analytic approach, their numerical solution is (at least in principle) straightforward. The aim of the course is to introduce students to a selection of computational algorithms that are used to solve problems in applied mathematics, and to provide them with practical skills in software development that will be useful in many fields both within and outside of academic work. The module uses python as the main coding language. Some experience with python or similar coding languages will be an advantage but will not be assumed.
This module covers advanced techniques in financial engineering, which are essential if you want to pursue jobs in financial institutions. We first study the discrete-time binomial model for asset pricing, introducing some more formal concepts such as conditional expectations. Then we look at continuous time models, and use the tools of stochastic calculus to derive the Black-Scholes equation. We solve explicitly for the prices of European call and put options. We also consider some more advanced applications, such as models for stock prices involving jumps and stochastic volatility, as well as interest rate models.
This module introduces key concepts in financial economics and risk management. We will learn economic theories used by investors to determine their optimal portfolio of investment: utility theory, stochastic dominance, mean-variance portfolio theory, CAPM, factor models and arbitrage pricing theory. We consider next efficient market theory. We learn various tests for testing efficient market theory. We also introduce stochastic models for asset prices. Finally we study topics related to ruin/risk theory and look at how insurance companies estimate their liabilities using run-off triangles.
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 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.
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 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.
Assessment
- 67% Modules
- 33%
- You will be assessed by a mixture of formal examinations and coursework in your taught modules
- You will undertake more self-directed work in completing your final Actuarial Science and Data Analytics Project
Teaching
You will learn primarily through a combination of lectures and tutorials, in addition to a significant amount of independent study and research.
You will be 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 diverse range of subjects in pure and applied mathematics, and is consolidated into research groups reflecting the School’s key strengths.
We have invested approximately £18 million in our building to provide state-of-the-art research, teaching and study facilities for staff and students.
The University holds a university-level Silver Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in Higher Education and research. The School of Mathematical Sciences holds its own department-level Athena Swan Bronze award.
We are a registered Supporter of the LMS Good Practice Scheme.
In the most recent Research Excellence Framework (REF 2014), Queen Mary ranked ninth in the UK among multi-faculty universities and fifth for our percentage of 3* and 4* research outputs.
Career paths
With this qualification, you can go beyond traditional actuarial roles such as insurance and pensions. You will be able to work across a variety of industries, including health, investment banking and climate risk.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £33,500
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: £16,750
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 2:1 or above at undergraduate level in actuarial science, mathematics, statistics, econometrics, mathematical economics, finance, or engineering.
Other routes
Applicants with a good 2:2 degree (55% or above) will be considered on an individual basis.
Other education backgrounds can be considered subject to demonstrating satisfactory knowledge of actuarial science, mathematics, statistics, finance or engineering.
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.