With study in practice and theory, you'll gain insight into analytics problems faced by businesses, governments, and nonprofits. On the practical side, you'll learn how to model a range of real-world problems using optimisation, stochastic simulation, and machine learning, using specialist software taught in tutorial sessions. On the theoretical side, you'll learn to recognise canonical underlying mathematical problems, and how to solve them with state-of-the-art methods. Courses are taught by faculty members with world-leading research profiles , who can provide insights that will give you a deeper understanding and a competitive edge.
In the first term, you'll learn the fundamentals of operations research and machine learning. In the second term, you can choose from a range of courses in mathematics, statistics, finance, and management. Course topics include algorithms and computation, optimisation, game theory, and further topics in machine learning and AI.
Your degree culminates in a consultancy project tackling a genuine challenge for one of our industry partners. Working directly with the organisation, you’ll apply the tools you’ve learned to a live business problem and deliver results with real-world impact. Past and present partners include Amazon, BT, British Airways, Emirates Airlines, FICO, Ford Motor Company, Just Eat, Legal and General, the National Audit Office, and Transport for London. As an alternative to the project, more theoretically minded students can write a dissertation supervised by a faculty member.
Our graduates have gone on to work in quantitative roles across a range of sectors, including consulting, finance, technology, transport and government. Employers have included McKinsey, BCG, Deloitte, Amazon, Huawei, Citi, UBS, Jane Street Capital, Airbus and British Airways, as well as public sector organisations such as Ofgem and Singapore’s Ministry of Defence. Other graduates have continued to PhD study at universities including Imperial College London and the National University of Singapore.
You're not required to do any preliminary reading in advance of this programme, but if you wish to read some material before arriving, we can make a few suggestions.
If you don't have experience of computer programming, you could learn Python, used in MA429 Algorithmic Techniques in Machine Learning, or R, used in MA424 Stochastic Modelling and Simulation. Once you learn any language it's easy to learn others, and programming will be useful in your career. Python will be introduced in a pre-sessional course but you can get a head start with Section 2.3 of the MA429 course textbook, freely available at Stat Learning .
There are also many resources listed at Wiki Python , including a free online tutorial . For R, there is the book Introductory Statistics with R by Peter Dalgaard, and a Coursera course . In MA423 Optimisation Theory and Modelling, you'll use the special-purpose mathematical programming language AMPL; for that, we recommend this quick introduction before the start of the course.
Linear algebra plays a major role in several key courses and in the field of OR generally. It's expected that you're comfortable with the basic notions (linear independence, rank, determinants, solutions of systems of equations, eigenvalues and eigenvectors). These will not be reviewed in the course; you can review this material independently. There are many good textbooks to choose from; a suitable one is Linear Algebra by Martin Anthony and Michele Harvey.
Discover more about our students and department.
The Department of Mathematics aims to be a leading centre for the study of mathematics in the social sciences.
The department has a vibrant intellectual community, with fantastic students, internationally respected academics and high-achieving alumni. Our department has grown rapidly in recent years, with exciting developments in research and new teaching programmes and courses.
This research encompasses four main overlapping areas:
All aspects of our research were ranked world-leading or internationally excellent in the most recent Research Excellence Framework (2021) , submitted jointly with the Department of Statistics.
We embrace the School’s ethos of research-led teaching. Currently, we offer four undergraduate and three postgraduate programmes, as well as doctoral research opportunities on our MPhil/PhD in Mathematics. These programmes are all in high demand – attracting talented students from diverse backgrounds.
An upper second class honours (2:1) degree in a relevant discipline (or equivalent). Students should normally have taken university courses including calculus, linear algebra, and statistics.
Relevant professional experience may also be taken into account.
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The English language requirement for this programme is Standard . Read more about our English language requirements .
Competition for places at LSE is strong. So, even if you meet the minimum entry requirements, this doesn't guarantee you an offer of a place.
However, please don’t feel deterred from applying – we want to hear from all suitably qualified students. Think carefully about how you can put together the strongest possible application to help you stand out.