MSc Operations Research and Analytics

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.

You'll undertake a final project where, working in a consultancy role and using the tools you have learned in the degree, you'll tackle a real problem faced by a partner organisation. 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.

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.

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:

• discrete mathematics
• mathematical game theory
• financial and related mathematics
• optimisation and algorithms.

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.

Our programmes are highly interdisciplinary and we have close ties with other departments at LSE, including Statistics, Economics, Finance, Management and the Data Science Institute.

Whatever your study route, you’ll benefit from a welcoming, inclusive and friendly learning environment where students and staff are supported to achieve their best.

Learn more about our programmes, recent research and regular events and seminars.

Department of Mathematics

We welcome applications from all suitably qualified prospective students. At LSE, we want to recruit students with the best academic merit, potential and motivation, irrespective of background.

We carefully consider each application and take into account all the information included on your application form, such as your:

• academic achievement (including predicted and achieved grades)
• statement of academic purpose
• two academic references
• CV.

See further information on supporting documents.

You may need to provide evidence of your English language proficiency. See our English language requirements.

Applications for this programme are considered on a rolling basis. This means that applications will close once the programme is full.

There is no fixed deadline. However, if you’d like to be considered for any funding opportunities, you must submit your application (and all supporting documents) by the funding deadline. See the fees and funding section below for more details.

For this programme, the tuition fee is different for home and overseas students depending on their fee status.

For this programme, the tuition fee is different for home and overseas students depending on their fee status.

All taught courses are required to include formative coursework which is unassessed. It is designed to help prepare you for summative assessment which counts towards the course mark and to the degree award. LSE uses a range of formative assessment, such as essays, problem sets, case studies, reports, quizzes, mock exams and many others. Summative assessment may be conducted during the course or by final examination at the end of the course. An indication of the formative coursework and summative assessment for each course can be found in the relevant course guide.

This programme is ideal preparation for a range of careers in quantitative positions in consultancy, management, finance, government and business, anywhere in the world.

Further information on graduate destinations for this programme

This programme is ideal preparation for a range of careers in quantitative positions in consultancy, management, finance, government and business, anywhere in the world.

Here's a sampling of where recent graduates have found placements:

Consultancy: Accenture, BCG, Deloitte, EY, PwC, McKinsey, and specialised consultancies including Bain & Company, Decision Point Analytics, LET Consulting, and Turner & Townsend.
Government: Ofgem (UK energy regulator), Ministry of Defence (Singapore).
Business: Ali Baba, BMW group, BT group, Dish Network, Johnson & Johnson, Just Eat, Procter & Gamble, Renault group, Ocado, Red Bull, Samba TV.
Transportation: Airbus, British Airways, DiDi, Emirates, Laskarides Shipping, Ryanair.
Tech: Amazon, Ant Group, BT group, China Telecom Europe, Databricks, Flexciton, Huawei, Satalia, Yahoo Japan.
Banking and finance: Central Bank of the Bahamas, CiBanco, Citi, Credit Suisse, CITIC Securities, FinTru, Jane Street Capital, Guodu Securities, Point72 Asset Management, Shanghai Pudong Development Bank, UBS.
PhD study: Goethe University Frankfurt, Imperial College London, Monash University Malaysia, National University of Singapore, University of Warwick.

From CV workshops through to careers fairs, LSE offers lots of information and support to help you make that all-important step from education into work.

Many of the UK’s top employers give careers presentations at the School during the year and there are numerous workshops covering topics such as job hunting, managing interviews, writing a cover letter and using LinkedIn.

See LSE Careers for further details.