London School of Economics and Political Science campus

Engineering

MSc Stochastic Finance and Machine Learning at London School of Economics and Political Science

London School of Economics and Political Science, United Kingdom

Programme overview

Explore the modern world of quantitative finance and build lasting expertise in stochastic modelling, statistics and machine learning. The MSc Stochastic Finance and Machine Learning is a technically demanding programme in quantitative finance. Firmly rooted in practice, it trains you for the challenges that matter in today's financial markets and prepares you for where the industry is heading next.

Department: Department of Statistics  |  Duration: 10 months full-time; 22 months part-time  |  Study mode: full-time/part-time  |  Intake: Mandatory pre-sessional course begins in early September 2027  |  Location: London, England, United Kingdom

What you will study

Compulsory and core courses include September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management), Stochastic Processes, Statistical Methods for Risk Management, Stochastic Simulation, Training, and Calibration.

Career pathways

Graduates have gone on to work in various areas of finance, including quantitative roles at investment banks, asset managers and hedge funds. Others have built careers in insurance and auditing as well as in consulting and data science.

Official programme page

Application information

Entry requirements

Upper second class honours (2:1) degree or equivalent in mathematics, statistics, actuarial science, mathematical economics/finance or other highly quantitative subject such as physics and engineering. English language requirement: Standard (IELTS 7.0 overall with 6.5 in each component).

Domestic applicants: GBP 23,100 for the whole programme (2027/28) Source

International applicants: GBP 41,900 for the whole programme (2027/28) Source

Intake: Mandatory pre-sessional course begins in early September 2027

Duration: 10 months full-time; 22 months part-time

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