Home > Department of Statistics

Department of Statistics


Department of Statistics
Columbia House
London School of Economics
Houghton Street


Online query form


BSc Queries

+44 (0)20 7955 7650


MSc Queries

+44 (0)20 7955 6879 

MSc Frequently Asked Questions


MPhil/PhD Queries

+44 (0)20 7955 7511









Welcome to the Department of Statistics at the London School of Economics & Political Science. The department enjoys a vibrant research environment and offers a comprehensive programme of undergraduate and postgraduate degrees in Statistics.
The department offers degree courses at undergraduate, taught postgraduate and research level.

Undergraduate programmes offer an opportunity to build on your mathematical skills and apply them to areas such as insurance, banking, finance and statistics.

The MSc programmes provide students with intensive training in statistics applicable to the social sciences, econometrics, finance and insurance.

The department welcomes applications for the MPhil/PhD in Statistics from suitably qualified candidates. Full details of departmental research interests are available on our Research pages.
Undergraduate (BSc)

The department has three undergraduate degrees that involve the applications of statistics to the social sciences and include a range of statistical and mathematical subjects.

BSc in Actuarial Science
BSc in Business Mathematics and Statistics
BSc in Financial Mathematics and Statistics - NEW IN 2017/18!

Postgraduate Taught (MSc)

Our taught postgraduate courses are based around lectures, with problem classes and computer workshops. Most courses are assessed by a two-hour exam, either mid-term or in the summer term, although some contain an element of course work.

The MSc in Statistics provides students with intensive training in statistics applicable to the social sciences, econometrics and finance.The aim of the programme is to foster an interest in applied statistics and equip students for work as professional statisticians.

The MSc also provides an opportunity to study specialist courses in related disciplines. There are excellent prospects for employment and further study for our graduates. Former MSc Statistics students have taken up positions in consulting firms, banks and in the public sector where there is a shortage of well-qualified statisticians. Many go on to take higher degrees.

The Financial Statistics stream of the MSc Statistics programme is mainly intended for students wishing to pursue careers in the finance industry or as a stepping stone towards PhD study in statistics for finance.  Students will receive a thorough grounding in the theory and methods of statistical inference and the statistical analysis of time series, as well as a selection of important concepts in statistical finance, such as financial time series, asset pricing and portfolio choice, and some aspects of continuous-time finance. Students will also learn to code in the R statistical computing environment. Optional modules from the Statistics, Finance and Economics departments (among others) are available.

The research branch both the MSc Statistics and MSc Statistics (Financial Statistics) programmes are open to students wishing to take the dissertation option.

The MSc Quantitative Methods for Risk Management – formerly known as MSc Risk and Stochastics - offers in-depth instruction in probabilistic, statistical, and computational methods to quantify risk arising from, but not limited to, economic, financial, and insurance applications. This programme is LSE’s timely response to industry’s strong demand in experts with quantitative expertise in risk management, finance, insurance, and their interface.
This programme will instruct students in theoretical as well as practical aspects of various quantitative methods to measure and mitigate financial and insurance risk. It draws on diverse disciplines, from mathematical finance, actuarial science to statistics and computation. You will work with real financial data to receive hands-on training in real-world problems and case studies. This programme draws on world class research in modern financial and actuarial mathematics and statistics within the Department.

The MSc in Data Science provides training in data science methods, with a focus on statistical perspectives. You will receive a thorough grounding in theory, much of it at a high mathematical level, as well as gain practical skills of applied data science, enabling you to apply advanced methods of data science and statistics to investigate real world questions.


Postgraduate Research (MPhil/PhD)

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics. Our core research areas are social statistics, time series and statistical learning and risk and stochastics in insurance and finance.

Research programmes are designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of a particular area.

MPhil/PhD in Statistics

The Department of Statistics offers a comprehensive programme of seminars throughout the year, including the statistics seminars series, joint statistics and econometrics seminars and the London Mathematical Finance seminars. You can find further details here, along with an archive of our past seminars and details of workshops, conferences and other special events.





Research in Probability in Finance and Insurance 

Our research in probability in finance and insurance covers diverse aspects in quantitative modelling in finance, insurance, and risk management. Current areas include robust models on option pricing; model-uncertainty in decision making; valuation financial derivatives with exotic features; equilibrium with market constraints and informational asymmetry; optimal trading with micro-structure noise; insurance securitisation; contagion in financial and insurance markets; modelling energy and commodity markets.

The current members of the Probability in Finance and Insurance group are Beatrice Acciaio, Pauline Barrieu, Erik Baurdoux, Luciano Campi, Umut Cetin, Angelos Dassios, Kostas Kardaras and Hao Xing.

Research in Social Statistics

Research in social statistics is concerned with the development of statistical methods that can be used across the social sciences. Statisticians play an essential role in all aspects of social inquiry, including: study design; measurement; data linkage; development of statistical models that account for the complex structure of social data; model selection and assessment.

Members of the Social Statistics group have interest in statistical methods in each of these areas and regularly collaborate with social scientists whose questions motivate new lines of methodological research. We have experience in a range of social science disciplines, including demography, education, epidemiology, psychology and sociology, and psychology.

The current members of the Social Statistics group are Wicher Bergsma, Sara Geneletti, Kostas Kalogeropoulos, Jouni Kuha, Irini Moustaki, Chris Skinner and Fiona Steele.

Research in Time Series and Statistical Learning

The Department's research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

The current members of the Time Series and Statistical Learning group are Matteo Barigozzi, Yining Chen, Piotr Fryzlewicz, Kostas Kalogeropoulos, Clifford Lam, Xinghao Qiao, Leonard Smith and Qiwei Yao. 

The Centre for the Analysis of Time Series (CATS)

The centre aims to ddress the question of data analysis using both physical insight and the latest statistical methods; focus on non-linear analysis in situations of economic and physical interest, such as weather forecasting; promote awareness of limitations of non-linear analysis and the danger of blindly transferring well-known physics to simulation modelling; focus on end-to-end forecasting, taking account of current uncertainty about the state of the system, model inadequacy and finite computational power.

CATS is led by Director Leonard Smith, with Chair Henry Wynn and Co-Directors Pauline Barrieu, Roman Frigg and David Stainforth.