Mathematical Statistics

About the subject

Do you want to learn how to make well-informed decisions, draw conclusions based on incomplete information and discover pattern in big data?

Mathematical statistics is the branch of mathematics that studies tools for describing randomness and uncertainty. It could be finding the genes that affect the risk a particular form of cancer, using historical data to predict climate change or decide how much capital an insurance company must reserve in order to be able to compensate customers in case of forest fires.

The foundation of the subject is the theory of probability, that while its history goes back to the 17th century, has developed in its modern form during the past century.

Probability theory also forms the foundation for theoretical statistics, the science of how to draw conclusions from data in the presence of randomness or uncertainty. Statistical theory is today the main tool for confirming scientific discovery in medicine and the natural sciences.

The development of computational tools has widened the scope of applications, both through techniques for simulating complex phenomena and the feasibility to collect and handle ever-increasing amounts of data. Here mathematical statistics play an important role in the development of tools for machine learning and artificial intelligence.

Career opportunities

There is a high demand for mathematical knowledge in society today. If you choose to focus on mathematical statistics and computer science, you will be attractive to most lines of business and research.

Courses and programmes

At Stockholm University, you can study mathematical statistics as individual courses at second cycle or by entering our master’s programme. First circle courses are in general only offered in Swedish.

Degree

Research

Active research areas at Stockholm University include probability and statistical theory with applications in many areas. Areas of particular interes include actuarial mathematics, biostatistics (in particular epidemic modelling and genetics), clmate modelling, econometrics, financial mathematics and stochastic networks.