Do you want to hone your analytical powers, learn to process reams of data and make this data sing? In this digital age, with an explosive growth in both the amount of data collected and the computational power at our disposal, there is enormous demand across all segments of the economy for highly trained statisticians. Our programme will stretch your mind, increase your statistical prowess and equip you with a solid theoretical foundation for tackling the data-driven challenges of the future.
You will be part of a human-sized Master's programme hosted by the Department of Mathematics and Mathematical statistics. The small-scale of the programme allows for individualised learning, and for richer and more personal interactions between students and teachers, and is one of its main attractions. All incoming Master students are assigned a mentor from one of our research groupse. The mentor follows the student through the programme, giving advice on the choice of courses and thesis topics, as well as on applications to PhD programmes.
Beyond the programme's core courses and thesis projects, there is a flexibility about replacong programme courses with elective courses from closely related fields such as mathematics and computer science in order to tailor your degree to your interests and career plans.
Teaching on the programme consists of a mixture of traditional lectures, seminars and individual supervision on the one hand, and of extensive courses and projects based in a computer lab setting on the other hand, in which one learns to master advanced data analytics and statistical software (R, Minitab, SPSS, ...). All teaching on the programme is done in English and by active researchers with a long experience of statistical analysis and statistical consulting.
Should you wish to, you also have the opportunity of taking part in a semester-long exchange abroad at partner mathematical statistics departments in Germany, Italy, France, Spain, the UK, the Czech Republic China, South Korea and Kazakhstan as part of your studies.
The programme's greatest draw is its close connection to the life of the research groups in the department, which we hope you will take full advantage of. As a student on the programme, you will be able to work on research and thesis projects with some of our highly successful researchers, and to use the mentoring system as a gateway into the world of research.
Living as we do in the age of the data-driven economy, statistical skills are in high demand on the labour market. In addition some of our core courses (and much of our research) lie in exciting "hot topic" areas such as machine learning, artificial intelligence, network science and big data. The employment prospects of our graduates are thus excellent.
Finally, Umeå provides a highly attractive environment for studying. It is a young and fast growing city of around 120 000 inhabitants, of whom over 35 000 are students. This makes for a rich cultural life, with many pubs, cafés, restaurants, concerts and festivals. The city is home to Norrlands Opera and to Bildmuseet, a museum of contemporary art and visual culture. In 2014, Umeå shared with Riga the distinction of being the European capital of culture.
The city is small, extremely safe and well-run, with an extensive network of bicycle paths allowing its denizens to cycle all year round. It is surrounded by beautiful nature – from the Umeå river, which freezes over in winter and becomes criss-crossed with cross-country skiing tracks, to Nydala lake and the Gammlia forest within the city limits, and to the vast and largely unspoiled countryside of forests, lakes and seashore just outside it. And of course studying in Umeå allows you to experience the Swedish way of life, with its well-delineated seasons and traditions, from Sankta Lucia in the dark of December to Midsommar in the overflowing light of June, with coffee and cinnamon buns throughout.
Read more about the department and Umeå University
Courses in Mathematical Statistics:
Statistical learning with high dimensional data analysis, 7.5 credits
Design of Experiments and Advanced Statistical Modelling, 15 credits
Inference Theory, 7.5 credits
Multivariate Data Analysis, 7.5 credits
Probability Theory, 7.5 credits
Research in the Mathematical Sciences, 7.5 credits
Current Research in Statistics, 7.5 credits
Stationary stochastic processes,7.5 credits
Time Series Analysis and Spatial Statistics, 7.5 credits
Courses in Statistics:
Programming in Statistics, 7.5 credits
Causal Inference, 7,5 credits
There is a lot of freedom to mix and match both statistical courses from the programme and relevant courses from mathematics, statistics and computer science: if there is an exciting course that you want to sub in for one of the programme courses listed above, there is considerable latitude to do so.
The elective courses listed below, most of which are from our sister programme in Mathematics, are alternatives you may want to consider. Your mentor will help and advise you on choosing a tailor-made set of courses from the available options.
Elective courses in Mathematics:
Enterprise Risk Management, 15 credits
Financial Mathematics, 7 credits
Monte Carlo Methods for Financial Applications, 7.5 credits
Stochastic Differential equations. 7.5 credits
A Mathematical Introduction to Machine Learning, 7.5 credits
Elective courses in Mathematical Statistics:
Statistics in Genetics, 7.5 credits
Computer Intensive Methods in Statistics
Thesis Project for the Degree of Master of Science in Mathematical Statistics, 15 credits
Thesis Project for the Degree of Master of Science in Mathematical Statistics, 30 credits
There is a lot of flexibility to mix and match both statistical courses from the programme and relevant courses from mathematics and computer science. Your mentor will help and advise you on choosing a tailor-made set of courses from the available options.
Students on the programme may also replace suggested courses with courses from the sister programme in Mathematics.
Graduates of a master's programme in statistics have a wide range of opportunities available to them. Statistical skills are highly sought-afterin the tech industry, in finance and the insurance sector, in the pharmaceutical industry and in medical research more generally, in many government agencies, and in any context where large reams of data are to be analysed. And of course a master's degree is a natural and necessary preparation for entering a PhD programme and pursuing a career in statistical research within academia.