Master's Programme in Statistics - Statistical Modelling and Data Science

The programme is focused on the analysis of data with a foundation in statistical theory and gives you the tools needed to find patterns in large and complex data sets, or for more accurate statistical modeling with the aim of identifying causal relationships. You get a good foundation for a career in data science and as a statistician where you are well equipped to drive the design of statistical surveys and make your own contributions to statistical method development. Perhaps you want to pursue a PhD in statistics? Even then, the programme is something for you! The programme is offered in English and you study with people from different parts of the world, all with a common focus on a career in Statistics or Data Science. Your teachers also have extensive professional experience and a wide network of contacts, internationally within academia and with employers in Sweden. As the only university in Sweden – and one of only two universities in the Nordic countries – Örebro University has been awarded the European Master in Official Statistics (EMOS) label by Eurostat. The European Master in Official Statistics is offered as an elective option in the Master’s Programme in Statistics. The option includes a 15 credit internship course at an agency in the official statistics field. The internship and the course work in the EMOS options provide students with the qualifications needed for a career in official statistics – in Sweden or internationally. The training emphasizes the ability to communicate and collaborate, partly to identify the organization's information needs and the prerequisites for the statistical survey or data analysis, partly to be able to clearly communicate the selected approaches and results. The programme of study is two years long and leads to a master’s degree (2 years) but there is also an option to exit after one year with a master’s (1 year) degree. The programme is delivered during four semesters each consisting of 30 ECTS credits of course work. During the first fall semester, the foundation is laid with courses in data visualization, programming in languages such as R and Python, database management and courses in mathematics, probability theory and inference theory. The second (spring) semester begins with courses in econometrics and computer-intensive methods and ends with a thesis (15 ECTS credits). In the second year, the fall semester consists of courses in machine learning, Bayesian statistics and survey sampling theory as well as 15 ECTS credits of optional courses. During the fourth semester, there is an opportunity to do an internship, alternatively optional courses are taken. The semester ends with a second thesis (15 ECTS credits).