Introduction to data science 3 credits
About the course
The course gives an introduction to data science with emphasis on the essential part of data science that consists predictive modelling. Predictive modelling aims to generate predictions based on historical data. In addition to parametric predictive models, such as linear regression and logistic regression models already known from the course Statistik A, some non-parametric predictive models, such as K-nearest neighbors models, are introduced during the course.
Regardless of which kind of predictive models that is used, it is of key importance to evaluate the accuracy of the predictions. Ways to evaluate predictions are therefore also introduced during the course.
As predictive modelling, more and more regularly, are used in all parts of society and as a basis for decisions it is also necessary to be aware of that, similar to human decisions, algorithms can also be subject to bias and errors. Thus, there are crucial ethical considerations that must be reflected on when doing data science and predictive modelling. During the course this is problematized.
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Spring 2026
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Introduction to data science
Second admissions round for EU/EEA citizens
Spring 2026 / Umeå / English / On site
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Starts24 March 2026
Ends21 April 2026
Number of credits3 credits
Type of studiesOn site
Study pace50%
Teaching hoursDaytime
Study locationUmeå
LanguageEnglish
Application codeUMU-27922
Eligibility Univ: 7.5 ECTS in Statistics, with linear regression and logistic regression, or similar knowledgeSelectionAcademic credits
ApplicationOnline applications are now open. Application deadline is 15 October 2025. Please note: This second application round is intended only for EU/EEA/Swiss citizens.
Application and tuition feesAs a citizen of a country outside the European Union (EU), the European Economic Area (EEA) or Switzerland, you are required to pay application and tuition fees for studies at Umeå University.
Application fee: SEK 900
Tuition fee, first instalment: SEK 5,905
Total fee: SEK 5,905
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How to apply
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