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.
The information below is only for exchange students
Starts
20 March 2024
Ends
18 April 2024
Study location
Umeå
Language
English
Type of studies
Daytime,
50%
Required Knowledge
Statistics, Basic Level 30 ECTS credits, or similar knowledge
Selection
Students applying for courses within a double degree exchange agreement, within the departments own agreements will be given first priority. Then will - in turn - candidates within the departments own agreements, faculty agreements, central exchange agreements and other departmental agreements be selected.
Application code
UMU-A2793
Application
This application round is only intended for nominated exchange students. Information about deadlines can be found in the e-mail instruction that nominated students receive.
The application period is closed.
