Data Science with R - applied predictive modelling

Undergraduate course

Course description

Objectives and Content

The course presents various advanced methods within data science for predictive modelling and the use of R. Methods for regression, including non-linear regression and generalized additive models, and methods for classification, including trees, boosting and Support Vector Machines, will be examined. The course will focus on practical use in r, without going into details of the mathematical theory of the methods.

Learning Outcomes

On completion of the course the student should have the following learning outcomes:


  • Knows the basic ideas underpinning carious methods in data science/predictive modelling


  • Can implement various models within data science/predictive modelling in R
  • use data science methods on real data sets and perform predictions

General competence

  • have an overview of how data science methods can be used to analyze larger data sets

Level of Study

Required Previous Knowledge
Credit Reduction due to Course Overlap
Teaching and learning methods

Digital lectures and/or videos approx 2 hours pr. week.

computer lab / approx 2 hours a week for 9 weeks

Compulsory Assignments and Attendance
Needs 5 out of 9 apprived mandatory hand-ins.
Forms of Assessment
Digital home-exam 3 days
Grading Scale
Examination Support Material