Facts about the course

ECTS Credits:
Responsible department:
Faculty of Logistics
Course Leader:
Berit Irene Helgheim
Lecture Semester:
Teaching language:
½ year

LOG904-154 Data Mining (Autumn 2020)

About the course

The aim of the Seminar is to provide the students with the foundations of Data Mining, either Statistical or of Machine Learning. It covers the basic methodologies of multivariate data analysis and modelling, which constitute the core mainstreams for Data Mining.


Topics covered: Introduction to Data Mining. Principal Component Analysis. Clustering techniques. Profiling. Logistic regression. Decision trees. Association rules

The course is connected to the following study programs

Recommended requirements

Basic Statistics course, Linear Algebra, R programming

The student's learning outcomes after completing the course

Students will learn the main steps to reveal the information hidden in data: 1. Visual representation of the information; its synthesis as clusters and their interpretation. 2. To develop a predictive data mining model. 3. The analysis of sequences of events.

Forms of teaching and learning

Learning is done combining the theoretical explanations and their application to solve real cases. During the course students must solve and deliver a practical case, using R software, covering in overall each one of the course’s topics.


Form of assessment: Home assessment without presentation

  • Proportion: 100%

  • Duration: -

  • Grouping: Group & Individual

  • Grading scale: Letter (A - F)

  • Support material: All printed and written supporting material

Last updated from FS (Common Student System) July 25, 2021 12:20:01 PM