LOG904-154 Data Mining (Autumn 2020)
- ECTS Credits:
- 2.5
- Responsible faculty:
- Avdeling for logistikk
- Campus:
- Molde
- Course Leader:
- Berit Irene Helgheim
- Teaching language:
- English
- Study level:
- {code=HN, name=Second degree level}
- Duration:
- ½ year
Table of contents
Contents
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
- Master in Logistics
- Experience-based Master in Logistics
- Master in Sustainable Energy Logistics
- Exchange programme - Master's level
Recommended knowledge
Basic Statistics course, Linear Algebra, R programming
The student's learning outcomes after passing the exam
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.
Exam
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 (Felles studentsystem)
Oct. 14, 2026 4:32:47 AM