Facts about the course
- ECTS Credits:
- 2.5
- Responsible department:
- Faculty of Logistics
- Course Leader:
- Berit Irene Helgheim
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- 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
- Master of Science in Logistics
- Experience-based Master in Logistics
- Master of Science in Sustainable Energy Logistics
- Exchange programme - Master's level
- Master of Science in Sustainable Transport and Urban Mobility
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.
Examination
Form of assessment: Home assessment without presentation
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Proportion: 100%
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Duration: -
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Grouping: Group & Individual
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Grading scale: Letter (A - F)
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Support material: All printed and written supporting material