Learning Outcomes
Upon completion of the course, the student shall be able to:
- Explain the basic concepts of data mining and pattern recognition
- Summarise and interpret data with the use of descriptive statistics.
- Explain the difference between regression techniques and classification techniques.
- Discuss data mining ethics.
- Understand how businesses extract extra value from data.
- Understand the difference between descriptive, predictive and prescriptive analytics.
- Describe research in the field beyond drawing from scientific papers.
- Show an ability to work with a given dataset and apply basic algorithms to extract information.
Course Content
This course gives an overview of Data Mining in the context of Business Intelligence (BI).
The course contents include data pre-processing methods such as data visualisation and data dimension reduction. Basic algorithms are introduced for classification and prediction purposes such as linear regression, decision trees and discriminant analysis. Unsupervised data mining methods are presented, such as association rules and collaborative filtering. Finally, data mining ethics are discussed.
Assessment
The lab reports have a weighting 2 credits, the home assignment 3 credits, and the seminar presentation 2.5 credits.
Forms of Study
Lectures, tutorials, lab session, home assignment and seminar
Grades
The Swedish grades U–VG.
To be awarded the grade VG, the student must achieve a VG in both the seminar presentation and the home assignment.Prerequisites
- Fundamentals of programming 7,5 credits
- Research Methodology 7.5 credits First Cycle