Official course description, subject to change:

Basic info last published 15/03-24
Course info
Language:
English
ECTS points:
7.5
Course code:
KSDAMIN1KU
Participants max:
50
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Software Design
Staff
Course manager
Fabricio Batista Narcizo
Part-time Lecturer
Course semester
Semester
Efterår 2024
Start
26 August 2024
End
24 January 2025
Exam
Abstract

This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.

Description

The course will cover the following main topics:

  • The data mining process
  • Cluster analysis
  • Data pre-processing
  • Pattern and association mining
  • Classification and prediction

Application examples will be given from domains including demographics, image processing and healthcare.

Formal prerequisites

Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions. This corresponds to at least having passed an introductory programming course, and preferably also an intermediate-level programming course. The course will contain compulsory programming in Python.

Students must be familiar with basic mathematical notation and concepts such as variables, sets, functions, averages, and variance. These competencies can be obtained by taking e.g. a course on discrete mathematics.

Information about study structure:

This course is a specialisation course on the MSc Software Design study programme, as well as an elective for other MSc study programmes. 
Moreover the student must always meet the admission requirements of the IT University. 


Intended learning outcomes

After the course, the student should be able to:

  • Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
  • Implement basic pre-processing, association mining, classification and clustering algorithms.
  • Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
  • Work efficiently in groups and evaluate the algorithms on real-world problems.
Ordinary exam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D1G: Submission for groups with following oral exam based on the submission. Shared responsibility for the report.