Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

PURPOSE
Students learn how data mining can be used to discover hidden rules, patterns, or structures in large datasets.

Learning objectives

Knowledge

Students should gain knowledge of key concepts and techniques in data mining such as:

• techniques for data preprocessing such as feature selection, dimensionality reduction, and outlier detection
• data clustering
• techniques for discovering rules and patterns such as association rule mining and decision tree learning
• evaluation of results from data mining

Skills

• be able to apply data mining techniques to real-world data using appropriate software tools and programming languages.
• be able to document the results of data mining on a dataset using correct terminology and apply appropriate evaluation methods

Competences

• be able to identify relevant goals and techniques for data mining in a given application area.
• be able to interpret the results of a study using data mining.

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854