Harokopio University
School: School of Digital Technology
Department: Informatics and Telematics
Program: Undergraduate Programme

Data Mining

Semester: 7 ECTS: 5.0 Elective Erasmus

General

Code: ΕΠ19

Language: Greek

Delivery: Face-to-face

Prerequisites: Databases
Artificial Intelligence
Probabilities and Statistics

Workload

  • Lectures: 39.0 hours
  • Lab: 0.0 hours
  • Study: 46.0 hours
  • Project: 20.0 hours

Course Content

Data warehouses. Data analysis. OLAP systems. The knowledge mining process. Data clustering. Classification. Association rules. Temporal mining. Uncertainty handling in data mining tasks. Semi-structured data, information retrieval from the web.

Learning Outcomes

The course aims to introduce basic data mining concepts, presenting requirements and needs for the application of new methods and techniques for data analysis. The course extensively presents algorithms for supervised and unsupervised data mining / learning, such as clustering, classification, association rules. The course also presents techniques concerning designing and developing of data warehouses. At the end of the course the students must be able to:
- Analyze a data set and identify useful knowledge
- Categorize the knowledge extraction problem in the types of problems that has been taught
- Use data mining software in a way that creates added value

Skills

- Independent work
- Team work
- Adaptation in new conditions
- Decision Making
- Promoting free, creative and deductive reasoning