Course Details

Course Code (English)*

Semester*

Title (English)*

Lecture Hours (Weekly)

ECTS Credits*

Course Type (English)

Prerequisites (English) Databases Artificial Intelligence Probabilities and Statistics

Course URL (e.g., on e-class)

Learning Outcomes (English) 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

General Competencies (English) - Independent work - Team work - Adaptation in new conditions - Decision Making - Promoting free, creative and deductive reasoning

Course Content (English) 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.

Use of ICT (English) - Specific software for data mining - Group projects that require data processing and problem solving with ICT - Dissemination and organization of course material using OpenClass - Communication via OpenClass and emails

Is it elective? Άγνωστο Ναι Όχι

Load within semester (Hours)

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Independent Study*

Project Work*

Lab Report*