DATA BASES AND DATA MINING
| Title | ΒΑΣΕΙΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΕΞΟΡΥΞΗ ΔΕΔΟΜΕΝΩΝ / DATA BASES AND DATA MINING |
| Code | IM-218 |
| Interdepartmental Programme | Interdisciplinary MSc on Informatics and Management 2015-today |
| Collaborating Schools | Informatics Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Common | No |
| Status | Active |
| Course ID | 40003484 |
| Academic Year | 2016 – 2017 |
| Class Period | Spring |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Class ID | 600039900
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600039900
- At the Website of the School: http://delab.csd.auth.gr/~symeon/courses/dm/index.htm
Prerequisites
General Prerequisites
There are no prerequisities.
Learning Outcomes
1. Cognitive domain:
Understanding: Explaining ideas or concepts Databases and Mining.
Application: Application of database and Mining concepts.
Analysis: Analyze database and Mining concepts into their component parts.
Creation: Synthetic work in Databases and Mining
2. Emotional domain:
Response: Active participation of learners with the presentation of a synthetic assignement on data bases and Mining
Valueing: Critical assessment of research articles in Database and Mining research field
3. Psychomotor domain:
Manipulation: Ability to perform specific actions on an data base and Mining management system.
Learning outcomes:
1. Knowledge:
Level 6: The student will have advanced knowledge in databases and Mining , involving a critical understanding of theories and principles.
2. Skills:
Level 6: The student will possess advanced skills in a database and Mining management system and will be able to prove it by using a DBMS.
3. Capacities:
Level 5: The student will be able to manage and oversee the creation of a database and Mining process.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
Data base Architecture, Modeling data with entity-relationship model, Relational model and relational algebra, language SQL, Relational calculus, database design, and multi-valued Functional Dependencies, Normal forms. Languages and architectures for data mining, association rules, Classification and prediction, Clustering, Mining complex data types.
Educational Material Types
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.3 | |||
| Project | 90 | 3 | |||
| Study | 96 | 3.2 | |||
| Total | 225 | 7.5 |
Student Assessment
Description
Final exam (50%), Synthetic assignment (50%).
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
[1] Παναγιώτης Συμεωνίδης, Αναστάσιος Γούναρης (2016) Βάσεις, Αποθήκες και Εξόρυξη Δεδομένων, Εκδόσεις Κάλλιπος
Additional bibliography for study
[1]. Συστήματα Βάσεων Δεδομένων: Θεωρία και Πρακτική Εφαρμογή, Ιωάννης Μανωλόπουλος και Απόστολος Παπαδόπουλος, Εκδόσεις Νέων Τεχνολογιών.
[2]. Dunham M.: “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003
[3]. Han J. and Kamber M.: “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2001
[4]. Chakrabarti S.: “Mining the Web”, Morgan Kaufmann, 2003
Last Update
14-02-2018
