DATA BASES AND DATA MINING

Course Information
TitleΒΑΣΕΙΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΕΞΟΡΥΞΗ ΔΕΔΟΜΕΝΩΝ / DATA BASES AND DATA MINING
CodeIM-218
Interdepartmental ProgrammeInterdisciplinary MSc on Informatics and Management 2015-today
Collaborating SchoolsInformatics
Economics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CommonNo
StatusActive
Course ID40003484

Programme of Study: Interdisciplinary MSc on Informatics and Management 2015-today

Registered students: 17
OrientationAttendance TypeSemesterYearECTS
InformaticsElective Courses217.5
ManagementElective Courses217.5

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Instructors from Other Categories
  • Panagiotis Symeonidis
Weekly Hours3
Class ID
600011434
Type of the Course
  • Scientific Area
  • Skills Development
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
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 (MS SQL Server). 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 multivalued Functional Dependencies, Normal forms. Languages and architectures for data mining, association rules, Classification and prediction, Glustering, Mining complex data types (text, time series, spatial data, DNA, Wed data etc).
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
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures
Laboratory Work
Project
Total
Student Assessment
Description
Final exam (70%), Synthetic assignment (30%).
Student Assessment methods
  • 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
08-02-2016