VBM613 - DATABASE LABORATORY

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
DATABASE LABORATORY VBM613 Any Semester/Year 1 0 2 3
PrequisitesNone
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesDrill and Practice
Problem Solving
Project Design/Management
 
Instructor (s)To be determined by the institute 
Course objectiveTo teach design and management of database systems. 
Learning outcomes
  1. In the end of this course the student will be able to
  2. ?Design database.
  3. ?Learn the database management system.
  4. ?Queries the database on SQL.
  5. ?Learn XML and multimedia databases.
  6. ?Have an idea about data mining techniques and data technologies
Course ContentDatabase Design, Database Management Systems, Database Query and Application :SQL, XML Databases, Geographic Database Systems, Multimedia Database Systems, Data Mining, Data Technologies. 
References? Sam R. Alapati- Experts Oracle Database10 G Administrator, Apress 2005 

Course outline weekly

WeeksTopics
Week 1Introduction to course
Week 2Database Design
Week 3Database Design(continued)
Week 4Database Design(continued)
Week 5Database Management Systems
Week 6Database Query and Application: SQL
Week 7Database Query and Application: SQL(continued)
Week 8Database Query and Application: SQL(continued)
Week 9XML Databases
Week 10Geographic Database Systems
Week 11Multimedia Database Systems
Week 12Data Mining
Week 13Data Technologies
Week 14Project Presentation
Week 15Project Presentation
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments1030
Presentation00
Project130
Seminar00
Midterms00
Final exam140
Total100
Percentage of semester activities contributing grade succes060
Percentage of final exam contributing grade succes040
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)10110
Presentation / Seminar Preparation000
Project12020
Homework assignment10110
Midterms (Study duration)000
Final Exam (Study duration) 188
Total Workload363390

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has detailed knowledge about data and knowledge engineering (DKE).   X 
2. Has a good understanding of common concepts such as abstraction, complexity, security, concurrency, software lifecycle and applies their expertise to the effective design, development and management of IS.    X
3. Understands the interaction of theory and practice and the links between them.    X
4. Has the ability to think at different levels of abstraction and detail; understands that an IS can be considered in different contexts, going beyond narrowly identifying implementation issues.  X  
5. Solves any technical or scientific problem independently and presents the best possible solution; has the communication skills to clearly explain the completeness and assumptions of their solution.  X  
6. Completes a project on a larger scale than an ordinary course project in order to acquire the skills necessary to work efficiently in a team.    X
7. Recognises that the field of DKE is rapidly evolving. Follows the latest developments, learns and develops skills throughout their career.   X 
8. Recognises the social, legal, ethical and cultural issues related to DKE practice and conduct professional activities in accordance with these issues.  X  
9. Can make oral presentations in English and Turkish to different audiences face-to-face, in writing or electronically.  X  
10. Recognises that DKE has a wide range of applications and opportunities.   X 
11. Is aware that DKE interacts with different fields, can communicate with experts from different fields and can learn necessary field knowledge from them.   X 
12. Define a research problem and use scientific methods to solve it.    X 

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest