Knowledge discovery in databases

General data
Course Title Knowledge discovery in databases
ECTS credits 5
Course Code  
Type of Course  Compulsory
Year and Semester of Study  First year / Summer semester
Course Website  -
Department  Department of Informatics
Course Coordinator Professor Mirjana Pejić Bach, PhD
Instructors Professor Mirjana Pejić Bach, PhD
Assistants  -
Type of Degree Program  Graduate Study Programme
Major  -
Hours per Semester  30
Language of Instruction  English
Class Schedule                                                Schedule 


 
Course Contents:
1. Introduction to discovering knowledge in data bases
2. Data base knowledge discovering process
3. Identification of business problem
4. Data base knowledge discovery application across different areas
5. Preparation of input data
6. Methods of data base knowledge discovery
7. Software for data base knowledge discovery
8. Data base knowledge discovery as a method of business intelligence
Description of general and specific competences (knowledge and skills) to be developed by this course:
Enterprises that produce large amounts of data every day do not use them efficiently. Knowledge discovery in data bases is a method for discovering valuable information in the business data bases that can improve the enterprise business. The objectives of the course are to familiarize the students with the process of knowledge discovery in data bases through the most frequently used software and the survey of the areas it is most efficiently applied in. The analysis includes business cases within the field of finance, marketing and management.
Teaching methods:
Lectures, seminars, papers and practical tasks that will be solved through individual work.
Additional requirements for students:
 Active participation in classroom activities. Monitoring and readings of the most recent literature. Writing short papers concerning current issues. Possible participation in different projects.
Assessment/examination method:

 Knowledge evaluation will be conducted in classes (lectures, seminars, individual solutions to particular problems and cases). The final grade will be based on the students' scores in the written exams (20%), oral exams (20%) and different forms of in-class knowledge assessment (40%).

Required reading:
 1. Pejić Bach, M. Otkrivanje znanja u bazama podataka. To be published soon.
2. Berry, M.J.A., Linoff, G.S., Mastering Data Mining. Wiley, Chichester, 2000.
Recommended reading:
1. Westphal, C., Blaxton, T., Data Mining Solutions. Wiley, Chichester, 1998
2. Parr Rud, O. Data Mining Cookbook. Wiley, Chichester, 2003
Course and teaching quality assurance method (method of monitoring the quality of the course and its teaching):
 Course evaluation will be conducted through the anonymous student poll at the end of the teaching cycle.
Course Prerequisites
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Additional Information
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