GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
DATA MINING/5021303
Course Title: DATA MINING
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Ability of learning the concept of data mining
Ability of learning the data mining algorithms
Ability of learning the techniques of data mining with R and Python
Ability of using the data mining methods in the real environment implementation
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Historical development and scope of data mining
2. Week  Basic concepts and softwares
3. Week  Preparation of data for analysis and exploring data
4. Week  Feature selection methods
5. Week  Classification using decision trees
6. Week  ZeroR, OneR, Bayes, k-nearest neighbor classifications
7. Week  Measurement of classification quality
8. Week  Basic concepts of clustering
9. Week  Partitioning-based clustering methods
10. Week  Hierarchical clustering methods
11. Week  Measurement of clustering quality
12. Week  Association Rules
13. Week  Project work
14. Week  Project work
15. Week  Final Exam
16. Week  -
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
30
 Assignment
0
0
 Application
0
0
 Projects
1
30
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
60
 Percentage of Final Exam to Total Score  
40
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
0
 Searching in Internet and Library
2
2
4
 Material Design and Implementation
0
 Report Preparing
0
 Preparing a Presentation
5
5
25
 Presentation
5
5
25
 Midterm Exam and Preperation for Midterm Exam
5
5
25
 Final Exam and Preperation for Final Exam
5
5
25
 Other (should be emphasized)
14
3
42
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. Based on the capabilities of undergraduate level, the students enrolled to the program can develop and deepen their knowledge and skill at the level of expertise on the same field of the undergradute study or a different field.X
22. The students use their theoretical and practical knowledge at the level of expertise in the area of statistics.X
33. The students should evaluate their acquired knowledge and skills in a critical perspective and the critical point of view guides their learning process.X
44. Theoretical and practical knowledge gained in graduate level in the field of Statistics should be applied and transfer to the current problems.X
55. By performing the process from the identification of the scientific research problem to reporting and the process should be transferred in oral, written and visual ways.X
66. The students should use computer software and information technologies on the level required by the field of Statistics.X
77. The students should have the ability to use Statistics in interdisciplinary studies.X
88. The students should have enough foreign language level to pursue statistical literature.X
99. At the required level of field of statistics, he/she should use statistical software and information technology efficiently in a such a way that helps solving problems in his/her research.X
1010. In the process of applying knowledge in a professional sense, social, scientific, and ethical values should be regarded.X
 -- NAME OF LECTURER(S)
   (Prof.Dr. Bülent ALTUNKAYNAK)
 -- WEB SITE(S) OF LECTURER(S)
   (websitem.gazi.edu.tr/bulenta)
 -- EMAIL(S) OF LECTURER(S)
   (bulenta@gazi.edu.tr)