Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA MINING 805101725181 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors Bülent TUĞRUL
Assistants
Goals The aim of this course is to grasp the methods which are used to facilitate extraction of meaningful data for decision making through very large amounts of data.
Course Content Data Mining Introduction to methods, data mining, pipeline, data pre-processing and cleaning, data reduction, data mining, basic items, cluster analysis, clustering, association rules, time series analysis and sequence mining, graph mining, data visualization and data warehousing. The detailed applications to different areas.
Learning Outcomes 1) It develops algorithms that make meaningful, usable inferences from large data.
2) Gives decision by making comments on the extracted data.
3) Be able to analyze data

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introdcution Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
2. Week Data processing Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
3. Week Similarities and distance metrics Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
4. Week Relation and pattern mining Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
5. Week Clustering analysis Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
6. Week Outlier analysis Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
7. Week Classification Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
8. Week Data stream mining Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
9. Week Text mining Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
10. Week Time series analysis Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
11. Week Mining of discrete data Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
12. Week Spatial data analysis Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
13. Week Graph data analysis Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
14. Week Web data mining Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
15. Week Project Problem Solving
Colloquium
Project Based Learning 		Presentation (Including Preparation Time)
16. Week Final Exam Problem Solving

Scenario Based Learning 		Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Data Mining: The Textbook (Hardcover) by Charu C. Aggarwal

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY14000
PY24353530
PY44000
PY114353530

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Project (Including Preparation and presentation Time) 2 20
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 10 12
Final Exam 1 40
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload