Akademik Dersler - Tanıtım

Last Updated: 30/09/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
BİL524 - Data Mining	II. SEMESTER	3 +0	6.0

Language of Instruction	Türkçe
Course Type	Elective Courses
Course Instructor(s)	DR. ÖĞR. ÜYESİ İLKER KAYABAŞ
Mode of Delivery	Distance Education
Place of Delivery
Used Educational Platforms
Prerequisites	There is no prerequisite or co-requisite for this course.
Courses Recomended	There is no other recommended course prior to this course.
Recommended Resources	Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
Work Placement	This course has no internship practice.
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Introduction to Data Mining
Week - 2		Data Mining Applications
Week - 3		The R programming language and RStudio as the development environment (IDE)
Week - 4		Python programming language and Jupyter Notebook as the development environment (IDE)
Week - 5		Data Preprocessing
Week - 6		Exploratory Data Analysis
Week - 7		Association Rules Mining
Week - 8		Applications of Association Rule Mining
Week - 9		Classification
Week - 10		Applications of Classification
Week - 11		Regression
Week - 12		Cluster Analysis
Week - 13		Applications of Cluster Analysis
Week - 14		Neural Networks and Deep Learning

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Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)