Akademik Dersler - Tanıtım

Last Updated: 02/03/2026

Code - Course Title	Compulsory/Elective	Laboratory + Practice	ECTS
BVA102 - Data Mining	II. SEMESTER	3 +2	5.0

Language of Instruction	Türkçe
Course Type	Required Courses
Course Instructor(s)	ÖĞR. GÖR. DR. BÜLENT BATMAZ
Mode of Delivery	Face to face
Place of Delivery
Used Educational Platforms
Prerequisites	Basic Mathematical Knowledge
Courses Recomended	Basic Mathematical Knowledge
Recommended Resources	Faculty Recommendation
Work Placement	N/A
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Overview of data mining
Week - 2		Data mining process
Week - 3		Data cleaning techniques
Week - 4		Handling missing values, outlier detection and treatment
Week - 5		Data transformation methods
Week - 6		Data visualization techniques
Week - 7		Correlation analysis, data distribution analysis
Week - 8		Introduction to classification
Week - 9		Decision trees, logistic regression, Naive Bayes classifier
Week - 10		Model evaluation techniques
Week - 11		Introduction to clustering, K-means clustering, hierarchical clustering, evaluation of clustering results
Week - 12		Market basket analysis, frequent itemsets, association rule generation, rule evaluation and pruning
Week - 13		Feature selection techniques
Week - 14		Principal Component Analysis (PCA)

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Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
1.Midterm Exam	1	30
Final Exam	1	40
Homework	1	30
Toplam (%)		100