Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3VTK222Data Mining3+0+03323.08.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Associate Degree
Department / Program Web Design and Development
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course This course covers the fundamental data mining concepts, methods, and applications used to extract meaningful patterns and information from large datasets.
Course Content Introduction to data mining, data preprocessing, data cleaning and transformation, classification, clustering, association rules, model evaluation, data visualization, and fundamental data mining applications.
Course Methods and Techniques Lecturing, question-and-answer, case study, problem solving, practice, and individual study.
Prerequisites and co-requisities None
Course Coordinator Instructor Afife ÇİFTCİ OLGUN
Name of Lecturers Instructor AFİFE ÇİFTCİ OLGUN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Han, J., Kamber, M. & Pei, J. Data Mining: Concepts and Techniques. Morgan Kaufmann.
Tan, P.-N., Steinbach, M., Karpatne, A. & Kumar, V. Introduction to Data Mining. Pearson.
Lecture notes and current online resources.
Course Notes Lecture notes and current online resources.

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 2 % 10
Project 1 % 20
Final examination 1 % 40
Total
5
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 1 14
Assignments 2 4 8
Mid-terms 1 5 5
Project 1 8 8
Final examination 1 13 13
Total Work Load   Number of ECTS Credits 3 90

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Bilgi 
1 Explains the fundamental concepts and application areas of data mining.
Beceri 
2 Applies appropriate data preprocessing and transformation steps to datasets.
3 Explains and applies fundamental classification, clustering, and association analysis methods.
4 Evaluates the performance of data mining models using appropriate metrics.
Yetkinlik 
5 Analyzes a data mining problem and develops a solution using appropriate methods and tools.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to data mining, fundamental concepts and application areas
2 Data types, data sources and the data mining process
3 Data preprocessing: data cleaning and missing data
4 Data preprocessing: data transformation, normalization and feature selection
5 Exploratory data analysis and data visualization
6 Introduction to classification and fundamental classification methods
7 Decision trees and classification applications
8 Midterm Exam
9 Clustering concept and similarity/distance measures
10 K-Means and fundamental clustering applications
11 Hierarchical clustering methods
12 Association rules and the Apriori algorithm
13 Model evaluation metrics and interpretation of results
14 Data mining application / sample project study
15 General review and project evaluation
16 Final Exam
17 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16
In1 4 4 3 3 3
Sk2 3 4 4 4 4 3
Sk3 3 5 4 3 4 3 3
Sk4 3 4 5 3 4 4 4 3
Co5 4 5 5 3 3 4 5 4 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant