Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3BİT223Data Mining2+2+03427.03.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Associate Degree
Department / Program Informatics Security Technology
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To acquire basic information about the basic concepts of Data Mining and other disciplines (mathematics, statistics, data visualization, etc.) that data mining is related to.
Course Content Basic information about Data Mining, the most used data mining algorithms and their applications.
Course Methods and Techniques application, presentation narration
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Instructor Tuğba KAVAK
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources
Course Notes Lecture Presentations

Course Category
Mathematics and Basic Sciences %10
Field %90

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 % 40
Assignment 1 % 0
Final examination 1 % 60
Total
3
% 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 3 42
Assignments 1 5 5
Mid-terms 1 7 7
Project 1 0 0
Final examination 1 15 15
Total Work Load   Number of ECTS Credits 4 111

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Will be able to make basic definitions of data mining and explain basic data mining problems.
2 By preparing a data set, it will be able to perform various analyzes such as data preprocessing, data visualization, summary statistics, etc.
3 Will be able to explain the basic concepts of classification and use various classification techniques.
4 Will be able to explain the basic concepts of clustering, will be able to perform clustering analysis with the K-Means method.
5 Will have information about how the Python programming language is used in data mining and will have the ability to code in the Python programming language.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Overview of Data Analysis with Python: Numpy and Pandas
2 Fundamentals of Data Mining and Big Data Concept
3 Data Preprocessing and Model Preparation Processes
4 Basic Statistical Concepts and Outlier Analysis
5 Supervised Learning: Classification and Logistic Regression
6 Supervised Learning: Decision Trees and Classification
7 General Review: Data Prep and Classification Practices
8 Midterm Exam
9 Supervised Learning: Fundamentals of Regression Analysis
10 Regression Applications and Error Calculation Methods
11 Unsupervised Learning: K-Means and Clustering Logic
12 Clustering Analysis and Model Performance Evaluation
13 Intro to Text Mining and Natural Language Processing
14 Text Data Visualization and Final Practice

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 3 5 5 3 3 5 3 2 2 5
C1 3 5 5 4 3 5 3 2 2 5
C2 3 5 5 4 3 5 3 2 2 5
C3 4 5 5 3 3 5 3 3 3 5
C4 3 5 5 4 3 5 3 2 2 5
C5 4 5 5 3 3 5 3 3 3 5

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=240053&curProgID=5648&lang=en