TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ211 |
| Name | Introduction to Data Mining |
| Term | 2026-2027 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | FE Field Education Courses C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. YUSUF ALPER KAPLAN |
| Course Instructor |
Dr. Öğr. Üyesi Kasım ZOR
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
This course aims to introduce the fundamental concepts, algorithms, and applications of data mining. Students will learn how to explore, preprocess, and analyse data to discover meaningful patterns, and will gain hands-on experience applying core data mining techniques to real-world data sets. This course is designed to build the algorithmic and conceptual foundation required for subsequent courses in machine learning and deep learning.
Course Content
Introduction to Data Mining, Data (Types of Data, Data Quality, Data Preprocessing), Classification (Decision Tree Induction, Model Evaluation, Overfitting, Confusion Matrix, Accuracy, Precision, Recall, kNN), Association Analysis (Support, Confidence, the Apriori Algorithm, Rule Generation, Case Study: Market Basket Analysis), Cluster Analysis (K-means Algorithm, Hierarchical Clustering, Introduction to DBScan), Anomaly Detection (Statistical and Distance-Based Approaches), Introduction to Ensemble Methods, and Final Project Presentations.
Course Precondition
There are no formal prerequisites for the course.
Resources
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar, Introduction to Data Mining, 2nd Ed., Pearson, 2019.
Notes
Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th Ed., Elsevier, 2023. Michael Hahsler, An R Companion for Introduction to Data Mining, 1st Ed., 2025. Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, and Kenneth C. Lichtendahl, Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1st Ed., Wiley, 2018. Galit Shmueli, Peter C. Bruce, Peter Gedeck, and Nitin R. Patel, Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, 1st Ed., Wiley, 2020. Di Wu, Data Mining with Python: Theory, Application, and Case Studies, 1st Ed., CRC Press, 2024.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the data mining process and distinguish major data mining tasks including classification, association analysis, clustering, and anomaly detection. |
| LO02 | Identify data quality issues and apply fundamental data preprocessing techniques to prepare raw data for analysis. |
| LO03 | Construct and evaluate decision tree and kNN classifiers using a confusion matrix, accuracy, precision, recall, and F1-score. |
| LO04 | Apply the Apriori algorithm to generate association rules and interpret their practical relevance in applications such as market basket analysis. |
| LO05 | Implement and compare K-means and hierarchical clustering methods, and describe the basic principles of density-based clustering using DBSCAN. |
| LO06 | Explain and apply fundamental statistical and distance-based approaches for anomaly detection. |
| LO07 | Describe the rationale of ensemble learning methods and their relationship to subsequent machine learning topics. |
| LO08 | Design, implement, and present an end-to-end data mining project using a real-world data set including preprocessing, modelling, evaluation, and interpretation of results. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | 5 |
| PLO04 | - | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | 4 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course Introduction and Scope, Introduction to Data Mining | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Data | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 3 | Data - 2 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 4 | Classification | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 5 | Classification - 2 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 6 | Classification - 3 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 7 | Association Analysis | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 8 | Midterm Examination | Reviewing the previous topics | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Association Analysis - 2 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 10 | Association Analysis - 3 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 11 | Clustering Analysis | Reading the lecture notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 12 | Clustering Analysis - 2 | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 13 | Anomaly Detection | Reading the lecture notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 14 | Introduction to Ensemble Methods | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 15 | Final Project Presentations | Practicing and presenting | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım, Performans Değerlendirmesi |
| 16 | Final Examination | Reviewing the previous topics | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Final Examination | Reviewing the previous topics | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 60 | 60 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 3 | 3 |
| Final Exam | 1 | 3 | 3 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||