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Information
| Code | BBZ206 |
| Name | Data Mining |
| Term | 2024-2025 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to provide students with theoretical knowledge about data mining algorithms and techniques and to provide students with the ability to select and apply appropriate data mining techniques for different applications.
Course Content
In this course, data preprocessing, association rule analysis, classification, clustering analysis and their applications are covered.
Course Precondition
None
Resources
Veri Madenciliği Yöntemleri ve R Uygulamaları, Bülent Altunkaynak, Seçkin Yayıncılık.
Notes
Ders Notları
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines basic data mining concepts. |
| LO02 | Recognize data mining processes. |
| LO03 | Establishes the relationship between data mining and database. |
| LO04 | Implements various algorithms related to data mining methods. |
| LO05 | Develops data mining knowledge that can be used in business life. |
| LO06 | It uses the data mining technique appropriate to solve a particular problem. |
| LO07 | Designs a data mining model. |
| LO08 | Implements a data mining algorithm. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | 3 |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introductory information about Data Mining. | Required reading | Öğretim Yöntemleri: Tartışma, Beyin Fırtınası |
| 2 | ID3 Algorithm | Reading sources | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 3 | C4.5(J48) Algorithm | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | CART Algorithm(Twoing Criteria) | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | CART Algorithm(Twoing Criterion) | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | CHAID Algorithm | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 7 | 24 / 5.000 Pruning the Decision Tree | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Mid-Term Exam | Written Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | ZeroR and OneR Methods | Reading sources | Öğretim Yöntemleri: Anlatım, Örnek Olay |
| 10 | Bayesian Classification | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 11 | K-neighbor Method | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Measuring ClassificationQuality | Reading sources | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Association Rules | Reading sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Distance Measures in Clustering | Reading sources | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 15 | Solving Problem | Review of topics discussed in the lecture notes and sources | Öğretim Yöntemleri: Soru-Cevap, Problem Çözme |
| 16 | Term Exams | Written exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Written exam | 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 | 6 | 84 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 8 | 8 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||