TÜRKÇE
ENGLISH
Information
| Code | ISB206 |
| Name | Data Mining |
| Term | 2024-2025 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
1 2 |
Course Goal / Objective
The aim of this course is to give students the theoretical background of data mining algorithms and techniques and to give the student the ability to select and apply appropriate data mining techniques for different applications.
Course Content
The scope of this course is data preprocessing, association rule mining, classification, cluster analysis with applications.
Course Precondition
None
Resources
Veri Madenciliği Yöntemleri ve R Uygulamaları , Doç. Dr. Bülent ALTUNKAYNAK, Seçkin Yayınları, 2017
Notes
Veri Madenciliği Yöntemleri ve R Uygulamaları , Doç. Dr. Bülent ALTUNKAYNAK, Seçkin Yayınları, 2017
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines basic data mining concepts. |
| LO02 | Explains data mining processes. |
| LO03 | Understand Database Support to Data Mining. |
| LO04 | Apply several algorithms of data mining techniques |
| LO05 | Explains data mining in business. |
| LO06 | Determine which data mining technique is appropriate to solve a particular problem |
| LO07 | Apply preprocessing operations on data. |
| LO08 | Design a data mining model. |
| LO09 | Implement a data mining algorithm |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | 1 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Make statistical inference (estimation, hypothesis tests etc.) | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques and gain insight | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programs | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | 3 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | 2 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 1 |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Mining | Reading source books-Application | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Beyin Fırtınası |
| 2 | Data Mining: A Closer View | Reading source books-Application | Öğretim Yöntemleri: Tartışma, Örnek Olay |
| 3 | Learning strategies | Reading source books-Application | Öğretim Yöntemleri: Örnek Olay, Benzetim |
| 4 | Machine learning process steps | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Örnek Olay, Benzetim |
| 5 | Distance Measures | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Örnek Olay |
| 6 | k-nearest neighbor algorithm | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Tartışma, Örnek Olay |
| 7 | k nearest neighbor example II | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Naive Bayes Classification | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Naive Bayes Algorithm and its application II | Reading source books-Application | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 11 | ID3 and C4.5 Decision Tree Algorithms | Reading source books-Application | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Örnek Olay |
| 12 | ID3 and C4.5 Decision Tree Algorithms and their applications II | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Örnek Olay |
| 13 | K- Means Algorithm and its Applications | Reading source books-Application | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama, Proje Temelli Öğrenme |
| 14 | K- Means Algorithm and its Applications II | Reading source books-Application | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Presentations | Reading source books-Application | Öğretim Yöntemleri: Proje Temelli Öğrenme , Tartışma |
| 16 | Term Exams | Review the topics discussed in the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review the topics discussed in the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 78 | ||
| Total Workload / 25 (h) | 3,12 | ||
| ECTS | 3 ECTS | ||