ISB206 Data Mining

3 ECTS - 2-0 Duration (T+A)- 4. Semester- 2 National Credit

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
Prof. Dr. GÜZİN YÜKSEL (A Group) (Ins. in Charge)


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

Update Time: 12.06.2024 10:46