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Information
| Code | ISB206 |
| Name | Data Mining |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| 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 |
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
Data preprocessing, association rule mining, classification, cluster analysis with applications.
Course Precondition
None
Resources
Bülent Altunkaynak, Veri Madenciliği Yöntemleri ve R Uygulamaları , 2017
Notes
Course notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Define basic data mining concepts. |
| LO02 | Learn Data Mining Processes and Knowledge Discovery |
| LO03 | Understand Database Support to Data Mining. |
| LO04 | Apply several algorithms of data mining techniques |
| LO05 | Learn 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 | Have in-depth theoretical and practical knowledge about Probability and Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | They have the knowledge to make doctoral plans in the field of statistics. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge about analysis and modeling methods used in statistics. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Has comprehensive knowledge of methods used in statistics. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Make scientific research on Mathematics, Probability and Statistics. | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Indicates statistical problems, develops methods to solve. | 5 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply innovative methods to analyze statistical problems. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Designs and applies the problems faced in the field of analytical modeling and experimental researches. | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Access to information and do research about the source. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Develops solution approaches in complex situations and takes responsibility. | 5 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Has the confidence to take responsibility. | 3 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | They demonstrate being aware of the new and developing practices. | 3 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | He/She constantly renews himself/herself in statistics and related fields. | 4 |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Communicate in Turkish and English verbally and in writing. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Transmits the processes and results of their studies clearly in written and oral form in national and international environments. | 2 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | It considers the social, scientific and ethical values in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. | 5 |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Uses the hardware and software required for statistical applications. | 5 |
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: Soru-Cevap, Tartışma, Örnek Olay |
| 3 | Learning strategies | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Örnek Olay |
| 4 | Machine learning process steps | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Örnek Olay |
| 5 | Distance Measures | Reading source books-Application | Öğretim Yöntemleri: Tartışma, Örnek Olay |
| 6 | k-nearest neighbor algorithm | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Örnek Olay |
| 7 | k nearest neighbor example II | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, 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, Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 10 | Naive Bayes Algorithm and its application II | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 11 | ID3 and C4.5 Decision Tree Algorithms | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 12 | ID3 and C4.5 Decision Tree Algorithms and their applications II | Reading source books-Application | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 13 | K- Means Algorithm and its Applications | Reading source books-Application | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Örnek Olay |
| 14 | K- Means Algorithm and its Applications II | Reading source books-Application | Öğretim Yöntemleri: Alıştırma ve Uygulama, Örnek Olay |
| 15 | Presentations | Reading source books-Application | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Gösteri |
| 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 | ||