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Information
| Code | BL148 |
| Name | data mining |
| Term | 2024-2025 Academic Year |
| Semester | 2. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Ön Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör. Mahir ATMIŞ |
| Course Instructor |
1 |
Course Goal / Objective
The general aim of this course is to give students; parallel to the development of backup environments making increasing amounts of data useful, decision support systems How to carry out the operations necessary to provide useful information for Teaching that it was brought. Hidden information, patterns and rules contained in the data how the data can be made understandable and the findings obtained The aim of this course is to introduce evaluation methods.
Course Content
Data Mining Concepts, Data Preparation Techniques, Statistical Learning Theory (Naive Bayes), Clustering Methods (K-Means, hierarchical), Decision Trees and Decision Rules, Association Rules
Course Precondition
None
Resources
Özkan Y., (2016). Veri Madenciliği Yöntemleri, Papatya Yayıncılık, Baskı 3. Lecture Notes, Mahir Atmis
Notes
Kaggle Website Google Colab
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Gains the knowledge about data preprocessing methods. |
| LO02 | Gains the knowledge and skills to learn and apply the basic concepts of Data Mining. |
| LO03 | Gains the knowledge about data reduction methods. |
| LO04 | Gains the knowledge about clustering concepts. |
| LO05 | Implements classification and clustering methods with and without trainer. |
| LO06 | Gains knowledge about association rules. |
| LO07 | Applies a data mining algorithm. |
| LO08 | Designs a data mining model. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gains basic, current and applied knowledge about Computer Technologies. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Gains knowledge about occupational health and safety, environmental awareness, and quality processes. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Has knowledge of basic electronic components comprising computer hardware and their operations. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Has knowledge about Atatürk's Principles and History of Revolution. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Keeps track of current developments and applications in computer programming, and utilizes them effectively. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Has the ability to solve problems in the field of computer programming. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Creates algorithms and data structures, and performs mathematical calculations. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Explains and implements web programming technologies. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Performs database design and management. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Tests software and resolves errors. | 3 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Can utilize software and package programs in the field of computer programming. | 5 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Explains, designs and installs network systems. | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Uses word processor, spreadsheet, presentation programs. | |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Can effectively present thoughts on computer technologies through written and verbal communication, expressing them clearly and comprehensibly. | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Takes responsibility as a team member to solve unforeseen complex problems encountered in practical applications of computer programming. | 5 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Has awareness in career management and lifelong learning. | |
| PLO17 | Yetkinlikler - Alana Özgü Yetkinlik | Has societal, scientific, cultural, and ethical values in the collection, application, and announcement of results related to computer technologies. | |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Follows developments in the field using a foreign language and communicates with colleagues. | |
| PLO19 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Can effectively communicate in Turkish both in written and oral forms. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Mining | Preparation is not required. | Öğretim Yöntemleri: Anlatım |
| 2 | Data Mining Processes | Preparation is not required. | Öğretim Yöntemleri: Anlatım |
| 3 | Classification Algorithms | Preparation is not required. | Öğretim Yöntemleri: Anlatım |
| 4 | Data Preprocessing Steps | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 5 | Classification with Decision Trees | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 6 | K-means Clustering Algorithm | Preparation is not required. | Öğretim Yöntemleri: Anlatım |
| 7 | Classification and Evaluation | Preparation is not required. | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Study | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Memory Based Classification | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 10 | Statistical Classification Models | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 11 | Clustering | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 12 | Association Rules | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 13 | Data Mining Tools and Software | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 14 | Data Mining Applications-1 | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Data Mining Applications-2 | Preparation is not required. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | 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 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 2 | 2 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 3 | 3 |
| Total Workload (Hour) | 77 | ||
| Total Workload / 25 (h) | 3,08 | ||
| ECTS | 3 ECTS | ||