| Course Code: | COE012 | ||||
| Course Name: | Data Mining | ||||
| Semester: |
Fall |
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| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. AMIR SEYYEDABBASI | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Ali HAMİTOĞLU | ||||
| Course Assistants: |
| Course Objectives: | Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples. Special emphasis will be give to the Machine Learning methods as they provide the real knowledge discovery tools. Important related technologies, as data warehousing and on-line analytical processing (OLAP) will be also discussed. The students will use recent Data Mining software. |
| Course Content: | This course on data mining covers the fundamental concepts, techniques, and tools used for extracting valuable insights from large datasets through topics such as data preprocessing, exploratory data analysis, predictive modeling, clustering, text mining, and project work. |
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The students who have succeeded in this course;
1) To introduce students to the basic concepts and techniques of Data Mining. 2) To develop skills of using recent data mining software for solving practical problems. 3) To gain experience of doing independent study and research. |
| Week | Subject | Related Preparation |
| 1) | Introduction to Course | NO |
| 2) | Know Your Data | NO |
| 3) | Data Preprocessing | NO |
| 4) | Data Warehousing and On-Line Analytical Processing | NO |
| 5) | Data Cube Technology | NO |
| 6) | Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods | NO |
| 7) | Advanced Frequent Pattern Mining | NO |
| 8) | Midterm | |
| 9) | Classification: Advanced Methods | |
| 10) | Classification: Basic Concepts | NO |
| 11) | Cluster Analysis: Basic Concepts and Methods | NO |
| 12) | Cluster Analysis: Advanced Methods | NO |
| 13) | Outlier Detection | NO |
| 14) | Trends and Research Frontiers in Data Mining | NO |
| Course Notes / Textbooks: | Textbook: Han, Jiawei, Jian Pei, and Hanghang Tong. Data mining: concepts and techniques. Morgan kaufmann, 2022. |
| References: | 1. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2005. 2. Mohammed J. Zaki, Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge Press, 2014. |
| Course Learning Outcomes | 1 |
2 |
3 |
|---|---|---|---|
| Program Outcomes |
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
| Semester Requirements | Number of Activities | Level of Contribution |
| Laboratory | 5 | % 30 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 14 | 42 |
| Homework Assignments | 12 | 12 |
| Midterms | 1 | 15 |
| Final | 1 | 15 |
| Total Workload | 126 | |