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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | YÖN301B | Data Mining | Compulsory | 3 | 5 | 5 |
| | Level of Course Unit | | First Cycle | | Objectives of the Course | | To be able to apply data mining techniques in data science to develop artificial intelligence-based models | | Name of Lecturer(s) | | Doç.Dr. Didem Güleryüz | | Learning Outcomes | | 1 | Describe the definition and aim of data mining. | | 2 | Identify overview to application areas, techniques and methods of discipline | | 3 | Identify data pre-processing steps for data warehouse concept. | | 4 | Plan proper solution to the problems and define data mining algorithms | | 5 | Apply association rules. | | 6 | Analyse decision trees and classification techniques | | 7 | Distinguish clustering methods | | 8 | Plan a sample warehouse with a real world data and analyse this data with all data mining algorithms |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | This course, which is an introduction to data mining, includes basic Data Preprocessing, Association Rules, Classification and Bundling algorithms and their applications. The final sections of the course are devoted to advanced topics such as data mining and Intrusion Detection and Text/Web Mining. | | Weekly Detailed Course Contents | |
| 1 | Description of data mining | | | | 2 | Overview to application areas, techniques and methods of discipline. data mining steps, OLAP | | | | 3 | Data preparation process and techniques for data mining | | | | 4 | Data preparation process and techniques for data mining | | | | 5 | Mining algorithm, method, association rules(Appriori algorithm) | | | | 6 | Mining algorithm, method, association rules(FP Growth algorithm) | | | | 7 | Mining algorithm, method, association rules(different types of rule extraction) | | | | 8 | Midterm Exam | | | | 9 | Mining algorithm, method, classification and prediction(Decision trees) | | | | 10 | Mining algorithm, method, classification and prediction(Bayesian classification) | | | | 11 | Application examples | | | | 12 | Data mining cluster analysis and data types | | | | 13 | Data mining clustering methods (k-neighbour algorithm) | | | | 14 | Data mining clustering methods(hierarchical methods, density based methods) | | | | 15 | Data mining clustering methods (hierarchical methods, density-based methods) II | | |
| | Recommended or Required Reading | | G. Silahtaroğlu, Veri Madenciliği, Papatya Yayınevi, İstanbul, 2008.
Yalçın Özkan, Veri Madenciliği Yöntemleri, Papatya Yayınevi, İstanbul, 2008
J. Han, M. Kamber, Data MiningConceptsandTechniques, 2. Ed.,USA, 2006.
Yaşar Gözüdeli, Yazılımcılar İçin SQL Server 2008 ve Veritabanı Programlama, Seçkin Yayıncılık, 2009, Ankara
Turgut Özseven, Veritabanı yönetim Sistemleri I, Murathan Yayınları, 2010, Trabzon
GehrkeRamakrishnan, Database Management Systems, McGraw-Hill, 2003. | | Planned Learning Activities and Teaching Methods | | | Assessment Methods and Criteria | |
| Midterm Examination | 1 | 40 | | Individual Study for Mid term Examination | 6 | 60 | | SUM | 100 | |
| Final Examination | 1 | 60 | | Individual Study for Final Examination | 15 | 40 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
| | Workload Calculation | |
| Midterm Examination | 1 | 1 | 1 | | Final Examination | 1 | 2 | 2 | | Quiz | 3 | 1 | 3 | | Attending Lectures | 1 | 1 | 1 | | Project Preparation | 1 | 20 | 20 | | Project Presentation | 1 | 1 | 1 | | Self Study | 1 | 1 | 1 | | Individual Study for Mid term Examination | 6 | 4 | 24 | | Individual Study for Final Examination | 14 | 4 | 56 | | Reading | 12 | 2 | 24 | | Individual Study for Quiz | 3 | 2 | 6 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | 3 | 1 | 4 | | | | | LO2 | 3 | | | 2 | | | | LO3 | 2 | | | | 5 | | | LO4 | 4 | | | | 4 | | | LO5 | 4 | | | | 5 | | | LO6 | 3 | | | 2 | | | | LO7 | 4 | | 4 | | | | | LO8 | 3 | | | 4 | | |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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