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| YBS3004 | Business intelligence and data mining | 3+0+0 | ECTS:5 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Fatih GÜRCAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | In this course, it is aimed to gain knowledge and skills related to data mining concepts and techniques used in the process of knowledge discovery. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Identify data mining functionalities | 1,3,5,8 | | | LO - 2 : | Identify data warehousing functionalities | 1,3,5,8 | | | LO - 3 : | Apply data preprocessing techniques | 1,3,5,8 | | | LO - 4 : | Describe data mining primitives, languages, and system architectures | 1,3,5,8 | | | LO - 5 : | Applies appropriate data mining techniques in large databases | 1,3,5,8 | | | LO - 6 : | Use data mining software to perform data mining functionalities | 1,3,5,8 | | | LO - 7 : | Describe the current needs in data mining research | 1,3,5,8 | | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Fundamentals of data mining, data mining techniques, data mining software and R |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Data Mining | | | Week 2 | Getting to know your data | | | Week 3 | Data Preprocessing | | | Week 4 | Data Warehousing and Online Analytical Processing | | | Week 5 | Association rules | | | Week 6 | Classification: Basic Concepts | | | Week 7 | Classification: Advanced Methods | | | Week 8 | Cluster Analysis | | | Week 9 | Midterm exam | | | Week 10 | Outlier detection | | | Week 11 | Data mining trends and research frontiers | | | Week 12 | Data Mining Applications | | | Week 13 | Basics of Pandas | | | Week 14 | Python and Data Mining Applications-I | | | Week 15 | Python and Data Mining Applications-II | | | Week 16 | Final exam | | | |
| 1 | Akpınar, Haldun. 2014. DATA Veri Madenciliği Veri Analizi (2. Baskı), Papatya Bilim | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 04/2020 | 1 | 50 | | End-of-term exam | 16 | 06/2020 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 1 | 1 | 1 | | Uygulama | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 2 | 6 | 12 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 142 |
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