| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Introductıon To Data Mınıng | ENM 424 | 8 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Arş.Gör.Dr. CANER ERDEN |
| Dersi Verenler | Arş.Gör.Dr. CANER ERDEN, Arş.Gör.Dr. CANER ERDEN, |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı | 1) Introducing Data Mining and Promoting Its Usage |
| Dersin İçeriği | This course covers the fundamentals of statistical, machine learning and database aspects of data mining . The course is composed of three parts. The first part will cover the fundamentals of statistics and machine learning approaches for data mining. In the second part, we will cover the fundamental data mining concepts and algorithms for tasks such as Online Analytical Processing (OLAP), association rules, clustering, etc. The final part of the course will focus on research areas such as text mining, collaborative filtering, link analysis and mining in biological domains (as time permits). |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Utilizing classification algorithms | Lab / Workshop, Self Study, Project Based Learning, | Project / Design, Testing, Homework, |
| 2 | Utilizing clustering algorithms | Project Based Learning, Self Study, Lab / Workshop, | Homework, Project / Design, Testing, |
| 3 | Utilizing market basket analysis | Lab / Workshop, Self Study, Project Based Learning, | Testing, Homework, Project / Design, |
| 4 | Ability of using data mining software | Project Based Learning, | Project / Design, Performance Task, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Data Mining: An Introduction | |
| 2 | Areas of interest in data mining | |
| 3 | Introduction to Data Mining computer programs - Data Mining in Spreadsheet Programs | |
| 4 | Prepering data to analysis (steps) | |
| 5 | OLAP | |
| 6 | Classification and Clustering | |
| 7 | Decision tree | |
| 8 | Statistics in data mining | |
| 9 | Artificial intelligent in data mining | |
| 10 | Neural Networks in data mining | |
| 11 | Association rules | |
| 12 | Other mining techniques in data mining- web and text mining | |
| 13 | Sample applications | |
| 14 | Data Mining Applications in Industry |
| Kaynaklar | |
|---|---|
| Ders Notu | http://www.cagil.sakarya.edu.tr lecture notes on this link |
| Ders Kaynakları | 1. Gökhan Silahtaroğlu, Basic Data Mining with Concepts and Algorithms, Papatya Publications, (2008) |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. | X | |||||
| 2 | Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. | X | |||||
| 3 | Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. | X | |||||
| 4 | Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. | X | |||||
| 5 | Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. | X | |||||
| 6 | Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually | X | |||||
| 7 | Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language | ||||||
| 8 | Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology | X | |||||
| 9 | Engineering graduates with well-structured responsibilities in profession and ethics | X | |||||
| 10 | Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. | X | |||||
| 11 | Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions. | X | |||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Kısa Sınav | 10 |
| 1. Ödev | 20 |
| 2. Ödev | 70 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 50 |
| 1. Final | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 2 | 32 |
| Mid-terms | 1 | 4 | 4 |
| Assignment | 2 | 10 | 20 |
| Performance Task (Laboratory) | 1 | 16 | 16 |
| Toplam İş Yükü | 120 | ||
| Toplam İş Yükü / 25 (Saat) | 4,8 | ||
| Dersin AKTS Kredisi | 5 | ||