Information Package / Course Catalogue
| Course Code | : EK356 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
Basic concepts of data mining and data mining modeling techniques used in the literature and the real world applications are taught.
1. Introduction to Data Mining 2. Data Understanding 3. Data Preparation 4. Association Rules and Sequence Analysis 5. Prediction Modelling Techniques 6. Cluster Analysis 7. Outlier Detection 8. Meta Modelling
| 1. | Understand the basic knowledge about the data mining concept |
| 2. | Have a knowledge about data mining applications in real world and specific sector |
| 3. | Have a knowledge about softwares which are used in data mining applications |
| 4. | To be able to models the statistical problems and produces solutions to problem-specific cases |
| 5. | To be able to analyze the data, to be able to make more efficient inferences and forecasts for the future |
| 1. | Han, J. and Kamber, M., 2006, Data Mining: Concepts and Techniques, The Morgan Kaufmann, Second Edition. |
| 2. | Olson, D.L.; Delen, D., 2008, Advanced Data Mining Techniques, Springer Publishing |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 11 | 3 | 3 | 66 |
| Lecture - Practice | 3 | 5 | 3 | 24 |
| Midterm Examination | 1 | 12 | 1 | 13 |
| Final Examination | 1 | 15 | 2 | 17 |
| TOTAL WORKLOAD (hours) | 120 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | |
OÇ-1 | 3 | 4 | 4 | 5 | 2 | 5 | 2 | 3 | 4 |
OÇ-2 | 3 | 4 | 5 | 2 | 2 | 2 | 2 | 2 | 4 |
OÇ-3 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 2 | 2 |
OÇ-4 | 3 | 5 | 2 | 2 | 5 | 5 | 5 | 5 | 5 |
OÇ-5 | 5 | 2 | 2 | 5 | 4 | 4 | 4 | 4 | 4 |