| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Data Mining | YBS214 | Turkish | Compulsory | 4. Semester | 3 + 0 | 3.0 | 6.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | FaceToFace |
| Course Coordinator | Öğr. Gör. Dr. Günay TEMÜR |
| Instructor(s) | |
| Goals | Introducing data mining and providing information about its intended use. Gaining analysis skills on data sets. Introducing and using the programs to be used in data mining. |
| Course Content | -Explaining the basic concepts of Data Mining -Introducing the usage areas of Data Mining -Explaining the basic methods of Data Mining -Introducing the software used in Data Mining -Data analysis using various Data Mining methods |
| # | Öğrenme Kazanımı |
| 1 | Knows the definition and purpose of using data mining |
| 2 | Knows data mining processes |
| 3 | Gets information about the softwares used in data mining |
| 4 | Knows and applies data preprocessing processes |
| 5 | Knows basic data mining methods, applies them and interprets the results. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Introduction to Data Mining | Preparation, After Class Study, Research |
| 2. Week | Data Mining Basic Concepts, Background, Methods | Research, Preparation, After Class Study |
| 3. Week | Basic Python operations | Research, Preparation, After Class Study, Practice |
| 4. Week | Basic Python Operations | Research, Preparation, After Class Study, Practice |
| 5. Week | Basic Python Operations | Preparation, After Class Study, Research, Practice |
| 6. Week | Dataset creation and preprocessing operations | Preparation, After Class Study, Practice, Research |
| 7. Week | Regression Algorithms | Practice, Preparation, After Class Study, Research |
| 8. Week | Classification Algorithms | Research, Preparation, After Class Study, Practice |
| 9. Week | Classification Algorithms | |
| 10. Week | Classification Algorithms | Practice, Preparation, After Class Study |
| 11. Week | Association Rules | Practice, Research, Preparation, After Class Study |
| 12. Week | Association Rules | Practice, Preparation, After Class Study |
| 13. Week | Clustering Algorithms | Preparation, After Class Study, Practice, Research |
| 14. Week | Overview of The Term | Preparation, After Class Study, Practice |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Graduates will have a holistic perspective on business functions | ||||||
| 2 | Graduates will have conceptual knowledge in the field of informatics in the sector average. | ✔ | |||||
| 3 | Graduates may integrate the business functions and IT infrastructure | ✔ | |||||
| 4 | Graduates will have awareness and knowledge about the processes of analyzing, designing, developing, and using information systems. | ✔ | |||||
| 5 | Students will have the ability to define the problem, collect data, analyze, interpret, evaluate, and develop a solution proposal for the solution of problems encountered in business. | ✔ | |||||
| 6 | Graduates may develop new strategic approaches for the efficiency of applications used in businesses. | ✔ | |||||
| 7 | Graduates may understand the logic of the algorithm and convert the designed algorithm into an up-to-date programming language. | ✔ | |||||
| 8 | Gradutes may have basic knowledge and understanding in the field of data science. | ✔ | |||||
| 9 | Graduates may have basic knowledge and understanding in the field of data science. | ✔ | |||||
| 10 | Graduates may base their vision on continuous learning and renewal. | ✔ | |||||
| 11 | Graduates may have an awareness of ethical and professional responsibility in business life. | ✔ | |||||
| 12 | Graduates may have an awareness of the individual and social effects of informatics applications and their legal consequences. gets the awareness of social responsibility. | ✔ | |||||
| 13 | Graduates may be able to use at least one foreign language in written and oral communication in the fields of information systems and business administration. | ||||||
| 14 | Graduates may take responsibility as an individual or team member in solving problems encountered in business life. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 |
|---|---|---|---|---|---|
| PY1 | 0 | 0 | 0 | 0 | 0 |
| PY2 | 5 | 5 | 0 | 5 | 5 |
| PY3 | 4 | 4 | 0 | 4 | 4 |
| PY4 | 5 | 5 | 0 | 5 | 5 |
| PY5 | 4 | 4 | 0 | 4 | 4 |
| PY6 | 3 | 3 | 0 | 3 | 3 |
| PY7 | 5 | 5 | 0 | 5 | 5 |
| PY8 | 5 | 5 | 0 | 5 | 5 |
| PY9 | 5 | 5 | 0 | 5 | 5 |
| PY10 | 3 | 3 | 0 | 3 | 3 |
| PY11 | 2 | 2 | 0 | 2 | 2 |
| PY12 | 1 | 1 | 0 | 1 | 1 |
| PY13 | 0 | 0 | 0 | 0 | 0 |
| PY14 | 3 | 3 | 0 | 3 | 3 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 3 | 42 |
|
Ders Dışı |
Preparation, After Class Study | 14 | 3 | 42 |
| Research | 14 | 3 | 42 | |
| Practice | 10 | 2.5 | 25 | |
|
Sınavlar |
Midterm 1 | 1 | 1 | 1 |
| Final | 1 | 1 | 1 | |
| Total Workload | 153 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 6.0 | ||
