| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Multivariate Data Analysis | SKY631 | Turkish | Compulsory | 3 + 0 | 3.0 | 10.0 |
| Prerequisite Courses | |
| Course Level | Graduate |
| Mode of delivery | The course is delivered through theoretical lectures, hands-on software exercises (e.g., SPSS, R, Python), group projects, and case studies. Theoretical knowledge is reinforced through projects and practical applications. |
| Course Coordinator | Arş. Gör. Melek TERZİ ÖZMEN |
| Instructor(s) | |
| Goals | The objective of this course is to teach fundamental methods and techniques of multivariate data analysis. It aims to enable students to analyze large and complex datasets to derive meaningful results, compare different analysis methods, and develop application scenarios. |
| Course Content | This course focuses on the methods and applications used in multivariate data analysis. The content includes topics such as principal component analysis, clustering, discriminant analysis, multidimensional scaling, canonical correlation analysis, and regression methods. |
| # | Öğrenme Kazanımı |
| 1 | Understand and apply multivariate data analysis methods. |
| 2 | Compare different analysis techniques and select the appropriate method. |
| 3 | Apply data preprocessing and dimensionality reduction techniques. |
| 4 | Use multivariate analysis methods on large datasets. |
| 5 | Interpret analysis results and integrate them into decision-making processes. |
| 6 | Perform analyses using relevant software (e.g., SPSS, R, Python). |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Introduction to multivariate analysis concepts | Interview, Presentation (Preparation), Practice, Preparation, After Class Study |
| 2. Week | Data preprocessing techniques | Presentation (Preparation), Interview, Practice, Preparation, After Class Study |
| 3. Week | Principal component analysis (PCA) | Interview, Presentation (Preparation), Practice, Preparation, After Class Study |
| 4. Week | Clustering analysis (K-means, hierarchical) | Preparation, After Class Study, Presentation (Preparation), Practice, Interview |
| 5. Week | Discriminant analysis | Presentation (Preparation), Practice, Interview, Preparation, After Class Study |
| 6. Week | Canonical correlation analysis | Practice, Interview, Preparation, After Class Study, Presentation (Preparation) |
| 7. Week | Multidimensional scaling | Practice, Preparation, After Class Study, Interview, Presentation (Preparation) |
| 8. Week | Midterm exam | Practice |
| 9. Week | Factor analysis | Interview, Presentation (Preparation), Preparation, After Class Study, Practice |
| 10. Week | Multiple regression analysis | Preparation, After Class Study, Interview, Practice, Presentation (Preparation) |
| 11. Week | Structural equation modeling | Presentation (Preparation), Preparation, After Class Study, Practice, Interview |
| 12. Week | Applications on large datasets | Practice, Preparation, After Class Study, Interview, Presentation (Preparation) |
| 13. Week | Presentation of group projects | Practice, Preparation, After Class Study, Interview, Presentation (Preparation) |
| 14. Week | General assessment and course wrap-up | Other Activities |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Ability to identify the health needs of the community and the demand for health services | ✔ | |||||
| 2 | Ability to develop service delivery models that can meet the health needs of the community and the demand for healthcare services | ✔ | |||||
| 3 | Ability to conduct economic and financial analysis of healthcare services | ✔ | |||||
| 6 | Ability to assess and enhance health outcomes | ✔ | |||||
| 7 | Proficiency in using statistical analysis, reporting, interpretation, and decision-making | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 |
|---|---|---|---|---|---|---|
| PY1 | 2 | 2 | 2 | 2 | 2 | 2 |
| PY2 | 2 | 2 | 2 | 2 | 2 | 2 |
| PY3 | 4 | 4 | 4 | 4 | 4 | 4 |
| PY6 | 4 | 4 | 4 | 4 | 4 | 4 |
| PY7 | 5 | 5 | 5 | 5 | 5 | 5 |
| 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 | 1 | 14 |
| Research | 10 | 2 | 20 | |
| Presentation (Preparation) | 1 | 3 | 3 | |
| Practice | 14 | 4 | 56 | |
|
Sınavlar |
Midterm 1 | 1 | 40 | 40 |
| Homework 1 | 1 | 20 | 20 | |
| Final | 1 | 60 | 60 | |
| Total Workload | 255 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 10.0 | ||
