| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Multivariate Data Analysis | SKY631 | 3 + 0 | 3.0 | 10.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Graduate |
| Course Type | |
| 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 |
Res. Assist. Melek TERZİ ÖZMEN |
| Instructor(s) | |
| Assistants | |
| 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. |
| Learning Outcomes |
- Understand and apply multivariate data analysis methods. - Compare different analysis techniques and select the appropriate method. - Apply data preprocessing and dimensionality reduction techniques. - Use multivariate analysis methods on large datasets. - Interpret analysis results and integrate them into decision-making processes. - Perform analyses using relevant software (e.g., SPSS, R, Python). |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | Introduction to multivariate analysis concepts | Preparation, After Class Study Practice Verbal Expression Visual Presentation |
| 2. Week | Data preprocessing techniques | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 3. Week | Principal component analysis (PCA) | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 4. Week | Clustering analysis (K-means, hierarchical) | Preparation, After Class Study Visual Presentation Verbal Expression Practice |
| 5. Week | Discriminant analysis | Verbal Expression Practice Visual Presentation Preparation, After Class Study |
| 6. Week | Canonical correlation analysis | Verbal Expression Preparation, After Class Study Visual Presentation Practice |
| 7. Week | Multidimensional scaling | Practice Visual Presentation Preparation, After Class Study Verbal Expression |
| 8. Week | Midterm exam | Practice |
| 9. Week | Factor analysis | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 10. Week | Multiple regression analysis | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 11. Week | Structural equation modeling | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 12. Week | Applications on large datasets | Visual Presentation Practice Verbal Expression Preparation, After Class Study |
| 13. Week | Presentation of group projects | Verbal Expression Visual Presentation Practice Preparation, After Class Study |
| 14. Week | General assessment and course wrap-up | Other Activities |
| ÇOK DEĞİŞKENLİ İSTATİSTİK ANALİZİ İLE FARKLI ALANLARDA UYGULAMALAR, Doç. Dr. Nilay KÖLEOĞLU - Doç. Dr. Şenol ÇELİK - Doç. Dr. Fatih ÇEMREK, HOLISTENCE PUBLICATIONS, 2022. |
| Python Uygulamalı İstatiksel Veri Bilimi ve Analizi, Ahmet SEL, Akademisyen Kitabevi, 2021 |
| Program Requirements | Contribution Level | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | Measurement Method |
|---|---|---|---|---|---|---|---|---|
| PY1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 60 |
| PY2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 60 |
| PY3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 60 |
| PY6 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 60 |
| PY7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 60 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Preparation, After Class Study | 14 | 1 | 14 |
| Research | 10 | 2 | 20 |
| Visual Presentation | 1 | 3 | 3 |
| Practice | 14 | 4 | 56 |
| Midterm 1 | 1 | 40 | 40 |
| Homework 1 | 1 | 20 | 20 |
| Final | 1 | 60 | 60 |
| Total Workload | 255 | ||
| ECTS Credit of the Course | 10.0 | ||
