| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Multivariate Data Analysis | SKY631 | 3 + 0 | 3.0 | 10.0 |
| Prerequisite Courses | 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/Applications | Method |
|---|---|---|
| 1. Week | Introduction to multivariate analysis concepts | Preparation, After Class Study Verbal Expression Practice Visual Presentation |
| 2. Week | Data preprocessing techniques | Preparation, After Class Study Verbal Expression Practice Visual Presentation |
| 3. Week | Principal component analysis (PCA) | Preparation, After Class Study Verbal Expression Visual Presentation Practice |
| 4. Week | Clustering analysis (K-means, hierarchical) | Preparation, After Class Study Verbal Expression Practice Visual Presentation |
| 5. Week | Discriminant analysis | Preparation, After Class Study Verbal Expression Visual Presentation Practice |
| 6. Week | Canonical correlation analysis | Visual Presentation Preparation, After Class Study Verbal Expression Practice |
| 7. Week | Multidimensional scaling | Visual Presentation Practice Preparation, After Class Study Verbal Expression |
| 8. Week | Midterm exam | Practice |
| 9. Week | Factor analysis | Preparation, After Class Study Verbal Expression Practice Visual Presentation |
| 10. Week | Multiple regression analysis | Verbal Expression Visual Presentation Preparation, After Class Study Practice |
| 11. Week | Structural equation modeling | Preparation, After Class Study Verbal Expression Visual Presentation Practice |
| 12. Week | Applications on large datasets | Preparation, After Class Study Verbal Expression Visual Presentation Practice |
| 13. Week | Presentation of group projects | Preparation, After Class Study Visual Presentation Verbal Expression Practice |
| 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 | ||
