MATHEMATICS | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
SEN4016 | Multivariate Data Analysis | Fall | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. MEHMET ALPER TUNGA |
Recommended Optional Program Components: | None. |
Course Objectives: | The students will have the ability of applying specific techniques included in multivariate analysis such as principle component analysis, factor analysis, linear regression to specific problems. |
The students who have succeeded in this course; 1. Describe multivariate data analysis concepts 2. Define the properties and limitations of PCA and compute PCA through different ways 3. Describe the types of factoring and factor computation 4. Define metric and non-metric scales 5. Describe simple and multiple correspondence analysis and chi squared distances 6. Define variations of MANOVA 7. Evaluate regression coefficients, parameter estimation, hypothesis testing 8. Describe deduction, induction, estimation, tests, correlation 9. Define univariate and multivariate filters |
The course content is composed of principle component analysis (pca), factor analysis, multidimensional scaling, correspondence analysis, multivariate analysis of variance (manova), multiple linear regression, statistical inference, feature subset selection. |
Week | Subject | Related Preparation |
1) | Introduction | |
2) | Principle Component Analysis (PCA) | |
3) | Principle Component Analysis (PCA) | |
4) | Factor Analysis | |
5) | Factor Analysis | |
6) | Multidimensional Scaling | |
7) | Correspondence Analysis | |
8) | Multivariate Analysis of Variance (MANOVA) | |
9) | Multiple Linear Regression | |
10) | Multiple Linear Regression | |
11) | Statistical Inference | |
12) | Statistical Inference | |
13) | Feature Subset Selection | |
14) | Feature Subset Selection |
Course Notes / Textbooks: | Multivariate Data Analysis, 7/E, Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson, 2010, 9780138132637 |
References: | Yok - None. |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 4 | % 20 |
Homework Assignments | 2 | % 10 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 4 | 5 | 20 |
Homework Assignments | 2 | 5 | 10 |
Quizzes | 4 | 3 | 12 |
Midterms | 1 | 15 | 15 |
Final | 1 | 17 | 17 |
Total Workload | 116 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | To have a grasp of basic mathematics, applied mathematics and theories and applications in Mathematics | |
2) | To be able to understand and assess mathematical proofs and construct appropriate proofs of their own and also define and analyze problems and to find solutions based on scientific methods, | |
3) | To be able to apply mathematics in real life with interdisciplinary approach and to discover their potentials, | |
4) | To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself, | 4 |
5) | To be able to tell theoretical and technical information easily to both experts in detail and non-experts in basic and comprehensible way, | |
6) | To be familiar with computer programs used in the fields of mathematics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level, | |
7) | To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement, | |
8) | To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense, | 4 |
9) | By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere, | |
10) | To be able to continue lifelong learning by renewing the knowledge, the abilities and the competencies which have been developed during the program, and being conscious about lifelong learning, | |
11) | To be able to adapt and transfer the knowledge gained in the areas of mathematics ; such as algebra, analysis, number theory, mathematical logic, geometry and topology to the level of secondary school, | |
12) | To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively. |