PRIVATE SECURITY AND PROTECTION (TURKISH)
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN4016	Multivariate Data Analysis	Spring Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Associate (Short 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.

Learning Outcomes

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

Course Content

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.

Weekly Detailed Course Contents

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

Sources

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.

Evaluation System

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

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Upon the completion of the program, the students will be able to; Utilize the theoretical information they have acquired in private security sector
2)	Develop the skill of working in a team cooperatively
3)	Develop the skill of identifying and analyzing the vocational problems of private security and resolving them effectively.
4)	Develop the behavioral consciousness of occupational ethics and sense of responsibility
5)	Develop an awareness for life long learning and physical progress
6)	Develop the skill of having information about daily problems and developments about private security.
7)	Comprehend the laws and regulations of the sector.
8)	Develop the skill of effective communication.
9)	Develop the skill of adopting the security technologies.
10)	Develop the skill of planning and practicing vocational processes.
11)	Develop the skill of having entrepreneurship personality
12)	Develop the skill of communicating in the private security sector in a foreign language.