BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
INE6103	Multivariate Data Analysis	Spring Fall	3	0	3	9

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:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :
Course Lecturer(s):	Prof. Dr. SELİM ZAİM Assoc. Prof. YÜCEL BATU SALMAN
Recommended Optional Program Components:	None
Course Objectives:	The purpose of this doctorate course is to broaden and enrich the student's knowledge and understanding of various topics in multivariate analysis and to provide some practical experience in their applications and interpretation. The focus will be on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting output, and presenting results of a complex nature.

Learning Outcomes

The students who have succeeded in this course;
1. Develop skills with a range of procedures and programs for multivariate data analysis.
2. Determine which multivariate technique is appropriate for a specific research problem.

Course Content

This course covers linear regression models, multiple regression models, dummy variable regression models, multicollinearity and its remedial measures, and multivariate statistical techniques such as; structural equation modelling, factor analysis, analysis of covariance and discriminant analysis.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to course
2)	Data examination and fundamentals of data manipulation
3)	Multiple Regression analysis
4)	Exploratory factor analysis
5)	Covariance based structural equation modeling technique
6)	Confirmatory factor analysis
7)	Review, Midterm Exam
8)	Path analysis using covariance based structural equation modeling technique
9)	Variance based structural equation modeling technique
10)	Path analysis using variance based structural equation modeling technique
11)	Neural network analysis
12)	Discriminant analysis
13)	Cluster analysis
14)	Review, Problem Session

Sources

Course Notes / Textbooks:	• Multivariate Data Analysis by Joseph F. Hair, Jr,, William C. Black, Barry J. Babin, Rolph E. Anderson, 7/E, Pearson, 2010. • Applied Multivariate Techniques by Subhash Sharma. John Wiley & Sons, Inc. 1996. • Regression Analysis by Example by Samprit Chatterjee and Ali S. Hadi. John Wiley & Sons, Inc. 2006.
References:	Various

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	13	3	39
Study Hours Out of Class	14	6	84
Homework Assignments	5	5	25
Midterms	1	15	15
Final	1	25	25
Total Workload			188

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)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.