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
INE6150	Design of Experiments	Fall	3	0	3	8

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 :
Recommended Optional Program Components:	N.A.
Course Objectives:	The aim of the course is to introduce the most commonly used experiments in engineering studies, to discuss the ideas, principles and assumptions required for the construction, implementation, and validity of the analysis for each experimental design and to analyze the resulting data. Applications with statistical software packages are also utilized.

Learning Outcomes

The students who have succeeded in this course;
I. Explain the difference between fixed and random factors.
II. Recognize the difference between completely randomized design and randomized blocks.
III. Design and conduct experiments involving several factors using the factorial design approach.
IV. Use ANOVA to analyze the data from the experiments.
V. Analyze and interpret main effects and interactions.
VI. Design and conduct experiments involving the randomized complete block design.
VII. Design and conduct fractional factorial designs.
VIII. Assess model adequacy with residual analyses.
IX. Perform power analysis and calculate the sample size required for a design.

Course Content

Randomization, replication, blocking, transformations, fixed and random effect models, single factor experiments (analysis of variance), Latin squares, factorial designs, fractional factorial designs.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Designed Experiments
2)	Basic Statistical Methods
3)	Basic Statistical Methods
4)	Analysis of Variance
6)	Analysis of Variance
7)	Experiments with Blocking Factors
8)	Experiments with Blocking Factors
9)	Midterm Exam
10)	Factorial Experiments
11)	Factorial Experiments
12)	Two-Level Fractional Factorial Designs
13)	Two-Level Fractional Factorial Designs
14)	Project presentations

Sources

Course Notes / Textbooks:	Douglas C. Montgomery, 2012. Design and Analysis of Experiments, John Wiley & Sons, 8th Edition
References:	N.A.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 10
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	14	28
Presentations / Seminar	1	10
Project	4	40
Homework Assignments	4	40
Midterms	1	15
Final	1	20
Total Workload		195

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.