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
ENM5203	Statistical Data Analysis and Decision Making	Fall	3	0	3	12

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 :	Assoc. Prof. ETHEM ÇANAKOĞLU
Course Lecturer(s):	Prof. Dr. SELİM ZAİM Assoc. Prof. YÜCEL BATU SALMAN
Recommended Optional Program Components:	N/A
Course Objectives:	This course provides the use of statistical and quantitative methods using computer technology to examine and explore data. It aims to build and interpret models from this data for decision making in all functional areas.

Learning Outcomes

The students who have succeeded in this course;
Ability to make decisions with statistical methods, ability to collect meaningful data, ability to interpret data and transform it into information/knowledge

Course Content

Methods covered include: collecting data, summarizing and exploring data, hypothesis testing, confidence intervals, regression analysis, ANOVA.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction: Description of data
2)	Data collection
3)	Descriptive Statistics
4)	Confidence intervals
5)	Hypothesis testing
6)	Hypothesis testing
7)	Midterm
8)	Regression analysis
9)	Regression analysis
10)	ANOVA
12)	ANOVA
12)	SPSS
13)	Project presentations
14)	Project presentations

Sources

Course Notes / Textbooks:	N/A
References:	Statistics for Business: Decision Making and Analysis, Robert A. Stine, Dean Foster, Pearson, 2011, 0321123913

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 35
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 25
PERCENTAGE OF FINAL WORK		% 75
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	4	56
Presentations / Seminar	1	15	15
Project	1	90	90
Midterms	1	40	40
Final	1	60	60
Total Workload			303

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