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
SEN5999	Project	Spring	0	0	0	16

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:	Prep
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Prof. Dr. ADEM KARAHOCA
Recommended Optional Program Components:	None
Course Objectives:	This course explains to be competitive to the students in the marketplace by leveraging cutting-edge technologies. It offers students the opportunity of obtain practical exposure to modern Information Technology problems, familiarizes students with the expected level of professionalism in IT and Business project deliver and introduces students to relevant contemporary research practices and topics.

Learning Outcomes

The students who have succeeded in this course;
1. Obtain knowledge of current and emerging concepts in Information Systems
2. Apply cutting-edge technologies to real life business problems
3. Demonstrate teamwork abilities and outcome-oriented deliverables
4. Produce research artifacts such as papers, theses, prototype systems, integrating the knowledge obtained throughout the program

Course Content

The content of this course is composed of several literature researches, article analyses and methodologies, evaluation, report and presentations.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Literature research
2)	Article analysis and methodology
3)	Evaluation
4)	Report and presentation I
5)	Literature research
6)	Article analysis
7)	Evaluation
8)	Report and Presentation II
9)	Literature research
10)	Article analysis
11)	Evaluation
12)	Literature research
13)	Article analysis
14)	Report and presentation III

Sources

Course Notes / Textbooks:	Will be given weekly throughout the course.
References:	Yok

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 50
Project	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

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