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
SEN5315	Service Oriented Architectures	Fall Spring	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 :	Prof. Dr. ADEM KARAHOCA
Course Lecturer(s):	Instructor BİLGİN EŞME
Recommended Optional Program Components:	None
Course Objectives:	Service Oriented Architecture (SOA) is a kind of computer philosophy that allow and guide to develop and integrate systems around system compatibilities and functionalities. Main objective of this course is to create such kind of philosophy to students.

Learning Outcomes

The students who have succeeded in this course;
1. Describe SOAP concept
2. Modify and contribute SOAP concept to design a new computer system and describe when it should be used.
3. Find new technologies like SOAP
4. Notice that there is no "away" idea with using of SOAP concept and provide existing systems more active with using SOAP technology.
5. Establish the idea that there is no system without using another one and find all problems' real reason in a system.
6. Find real and conscious solutions to the problems.
7. Adapt a new technology to the existing system.

Course Content

This course consists of introduction and information about sample organizations, presenting information about information technology that are used, presenting information about web technologies used and service oriented architecture, discussion on scenario which will be created, describe project subjects and discussion on project subjects, examination of the system to be followed step by step and project presentations.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction and information about sample organization
2)	Present information about information technology that will used.
3)	Present information about web technologies used and service oriented architecture
4)	Discussion on scenario which will be created.
5)	Describe project subjects and discussion on project subjects.
6)	Examination of the system to be followed step by step
7)	Examination of the system to be followed step by step
8)	Examination of the system to be followed step by step
9)	Examination of the system to be followed step by step
10)	Examination of the system to be followed step by step
11)	Project representation and evaluation
12)	Project representation and evaluation
13)	Project representation and evaluation
14)	Project representation and evaluation

Sources

Course Notes / Textbooks:	Understanding IBM SOA Foundation Suite: Learning Visually with Examples By Tinny Ng, Jane Fung, Laura Chan, Vivian Mak
References:	Dynamic SOA and BPM: Best Practices for Business Process Management and SOA Agility By Marc Fiammante

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 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	Duration (Hours)	Workload
Course Hours	14	3	42
Application	14	3	42
Study Hours Out of Class	14	3	42
Midterms	1	22	22
Final	1	41	41
Total Workload			189

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.