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
SEN5104	Advanced System Analysis and Design II	Fall Spring	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 :	Assist. Prof. TAMER UÇAR
Recommended Optional Program Components:	None
Course Objectives:	Provides in depth the concepts, principals, methods, and best practices in software architectures; emphasizes on team projects to architect domain-specific architectures, service-oriented architectures, product-line architectures, adaptive and generative architectures. This course provides an overview for software engineering concepts and architectures. Students will work in small groups to design and implement software applications. The course will also provide a high-level overview of the software engineering discipline: software requirements, software design, software construction, software management, and software quality and testing.

Learning Outcomes

The students who have succeeded in this course;
1. Define the phases of the software development lifecycle and be able to define them.
2. Describe the difference between project and process metrics.
3. Define the terms version control and change control.
4. Describe methods for performing requirements elicitation and requirements analysis.
5. Discuss important design principles such as information hiding and abstraction.
6. Discuss the differences between structured and object oriented analysis and design.
7. Define key testing terms such as black box testing and white box testing.
8. Perform the activities of the software lifecycle for a small to medium software project.

Course Content

The content of this course is included several issues such as product, process, project management, metrics, project planning, systems engineering, analysis concepts, analysis modeling, risk, SQA, project scheduling, SCM, design concepts, architecture design, user interface design, other design topics, technical metrics, OO concepts, OOA, OOD, software testing techniques and strategies, software maintenance, software testing techniques and strategies, OO metrics and A case study in software architecture – the A-7E Operational Flight Program

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Product, Process
2)	Project Management, Metrics, Project Planning
3)	Systems Engineering
4)	Analysis Concepts, Analysis Modeling
5)	Risk, SQA, Project Scheduling, SCM
6)	Design Concepts
7)	Architecture Design, User Interface Design, Other Design Topics
8)	Architecture Design, User Interface Design, Other Design Topics / Midterm I
9)	Technical Metrics, OO Concepts, OOA, OOD
10)	Software Testing Techniques and Strategies
11)	Software maintenance, Software Testing Techniques and Strategies , OO Metrics
12)	Software maintenance, Software Testing Techniques and Strategies , OO Metrics / Midterm II
13)	A Case Study in Software Architecture – the A-7E Operational Flight Program
14)	Project Presentations

Sources

Course Notes / Textbooks:	Craig Larman Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development, 3/E ISBN-10: 0131489062 \| ISBN-13: 9780131489066 Roger S. Pressman Software Engineering: A Practitioner's Approach, Sixth Edition , McGraw-Hill
References:	Software Architecture in Practice, 2/e Bass, Clements & Kazman 2003 \| Addison-Wesley Professional \| Cloth; 560 pp ISBN-10: 0321154959 \| ISBN-13: 9780321154958

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Application	1	% 5
Quizzes	1	% 5
Homework Assignments	2	% 10
Project	1	% 10
Midterms	2	% 30
Final	1	% 40
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.
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.