İŞLETİM SİSTEMLERİ TEMELLERİ PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
VBA109	İŞLETİM SİSTEMLERİ TEMELLERİ	1	3	3	7

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. ONUR TÜRKER
Instructor(s) of the Course Unit	Assoc.Prof. DERYA YILTAŞ KAPLAN
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	Teach students fundamentals of operating systems, design issues, algorithms and structures. Gaining theoretical information about operating systems.
Contents of the Course Unit:	Introduction, history. Processes: basic concepts, concurrent processes, mutual exclusion, process management, scheduling approaches. Deadlock and deadlock prevention approaches. Memory management: segmentation, paging, related methods, virtual memory. Input/Output. UNIX and other example operating systems.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Students will learn the basic concepts of process management.
Students will learn techniques for interprocess communication and synchronization. When given a problem involving concurrent processes, they will be able to design and code a solution to the problem.
Students will learn a range of algorithms for process scheduling and deadlock detection and avoidance.
Students will learn concepts of memory management (allocation, paging, segmentation, virtual memory).
Students will learn file management and input/output handling in operating systems.
Students will learn and be able to apply Unix system calls.
Students will be able to use operating systems features to solve real world problems.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Introduction	-
2	-	Operating systems: basic concepts, classification, history	-
3	-	Process management, time sharing, context switching, process management in UNIX	-
4	-	Threads, thread management, thread management in UNIX	-
5	-	Interprocess communication (IPC) and synchronization, semaphores, semaphores in UNIX	-
6	-	Classical problems on concurrent processes	-
7	-	Classical problems on concurrent processes	-
8	-	Deadlock, detection and avoidanc, shared memory in UNIX	-
9	-	Process scheduling algorithms, process scheduling in UNIX	-
10	-	MID-TERM EXAM	-
11	-	Memory management, segmentation, paging	-
12	-	Memory allocation, virtual memory management	-
13	-	File systems and management, UNIX file system	-
14	-	Input / Output	-
15	-	An overview	-
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Andrew Tanenbaum, Modern Operating Systems, Prentice-Hall, 2007.
Operating System Concepts, 7th Edition, John Wiley and Sons, Silberschatz, Galvin, and Gagne, ISBN 0-471-69466-5.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	30		Classical Exam
Homework Assessment	1	10
Short Exam	1	10
Final Exam	1	50		Classical Exam
TOTAL	4	100

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student gains proficiency in the fundamental components of data science and analytics, and is able to practically apply methods related to statistical analysis, data mining, and machine learning.		1
2	The student is capable of analyzing both structured and unstructured data types and can effectively utilize analytical methods to derive meaningful insights from large datasets.					4
3	The student can utilize programming languages such as Python, R, and SQL in data analysis and modeling processes and is able to effectively manage data processing and automation tasks.	0

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can express analytical findings clearly and effectively by using data visualization and result reporting techniques, contributing meaningfully to decision-making processes.		1
2	The student can analyze complex data-driven problems, develop appropriate solutions, and make creative, data-based decisions through the use of scientific research methods.				3

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can analyze problems encountered in the field of data science and analytics, develop solutions by selecting appropriate data analysis techniques, and critically evaluate statistical, algorithmic, and artificial intelligence-based methods.	0

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can effectively use programming languages such as Python, R, and SQL in data science and analytics applications; they are capable of developing practical solutions using data mining, machine learning, big data processing, data visualization, and modeling tools, and can work with real-world datasets.	0

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student is able to take responsibility in individual or team-based projects related to data science and analytics, independently plan and execute complex data-driven tasks, and play an active role in decision-making processes by developing analytical and creative solutions to encountered problems.					4

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student possesses the competence for continuous self-improvement with an awareness of lifelong learning by following current developments, technologies, and methods in the field of data science and analytics; they can rapidly acquire new knowledge and skills and apply them effectively.						5

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can communicate their work in data science and analytics clearly and effectively through written, oral, and visual means; they are capable of working efficiently in multidisciplinary teams, engaging in effective communication, and developing collaborative solutions.			2

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student has a strong command of the concepts, methods, algorithms, and tools specific to the field of data science and analytics; they can carry out data collection, processing, analysis, and interpretation processes in accordance with ethical principles, and act with a sense of responsibility regarding data privacy and security.				3

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	5	70
Preliminary & Further Study	14	7	98
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	2	4	8
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	0	0	0
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	0	0	0
Short Exam	2	2	4
Preparation for the Short Exam	0	0	0
TOTAL	34	0	182