USEFUL INFORMATION, RESOURCES & SERVICES FOR STUDENTSUSEFUL LINKS AND DOCUMENTS ADITIONAL & SUPPORTING INFORMATION

SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
BGT5102	Computer Networks	0	Fall	3	0	3	6

Course Type :	University Elective
Cycle:	Associate TQF-HE:5. Master`s Degree QF-EHEA:Short Cycle EQF-LLL:5. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor ABDULLAH ALAGÖZ
Dersin Öğretim Eleman(lar)ı:	Instructor ABDULLAH ALAGÖZ
Dersin Kategorisi:

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The Computer Networks course aims to provide students with a comprehensive understanding of the fundamental structures that enable data communication between computers and digital devices. Within this scope, the course covers the OSI and TCP/IP reference models, data transmission processes, and the interaction between network layers in detail. Students learn about various network topologies (LAN, WAN, MAN), IP addressing, and subnet masks to understand the principles of network design and operation. In addition, the course focuses on the working principles and interconnections of network devices such as routers, switches, hubs, access points, and modems. Students gain knowledge about how individual and enterprise-level network infrastructures are planned, established, and managed. Through both theoretical instruction and practical applications, students develop the ability to configure, test, and maintain computer networks effectively.

Course Content:

The main objective of this course is to enable students to identify and analyze the basic components of computer networks accurately. Students compare the OSI and TCP/IP models to understand the functions of each communication layer and gain hands-on experience in topics such as IP addressing, subnetting, routing, and configuring network topologies. This helps them develop systematic and analytical thinking skills in network design and management. Another objective is to help students acquire the ability to configure network devices, monitor network traffic, and analyze and troubleshoot connection problems. Through laboratory and simulation-based exercises, students learn to design and implement network systems similar to real-world environments. These competencies prepare them to work as network technicians, system support specialists, or network administrators in the information technology sector after graduation.

Course Specific Rules

1) Regular attendance is expected.

2) Assignments, projects, and applications must be submitted on the specified dates.

3) Participation in quizzes and exams is mandatory.

4) Adherence to academic integrity rules is essential.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
1) Explains the OSI and TCP/IP reference models and identifies the functions of each layer.
2) Compares LAN, WAN, and MAN topologies and explains their appropriate use cases.
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Performs IP addressing and subnet mask calculations through practical applications.
2) Configures network devices (router, switch, access point, etc.) and performs basic connectivity tests.
Competences *(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)*
1) Independently analyzes network problems and develops solution strategies.
2) Follows new network technologies and protocols, managing his/her own learning process.
3) Works collaboratively in laboratory settings and communicates technical information effectively.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction to Computer Networks	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
2)	OSI Model Layers	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
3)	TCP/IP Model and Comparison with OSI	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
4)	Network Topologies and Components	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
5)	Fundamentals of IP Addressing (IPv4)	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
6)	Subnet Mask and Subnetting	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
7)	Static and Dynamic Routing	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
8)	Midterm Exam	Bilgisayar Ağları - Prof. Dr. Resul KARA	Online Beykoz
9)	Switching and VLAN Concepts	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
10)	Student Industry Meeting - Network Protocols (ARP, ICMP, DNS, DHCP)	Materyal	Online Beykoz
11)	Network Traffic Monitoring and Troubleshooting	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
12)	Wireless Networks and Basic Security Measures	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
13)	Network Security, Access Control, and Firewalls	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz
14)	Course Review and Final Practice	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal	Online Beykoz

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	4	5	6	7
Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2)	It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3)	It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4)	Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5)	They apply natural language processing techniques to text data and develop basic NLP-based applications.
6)	It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7)	It creates data-driven decision models using decision support systems.
8)	It develops optimization models and produces solutions for industrial and sectoral problems.
9)	In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10)	They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Problem Solving
Demonstration
Views
Laboratory
Course Conference
Questions Answers
Individual and Group Work

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Quiz

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Quizzes	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	1	14
Laboratory	14	2	28
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	0	0	0
Total Workload of Teaching & Learning Activities	-	-	42
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	1	20	20
Midterms	1	41	41
Semester Final Exam	1	50	50
Total Workload of Assesment & Evaluation Activities	-	-	111
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6

Course Notes / Textbooks:	Online Beykoz Bilgisayar Ağları - Prof. Dr. Resul KARA
References:	Bilgisayar Ağları - Prof. Dr. Resul KARA Materyal