Computer Engineering Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course General Introduction Information

Course Code:

BYM202

Course Name:

Data Structures and Algorithms

Course Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Course Requirement:

Does the Course Require Work Experience?:

Type of course:

Necessary

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi KEMAL ÇAĞRI SERDAROĞLU

Course Lecturer(s):

Prof. Dr. RÜYA ŞAMLI

Course Assistants:

Course Purpose and Content

Course Objectives:	The aim of the course is to teach basic data structures and algorithms and how they can be used in different application areas.
Course Content:	Fundamentals of Algorithmic Problem Solving, Fundamentals of Algorithm Analysis, List and Linked Lists, Queue and Stack, Trees, Search Algorithms, Sort Algorithms, Divide and Conquer Algorithms, Graphs, Recurrence Relations

Learning Outcomes

The students who have succeeded in this course;
1) The student learns the correct and effective algorithm design.
2) The student learns important basic data structures such as heap, Queue, Tree, Graph and can use them correctly in problem solving.
3) The student learns the most important data structures and algorithms used today.
4) The student can analyze the best, average and worst working times of algorithms with algorithm analysis.
5) The student can solve new problems using techniques learned from standard algorithms.
6) To have professional and ethical responsibilities, to be able to take authority and fulfill the requirements
7) To be able to communicate effectively verbally and in writing in Turkish and English
8) Knowledge of the global and societal impacts of engineering practices on priority issues such as health, environment and safety; knowledge of contemporary issues; legal aspects of engineering solutions awareness of the consequences

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to data structures, basic data types and data concept
2)	Fundamentals of algorithm analysis, time and size complexities (asymptotic notations)
3)	Lists, linked list structures and applications
4)	Queue data structure and applications
5)	Stack data structure and applications
6)	Recursion concept
7)	Tree structure, binary trees, binary search trees
8)	Heap trees
9)	Hash structure, properties and applications
10)	Search algorithms, string search algorithms
11)	Sorting algorithms
12)	Divide and conquer algorithms
13)	Graflar, Graflar üzerinde gezinti (BFS, DFS)
14)	Minimum spanning tree, shortest path algorithms

Sources

Course Notes / Textbooks:	Introduction to Algorithms, Third Edition, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, The MIT Press, 2009. Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne, Addison-Wesley Professional, 2011.
References:	Ders Kitabı, Ders Notları

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Medium	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.	3
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.	3
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose	3
4)	Ability to devise, select, and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.	3
5)	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	2
6)	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.	2
7)	Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.	2
8)	Knowledge of the global and societal impacts of engineering practices on priority issues such as health, environment and safety and contemporary issues; knowledge of the legal aspects of engineering solutions. awareness of the consequences	2
9)	Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.	2
10)	Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and knowledge about sustainable development.	2
11)	Ability to design systems to meet desired needs	2
12)	Ability to apply basic sciences in the field of computer engineering	2
13)	Ability to implement designs by experiments	2
14)	Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.	2

Learning Activity and Teaching Methods

Course
Labs
Homework
Proje Hazırlama

Measurement and Evaluation Methods and Criteria

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Bireysel Proje
Raporlama

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Application	5	% 20
Homework Assignments	4	% 20
Midterms	1	% 20
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Aktiviteye Hazırlık	Aktivitede Harçanan Süre	Aktivite Gereksinimi İçin Süre	Workload
Course Hours	14	3			42
Laboratory	14	2			28
Study Hours Out of Class	14	6			84
Homework Assignments	4	8			32
Midterms	1	1			1
Final	1	1			1
Total Workload					188