YAZ209 Data Structures and AlgorithmsIstinye UniversityDegree Programs Software EngineeringGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Software Engineering

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

Course Introduction and Application Information

Course Code: YAZ209
Course Name: Data Structures and Algorithms
Semester: Fall
Course Credits:
ECTS
6
Language of instruction: Turkish
Course Condition:
Does the Course Require Work Experience?: No
Type of course: Compulsory Courses
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. Üy. ALI ASGHAR POUR HAJI KAZEM
Course Lecturer(s): Dr. Öğr. Üyesi Muhammed Davud, Araş. Gör. Yazım Beril Uluer
Course Assistants:

Course Objective and Content

Course Objectives: This course covers a range of topics, including basic data structures such as arrays, linked lists, stacks, queues, trees, and graphs, as well as more complex data structures such as hash tables and heaps. Students will also learn about algorithm analysis and design, including sorting and searching algorithms. Additionally, students will gain knowledge of algorithmic problem-solving techniques, algorithmic complexity, and the big-O notation. Throughout the course, students will be required to complete programming assignments and projects that apply the concepts covered in lectures and readings.
Course Content: This course covers a range of topics, including basic data structures such as arrays, linked lists, stacks, queues, trees, and graphs, as well as more complex data structures such as hash tables and heaps. Students will also learn about algorithm analysis and design, including sorting and searching algorithms. Additionally, students will gain knowledge of algorithmic problem-solving techniques, algorithmic complexity, and the big-O notation. Throughout the course, students will be required to complete programming assignments and projects that apply the concepts covered in lectures and readings.

Learning Outcomes

The students who have succeeded in this course;
1) Understand and apply the fundamental concepts of data structures and algorithms.
2) Understand and apply algorithm analysis and design techniques, including sorting and searching algorithms.
3) Analyze algorithmic problems and implement appropriate solutions using the concepts learned in the course.
4) Understand and apply algorithmic complexity and big-O notation to analyze and compare different algorithms.
5) Develop efficient and optimized algorithms for solving programming problems.

Course Flow Plan

Week Subject Related Preparation
1) Introduction
2) OOP
3) Asymptotic Analysis of Algorithms
4) Recursion
5) Lists and Stacks
6) Queues
7) Sorting Algorithms
8) Midterm Exam
9) Searching Algorithms
10) Trees
11) Heaps
12) Graphs
13) Graphs
14) Hashing

Sources

Course Notes / Textbooks: Weiss, Mark Allen. Data Structures and Algorithm Analysis in Java, Addison Wesley [either 2nd or 3rd edition]
ISBN: 0-321-37013-9 (2nd Edition, 2007), ISBN: 0-132-57627-9 (3rd Edition, 2011)
References: Ders Notları

Course - Program Learning Outcome Relationship

Course Learning Outcomes

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Program Outcomes
1) Adequate knowledge in mathematics, science and software engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2) Ability to identify, formulate, and solve complex software engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3) Ability to design, implement, verify, validate, measure and maintain a complex software system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in software engineering applications; ability to use information technologies effectively.
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or software engineering research topics.
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of software engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in software engineering; awareness of the legal consequences of software engineering solutions.

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Average 3 Highest
       
Program Outcomes Level of Contribution
1) Adequate knowledge in mathematics, science and software engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2) Ability to identify, formulate, and solve complex software engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3) Ability to design, implement, verify, validate, measure and maintain a complex software system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in software engineering applications; ability to use information technologies effectively.
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or software engineering research topics.
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of software engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in software engineering; awareness of the legal consequences of software engineering solutions.

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Quizzes 3 % 10
Homework Assignments 2 % 10
Project 1 % 15
Midterms 1 % 25
Final 1 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
total % 100

Workload and ECTS Credit Calculation

Activities Number of Activities Workload
Course Hours 13 39
Laboratory 13 26
Study Hours Out of Class 14 42
Project 2 10
Homework Assignments 4 12
Quizzes 2 4
Midterms 1 3
Final 1 3
Total Workload 139