Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05040002 | Data Structures | 2 | Spring | 4 | 2+2+0 | 3 | 6 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Assoc. Prof. Ali ÖZTÜRK |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Assoc. Prof. Ali ÖZTÜRK | A-127 | [email protected] | 0 | Thursday 14:00-15:00 |
Course Content
Veri yapılarının sınıflandırılması, bellek ve zaman hususları. Bağlı listeler, yığınlar ve kuyruklar, ağaç yapıları, grafikler. Temel veri yapılarının ve uygulamalarının incelenmesi. Özyinelemeli uygulamalar.
Objectives of the Course
The aim is to provide an introduction to basic data structures, and algorithms for manipulating them, using an object-oriented programming language.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
| P3 | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose | 5 |
| P4 | Ability to develop, select and use modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; ability to use information technologies effectively | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knowledge of algorithm design and analysis techniques. | P.2.5 | 1,7 |
| O2 | Knowledge and use of software development platforms. | P.3.5 | 1,7 |
| O3 | Knowledge of at least one structured programming language. | P.3.11 | 1 |
| O4 | Self-defining a new computer engineering problem. | P.4.4 | 1 |
| O5 | Determining the talent required to solve problems and developing the necessary method. | P.4.5 | 7 |
| O6 | Application of developed methods to solve problems effectively and efficiently. | P.4.6 | 1,7 |
| O7 | Algorithm development knowledge and creation of appropriate data structure for the algorithm. | P.2.15 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction |
| 2 | Classification of data structures |
| 3 | Memory and time considerations |
| 4 | Linked lists |
| 5 | Stacks and queues |
| 6 | Tree structures |
| 7 | Graphs |
| 8 | Study of the basic data structures and their implementations |
| 9 | Recursive applications |
| 10 | Tree abstract data type, binary trees, binary search tree |
| 11 | Binary search tree examples |
| 12 | Balanced binary trees, AVL trees, Red-Black trees |
| 13 | Hash tables |
| 14 | Sorting, Sets and Selection |
Textbook or Material
| Resources | Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 1997 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | 6 | 15 (%) |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 35 (%) |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 4 | 56 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 25 | 25 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 6 | 3 | 18 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 25 | 25 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 180 | ||
| Total Work Load / 30 | 6 | ||
| Course ECTS Credits: | 6 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P3 | P4 |
|---|---|---|---|---|
| O1 | Knowledge of algorithm design and analysis techniques. | 2 | 4 | 3 |
| O2 | Algorithm development knowledge and creation of appropriate data structure for the algorithm. | 2 | 3 | 1 |
| O3 | Knowledge and use of software development platforms. | 5 | 1 | 5 |
| O4 | Knowledge of at least one structured programming language. | 1 | 4 | 2 |
| O5 | Self-defining a new computer engineering problem. | 3 | 1 | 4 |
| O6 | Determining the talent required to solve problems and developing the necessary method. | 5 | 2 | 1 |
| O7 | Application of developed methods to solve problems effectively and efficiently. | 4 | 3 | 1 |