FACULTY OF ENGINEERING
Department of Software Engineering
CE 221 | Course Introduction and Application Information
Course Name |
Data Structures and Algorithms I
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
CE 221
|
Fall
|
3
|
2
|
4
|
7
|
Prerequisites |
|
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Course Language |
English
|
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Course Type |
Required
|
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Course Level |
First Cycle
|
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Mode of Delivery | - | |||||||
Teaching Methods and Techniques of the Course | Problem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||||
Course Coordinator | ||||||||
Course Lecturer(s) | ||||||||
Assistant(s) |
Course Objectives | The objective of this course is to teach students the notion of an abstract data type (ADT) which is central to the design and analysis of computer algorithms. This course introduces abstract data types, and presents algorithms and data structures for implementing several ADTs. It emphasizes the efficiency of algorithms as evaluated by asymptotic analysis of running time. The programming assignments will be given in the programming languages taught in SE 115 and/or SE116. |
Learning Outcomes |
The students who succeeded in this course;
|
Course Description | Algorithm analysis, linear data structures, trees, hashing, priority queues, sorting, and graph algorithms. |
|
Core Courses |
X
|
Major Area Courses | ||
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
Week | Subjects | Related Preparation |
1 | Introduction: Mathematics Review and Recursion | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 1.1, 1.2, 1.3) |
2 | Algorithm Analysis (basic concepts of algorithms, modeling runtimes, recurrences, Big-Oh notations, Running Time Calculations) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.1, 2.2, 2.3) |
3 | Algorithm Analysis and Linear Data Structures: (Linked Lists) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.4, 3.1 - 3.5) |
4 | Linear Data Structures (Linked Lists, Stacks, Stack Applications) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.5, 3.6) |
5 | Linear Data Structures (Queues) and Trees (Binary trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.7, 4.1, 4.2) |
6 | Trees (Binary search trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.3) |
7 | Trees (AVL Trees) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.4) |
8 | Midterm | |
9 | Hashing | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 5.1 – 5.5) |
10 | Priority Queues: Binary Heaps | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 6.1, 6.2, 6.3) |
11 | Sorting (Insertion Sort, Shellsort, Heapsort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.1, 7.2, 7.3, 7.4, 7.5) |
12 | Sorting (Mergesort, Quicksort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.6, 7.7) |
13 | Graph Algorithms (Definitions, Representation, Topological Sort) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.1 - 9.2) |
14 | Graph Algorithms (Shortest Path Algorithms) | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.3) |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012, 978-0132576277 |
Suggested Readings/Materials | R. Sedgewick, K. Wayne, Algorithms, 4/e, Addison-Wesley Professional, 2011, 978-0321573513 |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application |
1
|
40
|
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project | ||
Seminar / Workshop | ||
Oral Exams | ||
Midterm |
1
|
20
|
Final Exam |
1
|
40
|
Total |
Weighting of Semester Activities on the Final Grade |
2
|
60
|
Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
Total |
ECTS / WORKLOAD TABLE
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
Study Hours Out of Class |
14
|
3
|
42
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
0
|
||
Portfolio |
0
|
||
Homework / Assignments |
1
|
40
|
40
|
Presentation / Jury |
0
|
||
Project |
0
|
||
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
1
|
24
|
24
|
Final Exam |
1
|
24
|
24
|
Total |
210
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
1
|
2
|
3
|
4
|
5
|
||
1 | To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. |
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2 | To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. |
X | ||||
3 | To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose. |
X | ||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively. |
X | ||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems. |
X | ||||
6 | To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually. |
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7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions. |
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8 | To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions. |
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9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. |
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10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. |
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11 | To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) |
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12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
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13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest