COURSE INTRODUCTION AND APPLICATION INFORMATION

se.cs.ieu.edu.tr

Course Name

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
	Fall

Prerequisites

SE 116

To succeed (To get a grade of at least DD)

Course Language

Course Type

Required

Course Level

Mode of Delivery

Teaching Methods and Techniques of the Course

Problem Solving
Application: Experiment / Laboratory / Workshop

Course Coordinator

Doç. Dr. Cem EVRENDİLEK

Course Lecturer(s)

Assistant(s)

Course Objectives
Learning Outcomes	The students who succeeded in this course; be able to examine the loop structures of either a recursive or nonrecursive algorithms and infer its asymptotic running time and express its efficiency using big-Oh notation be able to assess the relative advantages of using array or linked list implementations in efficiently solving search problems with concurrent insertion, and/or deletions on collections of data, design be able to implement efficient computer programs running at the cost of O (log n) per searching, insertion and/or deletion of data items by employing correct variants of tree data structures covered in the course be able to develop efficient applications that require an order on data items by appropriately selecting the right sorting algorithm be able to describe the usage of various data structures be able to explain the operations for maintaining common data structures be able to design and apply appropriate data structures for solving computing problems be able to design simple algorithms for solving computing problems
Course Description

Course Category	Core Courses	X
	Major Area Courses
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials
1	Introduction: Mathematics Review and Recursion	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 1.1, 1.2, 1.3)
2	Programming Hints	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 1.4, 1.5, 1.6, 1.7)
3	Algorithm Analysis (basic concepts of algorithms, modeling runtimes, recurrences, BigOh notations)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 2.1, 2.2, 2.3)
4	Algorithm Analysis (Running Time Calculations)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 2.4)
5	Linear Data Structures: (Pointers, Linked Lists)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 3.1, 3.2, 3.3, 3.4, 3.5)
6	Linear Data Structures (Stacks)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 3.6)
7	Linear Data Structures (Queues)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 3.7)
8	Ara sınav / Midterm
9	Trees (Binary trees)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 4.1, 4.2)
10	Trees (Binary search trees)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 4.3)
11	Trees (AVL Trees)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 4.4)
12	Priority Queues: Binary Heaps	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 6.1, 6.2, 6.3)
13	Sorting (Insertion Sort, Shellsort)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 7.1, 7.2, 7.3, 7.4)
14	Sorting (Heapsort, Mergesort )	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 7.5, 7.6)
15	Sorting (Quicksort)	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006 (Ch. 7.7)
16	Review of the Semester

Course Notes/Textbooks	M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3/e, AddisonWesley, 2006
Suggested Readings/Materials

EVALUATION SYSTEM

Semester Activities	Number	Weigthing
Participation
Laboratory / Application	10	30
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade		60
Weighting of End-of-Semester Activities on the Final Grade		40
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: 16 x total hours)	16	2
Study Hours Out of Class	15	3
Field Work
Quizzes / Studio Critiques	5	2
Portfolio
Homework / Assignments		2
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms	1	15
Final Exams	1	20
		Total	170

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	Be able to define problems in real life by identifying functional and nonfunctional requirements that the software is to execute
2	Be able to design and analyze software at component, subsystem, and software architecture level		X
3	Be able to develop software by coding, verifying, doing unit testing and debugging					X
4	Be able to verify software by testing its behaviour, execution conditions, and expected results
5	Be able to maintain software due to working environment changes, new user demands and the emergence of software errors that occur during operation
6	Be able to monitor and control changes in the software, the integration of software with other software systems, and plan to release software versions systematically
7	To have knowledge in the area of software requirements understanding, process planning, output specification, resource planning, risk management and quality planning
8	Be able to identify, evaluate, measure and manage changes in software development by applying software engineering processes
9	Be able to use various tools and methods to do the software requirements, design, development, testing and maintenance		X
10	To have knowledge of basic quality metrics, software life cycle processes, software quality, quality model characteristics, and be able to use them to develop, verify and test software			X
11	To have knowledge in other disciplines that have common boundaries with software engineering such as computer engineering, management, mathematics, project management, quality management, software ergonomics and systems engineering			X
12	Be able to grasp software engineering culture and concept of ethics, and have the basic information of applying them in the software engineering		X
13	Be able to use a foreign language to follow related field publications and communicate with colleagues				X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest