Department of Computer 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	SE 116 To get a grade of at least FD
Course Language	English
Course Type	Required (Core Course)
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Problem Solving Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Prof. Dr. Cem Evrendilek
Course Lecturer(s)	Dr. Öğr. Üyesi Kaya Oğuz Prof. Dr. Cem Evrendilek Dr. Öğr. Üyesi Çağlar Durmaz
Assistant(s)	Araş. Gör. Melek Büşra Temuçin

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;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	will be able to analyze the loop structures of either recursive or non-recursive algorithms to express their asymptotic running times using big-Oh notation.					X
LO2	will be able to assess the relative advantages of using array or linked list implementations versus hashing in efficiently solving search problems with concurrent insertion, and/or deletions on collections of data.					X
LO3	will be able to develop efficient computer programs running in O (log n) per searching, insertion and/or deletion of data items by employing correct variants of tree data structures covered in the course.						X
LO4	will be able to select the right sorting algorithm for efficient applications requiring an order on data items.					X
LO5	will be able to describe the usage of various data structures.					X
LO6	will be able to explain the operations for maintaining common data structures.					X
LO7	will be able to devise appropriate data structures for solving specific computing problems.						X
LO8	will be able to use various graph algorithms to design solutions to simple computing problems.						X

Course Description

Algorithm analysis, linear data structures, trees, hashing, priority queues, sorting, and graph algorithms.

Related Sustainable Development Goals

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	Learning Outcome
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)	LO1
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)	LO1
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)	LO2
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)	LO2
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)	LO5
6	Trees (Binary search trees)	M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.3)	LO3
7	Trees (AVL Trees)	M. A. Weiss, Data Structures and Algorithm Analysis in in Java, 3/e, Pearson, 2012 (Ch. 4.4)	LO3
8	Midterm		-
9	Hashing	M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 5.1 – 5.5)	LO6
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)	LO7
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)	LO4
12	Sorting (Mergesort, Quicksort)	M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.6, 7.7)	LO4
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)	LO8
14	Graph Algorithms (Shortest Path Algorithms)	M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Bölüm 9.3)	LO8
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	Weighting	LO1	LO2	LO3	LO4	LO5	LO6	LO7	LO8
Laboratory / Application	1	40	X	X	X	X	X	X	X	X
Midterm	1	20	X	X	X		X
Final Exam	1	40				X		X	X	X
Total	3	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	16	2	32
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	13	3	39
Presentation / Jury	-	-	-
Project	-	-	-
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	24	24
Final Exam	1	25	25
		Total	210

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

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

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