CE 221 | Course Introduction and Application Information - Department of Software Engineering

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

Course Level

First Cycle

Mode of Delivery

-

Teaching Methods and Techniques of the Course

Problem Solving
Application: Experiment / Laboratory / Workshop
Lecture / Presentation

National Occupation Classification

-

Course Coordinator

Prof. Dr. Cem EVRENDİLEK

Course Lecturer(s)

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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	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.
2	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.
3	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.
4	will be able to select the right sorting algorithm for efficient applications requiring an order on data items.
5	will be able to describe the usage of various data structures.
6	will be able to explain the operations for maintaining common data structures.
7	will be able to devise appropriate data structures for solving specific computing problems.
8	will be able to use various graph algorithms to design solutions to simple computing problems.

Course Description

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

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

Week	Subjects	Related Preparation	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)
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

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6	LO 7	LO 8
Participation
Laboratory / Application	8	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

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

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	X	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	X
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	X	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 221 | Course Introduction and Application Information

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