Bilgisayar Mühendisliği

CE 221 | Ders Tanıtım Bilgileri

Dersin Adı

Data Structures and Algorithms I

Kodu	Yarıyıl	Teori (saat/hafta)	Uygulama/Lab (saat/hafta)	Yerel Kredi	AKTS
CE 221	FALL	3	2	4	7

Ön-Koşul(lar)	Array
Dersin Dili	English
Dersin Türü	Zorunlu
Dersin Düzeyi	Lisans
Dersin Veriliş Şekli	Face-To-Face
Dersin Öğretim Yöntem ve Teknikleri	Problem Solving Application: Experiment / Laboratory / Workshop Lecture / Presentation
Ulusal Meslek Sınıflandırma Kodu	-
Dersin Koordinatörü	Prof. Dr. Cem Evrendilek
Öğretim Eleman(lar)ı	Dr. Öğr. Üyesi Kaya Oğuz Prof. Dr. Cem Evrendilek Dr. Öğr. Üyesi Çağlar Durmaz
Yardımcı(ları)	Araş. Gör. Melek Büşra Temuçin

Dersin Amacı

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.

Öğrenme Çıktıları

Bu dersi başarıyla tamamlayabilen öğrenciler;

Ad	Açıklama	PC Alt	* Katkı Düzeyi
Ad	Açıklama	PC Alt	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.	5.2				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.	4				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.	3.2					X
LO4	will be able to select the right sorting algorithm for efficient applications requiring an order on data items.	4				X
LO5	will be able to describe the usage of various data structures.	2				X
LO6	will be able to explain the operations for maintaining common data structures.	2				X
LO7	will be able to devise appropriate data structures for solving specific computing problems.	3.1					X
LO8	will be able to use various graph algorithms to design solutions to simple computing problems.	3.2					X

Ders Tanımı

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

Dersin İlişkili Olduğu Sürdürülebilir Kalkınma Amaçları

Dersin Kategorisi	Temel Ders	X
	Uzmanlık/Alan Dersleri
	Destek Dersleri
	İletişim ve Yönetim Becerileri Dersleri
	Aktarılabilir Beceri Dersleri

HAFTALIK KONULAR VE İLGİLİ ÖN HAZIRLIK ÇALIŞMALARI

Hafta	Konular	Ön Hazırlık	Öğrenme Çıktısı
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		-

Ders Kitabı	M. A. Weiss. Data Structures and Algorithm Analysis in Java. 3/e. Pearson. 2012. 978-0132576277
Önerilen Okumalar/Materyaller	R. Sedgewick. K. Wayne. Algorithms. 4/e. Addison-Wesley Professional. 2011. 978-0321573513.

DEĞERLENDİRME ÖLÇÜTLERİ

Yarıyıl Aktiviteleri	Sayı	Katkı Payı %	LO1	LO2	LO3	LO4	LO5	LO6	LO7	LO8
Laboratuvar / Uygulama	1	40	X	X	X	X	X	X	X	X
Ara Sınav	1	20	X	X	X		X
Final Sınavı	1	40				X		X	X	X
Toplam	3	100

AKTS / İŞ YÜKÜ TABLOSU

Yarıyıl Aktiviteleri	Sayı	Süre (Saat)	İş Yükü
Katılım	-	-	-
Teorik Ders Saati	16	3	48
Laboratuvar / Uygulama Ders Saati	16	2	32
Sınıf Dışı Ders Çalışması	14	3	42
Arazi Çalışması	-	-	-
Küçük Sınav / Stüdyo Kritiği	-	-	-
Portfolyo	-	-	-
Ödev	13	3	39
Sunum / Jüri Önünde Sunum	-	-	-
Proje	-	-	-
Seminer/Çalıştay	-	-	-
Sözlü Sınav	-	-	-
Ara Sınavlar	1	24	24
Final Sınavı	1	25	25
		Toplam	210

DERSİN ÖĞRENME ÇIKTILARININ PROGRAM YETERLİLİKLERİ İLE İLİŞKİSİ

#	PC Alt	Program Yeterlilikleri / Çıktıları	* Katkı Düzeyi
#	PC Alt	Program Yeterlilikleri / Çıktıları	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering
	4	Computation
	5	Related engineering discipline-specific topics
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.					LO5 LO6
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems					LO7
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions					LO3 LO8
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.					LO2 LO4
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments				LO1
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (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) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
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
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.