CE 221 | Course Introduction and Application Information

Course Name
Data Structures and Algorithms
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 221
Fall
3
2
4
7

Prerequisites
  SE 116 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Required
Course Level
First Cycle
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. Topics include linear ADTs, heaps, binary trees, AVL trees, hashing, sorting, and graph algorithms. The programming assignments will be given in the programming languages taught in SE 115 and/or SE116.
Course Description The students who succeeded in this course;
  • 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,
  • 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,
  • 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,
  • will be able to develop efficient applications that require an order on data items by appropriately selecting the right sorting algorithm,
  • will be able to select the right sorting algorithm for efficient applications requiring an order on data items,
  • will be able to describe the usage of various data structures,
  • will be able to devise appropriate data structures for solving specific computing problems,
  • will be able to use various graph algorithms to design solutions to simple computing problems..
Course Content The course covers algorithm analysis, linear data structures, trees, hashing, priority queues and sorting.

 



Course Category

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 Midterm
8 Trees (AVL Trees) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.4)
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 Graph Algorithms ( Minimum Spanning Tree) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.5)
16 Review of the Semester M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012

 

Course Notes/Textbooks

M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012

Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
8
30
Field Work
Quizzes / Studio Critiques
Homework / Assignments
15
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral 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
Homework / Assignments
15
3
Presentation / Jury
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
16
Final / Oral Exam
1
24
    Total
210

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems X
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose X
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice X
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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