ECONOMICS
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN2212	Data Structures and Algorithms II	Spring	2	2	3	7

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi BETÜL ERDOĞDU ŞAKAR
Course Lecturer(s):	Dr. Öğr. Üyesi BETÜL ERDOĞDU ŞAKAR Dr. Öğr. Üyesi YÜCEL BATU SALMAN RA SEVGİ CANPOLAT RA MERVE ARITÜRK
Recommended Optional Program Components:	None
Course Objectives:	The objective of this course is to analyze data structures and algorithms used in software engineering in detail. After completing the course, the student will have knowledge of applying, implementing and analysis of data structures, including, trees, binary search trees, balanced search trees, heaps and graphs. Certain fundamental techniques, such as sorting, hashing and greedy algorithms are also taught.

Learning Outcomes

The students who have succeeded in this course;
The students who have succeeded in this course;
1) Describe and apply basic object oriented programming principles.
2) Implement basic data structures such as trees, binary search trees, balanced search trees, heaps and graphs.
3) Describe and implement sorting algorithms on common data structures.
4) Describe and implement searching algorithms on common data structures.
5) Implement and use hashing algorithms.
6) Implement and use greedy algorithms.
7) Choose and design data structures for writing efficient programs.

Course Content

The course content is composed of basic data structures like trees, binary search trees, balanced search trees, heaps, graphs and sorting, hashing and greedy algorithms.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction and Sorting Algorithms.	Sorting algorithms.
2)	Introduction to different tree structures.	Trees.
3)	Introduction to binary search trees.	Binary search trees.
4)	Implementing binary search tree using Java.	Binary search trees.
5)	Introduction to balanced trees and implementing AVL balanced tree structure using Java.	AVL trees.
6)	Using other balanced tree structure using Java.	Other balanced trees.
7)	Using heap structure and implementing them using Java.	Heap.
8)	Using heaps as priority queues. Midterm.	Heap.
9)	Analyzing and implementing hashing algorithms.	Hashing algorithms.
10)	Analyzing and implementing graph structure using Java.	Graph.
11)	Analyzing and implementing graph algorithms.	Graph algorithms.
12)	Analyzing and implementing greedy algorithms.	Greedy algorithms.
13)	Analyzing and implementing greedy algorithms. Quiz.	Greedy algorithms.
14)	Review.

Sources

Course Notes / Textbooks:	Data Structures & Problem Solving Using Java (Mark Allen Weiss) Data Structures and Algorithm Analysis in Java (Mark Allen Weiss) Data Structures and Abstractions with Java (Frank Carrano)
References:	Yok.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Application	4	% 10
Quizzes	1	% 10
Project	1	% 15
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 45
PERCENTAGE OF FINAL WORK		% 55
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Laboratory	14	2	28
Study Hours Out of Class	12	2	24
Project	10	2	20
Homework Assignments	2	5	10
Quizzes	5	2	10
Midterms	5	3	15
Final	10	3	30
Total Workload			165

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	As a world citizen, she is aware of global economic, political, social and ecological developments and trends.
2)	He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning.
3)	Absorbs basic economic principles and analysis methods and uses them to evaluate daily events.
4)	Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders.
5)	Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions.
6)	Comprehends new ways of doing business using digital technologies. and new market structures.
7)	Takes critical approach to economic and social problems and develops analytical solutions.
8)	Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods.
9)	In the works he/she contributes, observes individual and social welfare together and with an ethical perspective.
10)	Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines.
11)	Generates original and innovative ideas in the works she/he contributes as part of a team.