PSYCHOLOGY
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)	To develop an interest in the human mind and behavior, to be able to evaluate theories using empirical findings, to understand that psychology is an evidence-based science by acquiring critical thinking skills.
2)	To gain a biopsychosocial perspective on human behavior. To understand the biological, psychological, and social variables of behavior.
3)	To learn the basic concepts in psychology and the theoretical and practical approaches used to study them (e.g. basic observation and interview techniques).
4)	To acquire the methods and skills to access and write information using English as the dominant language in the psychological literature, to recognize and apply scientific research and data evaluation techniques (e.g. correlational, experimental, cross-sectional and longitudinal studies, case studies).
5)	To be against discrimination and prejudice; to have ethical concerns while working in research and practice areas.
6)	To recognize the main subfields of psychology (experimental, developmental, clinical, cognitive, social and industrial/organizational psychology) and their related fields of study and specialization.
7)	To acquire the skills necessary for analyzing, interpreting and presenting the findings as well as problem posing, hypothesizing and data collection, which are the basic elements of scientific studies.
8)	To gain the basic knowledge and skills necessary for psychological assessment and evaluation.
9)	To acquire basic knowledge of other disciplines (medicine, genetics, biology, economics, sociology, political science, communication, philosophy, anthropology, literature, law, art, etc.) that will contribute to psychology and to use this knowledge in the understanding and interpretation of psychological processes.
10)	To develop sensitivity towards social problems; to take responsibility in activities that benefit the field of psychology and society.
11)	To have problem solving skills and to be able to develop the necessary analytical approaches for this.
12)	To be able to criticize any subject in business and academic life and to be able to express their thoughts.