NEW MEDIA | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
SEN2212 | Data Structures and Algorithms II | Spring Fall |
2 | 2 | 3 | 7 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
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. |
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. |
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. |
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. |
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. |
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 |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | To be able to critically interpret and discuss the theories, the concepts, the traditions, and the developments in the history of thought which are fundamental for the field of new media, journalism and communication. | |
2) | To be able to attain written, oral and visual knowledge about technical equipment and software used in the process of news and the content production in new media, and to be able to acquire effective abilities to use them on a professional level. | |
3) | To be able to get information about the institutional agents and generally about the sector operating in the field of new media, journalism and communication, and to be able to critically evaluate them. | |
4) | To be able to comprehend the reactions of the readers, the listeners, the audiences and the users to the changing roles of media environments, and to be able to provide and circulate an original contents for them and to predict future trends. | |
5) | To be able to apprehend the basic theories, the concepts and the thoughts related to neighbouring fields of new media and journalism in a critical manner. | |
6) | To be able to grasp global and technological changes in the field of communication, and the relations due to with their effects on the local agents. | |
7) | To be able to develop skills on gathering necessary data by using scientific methods, analyzing and circulating them in order to produce content. | |
8) | To be able to develop acquired knowledge, skills and competence upon social aims by being legally and ethically responsible for a lifetime, and to be able to use them in order to provide social benefit. | |
9) | To be able to operate collaborative projects with national/international colleagues in the field of new media, journalism and communication. | |
10) | To be able to improve skills on creating works in various formats and which are qualified to be published on the prestigious national and international channels. |