ECTS - Analysis and Design of Algorithms
Analysis and Design of Algorithms (ECON381) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Analysis and Design of Algorithms | ECON381 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer. |
| Course Lecturer(s) |
|
| Course Objectives | This course provides an understanding of the application of software technologies that enables users to make better and faster decisions based on big data features. Students will learn the principles and best practices for how to use big data in order to support fact-based decision-making. Emphasis will be given to applications in various data which has big data facilities. Therefore, in this course, the algorithms which are given in the class targeted the big data facilities in order to teach student this structure. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Review of algorithm analysis; divide and conquer algorithms; graphs; dynamic programming; greedy algorithms; randomized algorithms; P and NP; approximate algorithms for NP-hard problems or polynomial algorithms for subproblems of NP-hard problems; partial recursive functions; computations and undecidable problems. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Search and Sorting | |
| 2 | Divide and Conquer Algorithms | Lecture notes are available |
| 3 | Graphs, Project Proposal | Lecture notes are available |
| 4 | Dynamic Programming | Lecture notes are available |
| 5 | Dynamic Programming | Lecture notes are available |
| 6 | Greedy Algorithms | Lecture notes are available |
| 7 | Midterm Exam | Lecture notes are available |
| 8 | Rastgele Algoritmalar | Lecture notes are available |
| 9 | P and NP | Lecture notes are available |
| 10 | Work with NP Hard Problems | Lecture notes are available |
| 11 | Work with NP Hard Problems | Lecture notes are available |
| 12 | Partial Recursive function. | Lecture notes are available |
| 13 | Computations and Unsolvable Problems | Lecture notes are available |
| 14 | Computations and Unsolvable Problems, Final Presentation of Project, Final | Lecture notes are available |
| 15 | Computations and Unsolvable Problems, Final Presentation of Project, Final | Lecture notes are available |
| 16 | Fınal Exam |
Sources
| Course Book | 1. Introdution to Algoritms, Thomas H. Cormen, Charles E. Leiserson, Ron Rivest, Clifford Stein |
|---|---|
| Other Sources | 2. Ders Notları |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 15 | 1 |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | 2 | 20 |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 50 |
| Toplam | 19 | 91 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Having the conceptual and applied knowledge about the basic functions of the business and gaining the ability to use the knowledge | |||||
| 2 | To be able to diagnose the problems related to the field of business, to use scientific approaches effectively in the solution of problems and decision-making processes. | |||||
| 3 | To be able to reveal and analyze the environmental, social, global effects and legal consequences of the applications related to the basic functions of the business. | |||||
| 4 | To be able to analyze information and reports that concern businesses at the national, regional and global level, to set strategic goals according to the results obtained | |||||
| 5 | Gain the ability to use, report and interpret Business Information Systems and sub-modules required by business management | |||||
| 6 | Planning necessary activities such as starting a new business with innovative and creative thinking and taking risks for the sustainability of the business, finding resources, making market analysis, preparing a business plan, and using the learned information in this direction. | |||||
| 7 | Being aware of the fact that the research and learning process continues throughout life, to be able to follow the scientific and technological developments related to the business, to support oneself and the organization in innovation and continuous development. | |||||
| 8 | To have the necessary leadership and managerial skills to achieve business objectives effectively and efficiently. | |||||
| 9 | To be able to conduct scientific research in the field of business and report the results of the research to be used in managerial decision-making processes. | |||||
| 10 | To be able to transfer information about the field of business using effective verbal, written and visual communication methods in the language of learning and professional English | |||||
| 11 | To be aware of professional ethics, environmental awareness, sustainability, social responsibility, cultural, social and universal values | |||||
| 12 | Working effectively with different disciplines or multicultural teams, taking responsibility, making risk analysis, adapting to change, critical thinking and using initiative in problem solving | |||||
| 13 | . | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | 1 | 21 | 21 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 142 | ||