ECTS - Analysis and Design of Algorithms for Social Sciences
Analysis and Design of Algorithms for Social Sciences (ECON551) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Analysis and Design of Algorithms for Social Sciences | ECON551 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Social Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| 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;
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| 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 | Lecture notes are available |
| 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 | Randomized Algorithms | Lecture notes are available |
| 8 | P and NP | Lecture notes are available |
| 9 | Work with NP Hard Problems | Lecture notes are available |
| 10 | Work with NP Hard Problems | Lecture notes are available |
| 11 | Partial Recursive function | Lecture notes are available |
| 12 | Computations and Unsolvable Problems | Lecture notes are available |
| 13 | Computations and Unsolvable Problems, Final Presentation of Project | Lecture notes are available |
| 14 | Final Exam |
Sources
| Other Sources | 1. Ders Notları/ Lecture notes are available |
|---|
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 14 | 10 |
| 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 | - | - |
| Toplam | 17 | 50 |
| 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 | To compare main microeconomic theories, approaches and make a critical evaluation of each | X | ||||
| 2 | To compare main macroeconomic theories, approaches and make a critical evaluation of each | X | ||||
| 3 | To apply mathematical modeling | X | ||||
| 4 | To employ statistical and econometric tools in analyzing an economic phenomena | X | ||||
| 5 | To analyze the main economic indicators and comment on them | X | ||||
| 6 | To acquire theoretical knowledge through literature survey and derive empirically confirmable hypothesis | X | ||||
| 7 | To make a research design and carry it out within predetermined time frames | X | ||||
| 8 | To be able to develop new approaches for complex problems in applied economics and/or apply statistical/econometric tools to new areas/problems | X | ||||
| 9 | To formulate and present policy recommendations based on academic research | X | ||||
| 10 | To combine economic knowledge with other disciplines in order to solve problems requiring scientific expertise | X | ||||
| 11 | To use information technology effectively | X | ||||
| 12 | To continue learning and undertake advanced research independently | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 14 | 3 | 42 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentation/Seminar Prepration | 1 | 21 | 21 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 150 | ||