| Data Science (Master) (with Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | VB5006 | ||||
| Course Name: | Decision Support Systems | ||||
| Semester: |
Fall Spring |
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| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | Asst. Prof. Şeyma BOZKURT UZAN | ||||
| Course Assistants: |
| Course Objectives: | • To learn certain algorithms under decision support systems and how these algorithms are used, • To develop new applications for decision support systems. • To discuss the future of decision support systems. |
| Course Content: | Rational decision-making and appropriate information support are key components of decision support systems (DSS). These systems encompass various elements such as data, information, databases, database management systems, knowledge bases, data warehouses, rule/model bases, expert system mechanisms, uncertainty factors, system dynamics and simulation, group decision support systems, executive information systems, user interface component recognition, as well as the design, implementation, and evaluation of DSS. |
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The students who have succeeded in this course;
1) Understand the concept of Multi-Criteria Decision Making and its importance in decision-making processes. 2) Gain knowledge about different MCDM techniques and their theoretical foundations. 3) Learn how to apply MCDM techniques using Excel. 4) Analyze decision problems with multiple criteria and identify suitable MCDM methods for specific scenarios. 5) Evaluate alternatives and prioritize them based on multiple criteria. 6) Develop practical problem-solving skills by applying MCDM techniques to real-world scenarios. |
| Week | Subject | Related Preparation |
| 1) | Decision Support Systems: Basic Concepts | Internet - Presentation |
| 2) | Multi-Criteria Decision Making | Internet - Presentation |
| 3) | Analytical Hierarchy Process - AHP | Internet - Presentation |
| 4) | AHP-Application | Internet - Presentation |
| 5) | DEMATEL | Internet - Presentation |
| 6) | DEMATEL - Application | Internet - Presentation |
| 7) | TOPSIS | Internet - Presentation |
| 8) | MIDTERM | |
| 9) | TOPSIS - Application | Internet - Presentation |
| 10) | ELECTRE - Application | Internet - Presentation |
| 11) | VIKOR - Application | Internet - Presentation |
| 12) | COPRAS - Application | Internet - Presentation |
| 13) | MOORA - Application | Internet - Presentation |
| 14) | PROMETHEE - Application | Internet - Presentation |
| Course Notes / Textbooks: | Çok Kriterli Karar Verme Yöntemleri Yazar: Bahadır Fatih Yıldırım , Emrah Önder Yayınevi : Dora Yayıncılık |
| References: | Data Science and Multiple Criteria Decision Making Approaches in Finance : Applications and Methods Author: Gökhan Silahtaroglu Publisher : SPRINGER |
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
6 |
|---|---|---|---|---|---|---|
| Program Outcomes | ||||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | ||||||
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | ||||||
| 3) Students who complete this program; Recognizes mathematics and code in application processes. | ||||||
| 4) Students who complete this program; Explain the effects of processes in data science on output and individual. | ||||||
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | ||||||
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognizes mathematics and code in application processes. | 3 |
| 4) | Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 50 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 16 | 48 |
| Midterms | 8 | 43 |
| Final | 8 | 53 |
| Total Workload | 144 | |