ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | DATS5007 | ||||
| Course Name: | Decision Support Systems | ||||
| Semester: | Spring | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Araş. Gör. KAZIM TİMUÇİN UTKAN | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The purpose of this course is to equip students with the theoretical foundations and practical skills required to design, implement, and evaluate decision support systems (DSS). Emphasizing on analytical modeling techniques and data processing tools, the course aims to prepare students for the challenges of decision-making in complex environments. Through hands-on experience and case studies, students will learn how to utilize technological resources to facilitate and improve the decision-making processes within organizations. |
| Course Content: | 1. Introduction to Decision Support Systems: key concepts and components 2. Decision Making Process: types of decisions, decision making under uncertainty 3. Decision Support System Modeling: linear, nonlinear, and discrete models 4. Data Warehousing and Data Mining for Decision Support: ETL processes and techniques 5. Advanced Topics in DSS: machine learning, artificial intelligence, and real-time decision systems |
Learning Outcomes
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The students who have succeeded in this course;
1) 1. Understand the principles and frameworks of decision support systems 2) Apply various modeling techniques for decision support 3) Integrate data warehousing and data mining methods for enhanced decision making 4) Evaluate the implications of incorporating AI and ML into decision support systems 5) Demonstrate the ability to design a functional decision support system prototype |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to the course; Overview of Decision Support Systems | - |
| 2) | Historical Development of DSS; Components and Classification of DSS | - |
| 3) | Decision Making in Organizations; Cognitive and Behavioral Aspects | - |
| 4) | Quantitative Models for Decision Making; Optimization and Simulation | - |
| 5) | Data Warehousing Foundations; Design and Implementation | - |
| 6) | Data Mining Concepts and Techniques; Classification, and Prediction | - |
| 7) | User Interfaces and Visualization in DSS; Dashboard Design Principles | - |
| 8) | MIDTERM EXAM | - |
| 9) | Decision Trees and Model-Based DSS; Prescriptive Analytics | - |
| 10) | Expert Systems and Knowledge Management for DSS | - |
| 11) | Neural Networks and Machine Learning in DSS | - |
| 12) | Integration of AI for Intelligent Decision Support Systems | - |
| 13) | Collaborative and Group Decision Support Systems | - |
| 14) | Real-time and Mobile DSS; Challenges and Solutions | - |
| 15) | Review of DSS Case Studies from Various Industries | - |
| 16) | FİNAL SINAVI | - |
Sources
| Course Notes / Textbooks: | 1. Turban, Efraim, et al. "Decision Support and Business Intelligence Systems." Prentice Hall Press. 2. Marakas, George M. "Decision Support Systems in the 21st Century." Pearson Education. |
| References: | 1. Power, D. J. "Decision Support Systems: Concepts and Resources for Managers." Quorum Books. 2. Sharda, Ramesh, Delen, Dursun, Turban, Efraim. "Business Intelligence and Analytics: Systems for Decision Support." Pearson Education. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| 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. | 3 | 3 | 2 | 3 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 2 | 3 | 2 | 3 | 2 |
| 3) Students who complete this program; Recognize the mathematics and code in application processes | 2 | 2 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 2 | 3 | 3 | 2 | 2 |
| 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. | 2 | 2 | 2 | 2 | 2 |
Course - Learning Outcome Relationship
| 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. | 2 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 2 |
| 3) | Students who complete this program; Recognize the mathematics and code in application processes | 3 |
| 4) | Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 2 |
| 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 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Midterms | 8 | 29 |
| Final | 8 | 86 |
| Total Workload | 157 | |