| Teaching Staff
|
Dr. Öğr. Üyesi Fatih SAĞLAM |
| Language of Instruction |
Türkçe (Turkish) |
| Type Of Course |
Compulsory |
| Prerequisites |
|
| Recommended Optional Programme Component |
There are no additional considerations. |
| Course Objectives |
The aim of this course is to introduce decision making and decision support processes in management activities, and to gain competencies for the use of data-based decision support systems and basic approaches in the world of solution-oriented information technologies. |
| Course Content |
Decision and decision structures, business intelligence and business analytics processes, artificial intelligence and DSS, recommender systems, expert systems, fuzzy logic, basic processes of data mining, descriptive-predictive analytics applications, artificial neural networks and text mining are discussed and also supported with KNIME analytic tool. |
| Learning Outcomes (LO) |
1. Knows the concepts of management, organization, efficiency, effectiveness, R&D, innovation and decision.
2. Knows decision making processes.
3. Calculate decision making approaches under uncertainty and risk.
4. Define Big Data, database, data warehouse and data market structures together with their relationships.
5. Knows the disciplines of Business Intelligence and Business Analytics and their effectiveness in KDS processes.
6. Knows the usage areas of Artificial Intelligence in the field of KDS.
7. Can design a basic KDS system with expert systems.
8. Can perform descriptive and predictive analytics processes and the most basic machine learning and artificial neural network algorithms used in these processes by using a tool. |
| Mode of Delivery |
Face to face |
| Course Outline |
| Week |
Topics |
| 1. Week |
Introduction – Social Change and Information Society |
| 2. Week |
Decision and Decision Structures |
| 3. Week |
Decision Support Systems and Analytics |
| 4. Week |
AI & DSS |
| 5. Week |
Recommender Systems – Expert Systems |
| 6. Week |
Fuzzy Logic |
| 7. Week |
Data Mining and Descriptive Statistics |
| 8. Week |
Mid Term |
| 9. Week |
Introduction to KNIME |
| 10. Week |
Descriptive Statistics with KNIME |
| 11. Week |
Predictive Analytics with KNIME - I |
| 12. Week |
Predictive Analytics with KNIME - II |
| 13. Week |
Artificial Neural Networks |
| 14. Week |
Text Mining |
|
| Assessment |
| |
Percentage(%) |
| Mid-term (%) |
40 |
| Quizes (%) |
|
| Homeworks/Term papers (%) |
20 |
| Practice (%) |
|
| Labs (%) |
|
| Projects/Field Work (%) |
|
| Seminars/Workshops (%) |
|
| Final (%) |
40 |
| Other (%) |
|
| Total(%) |
100 |
|
| Course Book (s) and/or References |
• “Analytics, Data Science, & Artificial Intelligence Systems for Decision Support”, 11e, R.Sharda, D.Delen, E. Turban, 2020.
• Vaughan, Daniel. Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise. " O'Reilly Media, Inc.", 2020.
• Presentations. |
| Work Placement(s) |
|
| The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO) |
| PQ1 | PQ2 | PQ3 | PQ4 | PQ5 | PQ6 | PQ7 | PQ8 | PQ9 | PQ10 | PQ11 | PQ12 | PQ13 | PQ14 | LO1 | 2 | 5 | 3 | 5 | | | | 3 | | 4 | 4 | | 4 | | LO2 | 3 | 4 | | 4 | | | | 3 | | 4 | 4 | | 4 | | LO3 | 3 | 3 | | 3 | | | | 3 | | 3 | 3 | | 3 | | LO4 | 3 | 2 | | 3 | | | | 3 | | 2 | 2 | | 2 | | LO5 | 3 | 3 | | 3 | | | | 3 | | 3 | 3 | | 3 | | LO6 | 2 | 3 | 1 | | | | 4 | | | 2 | 2 | | | |
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |