YBS601


Course Title Course Code Program Level
MANAGEMENT DECISION SUPPORT SYSTEMS YBS601 Management Information Systems (Doctorate) M.A / M.Sc.

Course Term
(Course Semester)
Teaching and Learning Methods
Credits
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
01
(Fall)
42 88 70 200 3 8

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

 

 

 

 

 

 

LO2

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