Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Can understand the structure and functioning of decision support systems.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Can identify and design DSS components appropriate for a particular problem.
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2) Can apply multi-criteria decision-making methods.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) It can integrate decision support systems with big data and analytical approaches.
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2) Can develop sample systems using software tools of decision support systems.
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Introduction to Decision Support Systems |
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| 2) |
Decision Types and Processes |
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| 3) |
DDS Components (Data, Model, Interface) |
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| 4) |
Decision Types and Processes |
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| 5) |
Decision Matrices |
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| 6) |
Cost and Benefit Analysis |
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| 7) |
AHP (Analytic Hierarchy Process) |
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| 8) |
Midterm |
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| 9) |
Decision Under Uncertainty |
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| 10) |
Decision Making Under Uncertainty |
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| 11) |
Sensitivity Analysis |
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| 12) |
Sensitivity Analysis |
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| 13) |
Multi-objective programming |
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| 14) |
Multi-objective programming |
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. |
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| 2) |
It explains the principles of algorithm design and develops software for solving problems using at least one programming language. |
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| 3) |
It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. |
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| 4) |
Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. |
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| 5) |
They apply natural language processing techniques to text data and develop basic NLP-based applications. |
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| 6) |
It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. |
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| 7) |
It creates data-driven decision models using decision support systems. |
5 |
| 8) |
It develops optimization models and produces solutions for industrial and sectoral problems. |
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| 9) |
In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. |
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| 10) |
They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
3 |
42 |
| Laboratory |
0 |
0 |
0 |
| Application |
0 |
0 |
0 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
23 |
3 |
69 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
0 |
0 |
0 |
| Homework Assignments |
13 |
2 |
26 |
| Total Workload of Teaching & Learning Activities |
- |
- |
137 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
1 |
1 |
1 |
| Midterms |
1 |
1 |
1 |
| Semester Final Exam |
1 |
2 |
2 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
4 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
141 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |