| Course Objectives: |
This course examines the fundamental principles, design approaches, service models (IaaS, PaaS, SaaS), and deployment models (public, private, and hybrid) of cloud computing architectures in detail. It covers cloud-based technologies such as virtualization, containerization, and serverless computing. The course focuses on scalability, high availability, cost management, security, and performance optimization in cloud-based systems, while also providing practical applications using key services offered by popular cloud providers (AWS, Azure, and Google Cloud). |
| Course Content: |
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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) Define the basic concepts of cloud computing, service and delivery models (IaaS, PaaS, SaaS, public, private, hybrid) and explain the differences between these models.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Explain the key technologies used in cloud computing architectures (virtualization, containerization, serverless computing) and specify their roles in a cloud environment.
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2)
Manage the lifecycle of cloud solutions by applying fundamental principles of cloud security, cost management and performance optimization.
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3) Able to deploy and manage a basic cloud application using relevant cloud services, thus transforming theoretical knowledge into practical skill.
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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) Can design scalable, flexible and cost-effective cloud-based application architectures and select the most appropriate cloud services (from platforms such as AWS, Azure, Google Cloud) for a scenario
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Introduction and Basic Concepts |
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| 2) |
Core Cloud Technologies |
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| 3) |
Infrastructure (IaaS) Services |
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| 4) |
Platform (PaaS) and Software (SaaS) Services |
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| 5) |
Database and Data Management |
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| 6) |
Cloud Architecture Design Principles |
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| 7) |
Cloud Security and Identity Management |
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| 8) |
Midterm |
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| 9) |
Serverless Computing |
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| 10) |
DevOps and Cloud Computing |
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| 11) |
Major Cloud Providers and Application Development |
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| 12) |
Major Cloud Providers and Application Development |
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| 13) |
Cost Management and Optimization |
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| 14) |
Advanced Topics and Case Studies |
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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. |
5 |
| 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. |
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| 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 |
14 |
3 |
42 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
0 |
0 |
0 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
0 |
0 |
0 |
| Homework Assignments |
1 |
10 |
10 |
| Total Workload of Teaching & Learning Activities |
- |
- |
94 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
0 |
0 |
0 |
| Midterms |
1 |
20 |
20 |
| Semester Final Exam |
1 |
30 |
30 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
50 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
144 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |