Information Package / Course Catalogue
Advanced Cloud Computing Applications
Course Code: MCS538
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

This course is designed to provide advanced knowledge and practical skills in Cloud Computing Application. The course focuses on hands-on learning experiences and real-world applications of cloud computing technologies and platforms. Students will explore advanced topics, tools, and techniques used in developing, deploying, and managing cloud-based applications.

Course Content

Develop an in-depth understanding of advanced cloud computing concepts, architectures, and models. Explore advanced cloud services and platforms, including AWS, Azure, and Google Cloud. Gain practical experience in designing, implementing, and deploying cloud-based applications. Understand cloud-native development practices and microservices architecture. Learn advanced techniques for cloud storage, database management, and data analytics in the cloud. Explore security considerations and best practices for cloud-based applications. Investigate containerization and orchestration tools such as Docker and Kubernetes. Discuss emerging trends and future directions in cloud computing applications.

Name of Lecturer(s)
Learning Outcomes
1.Demonstrate an advanced understanding of cloud computing concepts, architectures, and models.
2.Apply cloud-native development practices and microservices architecture in real-world scenarios.
3.Utilize advanced features and services of major cloud platforms, including AWS, Azure, and Google Cloud.
4.Design and implement cloud-based applications, considering scalability, availability, and performance.
5.Apply cloud-based data analytics and machine learning techniques for large-scale data processing and insights.
Recommended or Required Reading
1."Cloud Computing: A Hands-On Approach", Arshdeep Bahga and Vijay Madisetti.
2."Cloud Computing: Principles and Paradigms", Rajkumar Buyya, James Broberg, and Andrzej Goscinski.
3."Cloud Native Development Patterns and Best Practices", John Gilbert.
4."Cloud Computing: From Beginning to End", Ray J. Rafaels.
5."Cloud Computing: Theory and Practice", Dan C. Marinescu.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Advanced Cloud Computing. Overview of cloud computing principles and models. Cloud-native development and microservices architecture
Week 2 - Theoretical
Advanced Cloud Services and Platforms I. In-depth exploration of AWS, Azure, and Google Cloud platforms. Advanced features and services for cloud-based applications.
Week 3 - Theoretical
Advanced Cloud Services and Platforms II.
Week 4 - Theoretical
Designing and Implementing Cloud Applications I. Advanced application design considerations for the Cloud. Hands-on exercises in developing cloud-based applications.
Week 5 - Theoretical
Designing and Implementing Cloud Applications II.
Week 6 - Theoretical
Designing and Implementing Cloud Applications III.
Week 7 - Theoretical
Cloud Storage and Database Management I. Advanced cloud storage services and techniques. Database management in the cloud and scalable data solutions.
Week 8 - Theoretical
Cloud Storage and Database Management II.
Week 9 - Theoretical
Data Analytics and Machine Learning in the Cloud I. Leveraging cloud resources for big data processing and analytics. Cloud-based machine learning and AI services.
Week 10 - Theoretical
Data Analytics and Machine Learning in the Cloud II.
Week 11 - Theoretical
Data Analytics and Machine Learning in the Cloud III.
Week 12 - Theoretical
Cloud Security and Compliance. Security challenges and best practices for cloud-based applications. Compliance considerations and data privacy in the Cloud.
Week 13 - Theoretical
Containerization and Orchestration I. Introduction to containerization with Docker. Orchestration and scalability using Kubernetes
Week 14 - Theoretical
Containerization and Orchestration II.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Project1%15
Midterm Examination1%10
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142377
Project120323
Individual Work140114
Midterm Examination110313
Final Examination120323
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
4
4
4
4
4
4
4
4
4
OÇ-2
3
3
3
3
3
3
3
3
3
OÇ-3
4
4
4
4
4
4
4
4
4
OÇ-4
4
4
4
4
4
4
4
4
4
OÇ-5
4
4
4
4
4
4
4
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026