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Cloud Computing Course

This course provides a comprehensive introduction to cloud computing, its fundamental concepts, architecture, and service models (IaaS, PaaS, SaaS). You will explore leading cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) through hands-on labs, real-world use cases, and guided projects.

The course covers essential cloud services including compute, storage, networking, and security. You'll also learn about virtualization, containers, serverless computing, cloud deployment models (public, private, hybrid), and the principles of scalable and resilient cloud architecture.

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💡 Learning Objectives

By the end of the course, students will be able to

Define core cloud computing concepts, service models, and deployment models

Describe the architecture and key components of cloud environments

Explain cloud security principles, identity management, and data protection

Integration

Understand cost models and best practices for performance and reliability

Explain cloud security principles, identity management, and data protection

Deploy and manage basic cloud infrastructure using a major cloud provider (e.g., AWS or Azure)

Use tools like virtual machines, containers (Docker), and basic automation (IaC)

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

👨 Assessment

  • Weekly quizzes
  • Practical lab assignments
  • Final project or cloud deployment
  • Participation in discussions / Q&A

📦Ideal For

  • Students interested in cloud careers
  • IT professionals transitioning to cloud roles
  • Developers looking to expand into DevOps or cloud architecture
  • Anyone preparing for cloud certifications (AWS CCP, Azure Fundamentals, etc.)

📦 Technologies & Tools Covered:

Introduction to Cloud Computing

Cloud Service & Deployment Models

Virtualization & Containers

Cloud Providers Overview: AWS, Azure, GCP

Compute, Storage, and Networking in the Cloud

Cloud Security & IAM (Identity and Access Management)

Cloud Architecture and Design Principles

Serverless Computing & Microservices

DevOps and Automation (CI/CD, IaC)

Cloud Monitoring, Logging, and Cost Management

Capstone Project / Final Lab

📚 Cloud Computing Course

🖥️ 1. Cloud Platforms (IaaS / PaaS / SaaS)
  • These are the primary environments students work in.
  • Amazon Web Services (AWS) – Most widely used, great for beginners and professionals alike
  • Microsoft Azure – Popular in enterprise and hybrid environments
  • Google Cloud Platform (GCP) – Strong in data, AI, and open-source integration
  • Many beginner courses use AWS Free Tier for hands-on labs.
  • Used to teach infrastructure fundamentals and portable deployments.
  • VirtualBox / VMware Workstation – For local virtualization labs (e.g., creating virtual machines)
  • Docker – Most popular containerization tool
  • Kubernetes (K8s) – For container orchestration (covered in advanced courses)
  • To automate cloud deployments and teach DevOps concepts.
  • Terraform – Platform-independent IaC tool
  • AWS CloudFormation – AWS-native IaC tool
  • Ansible – Automation and configuration management
  • Git – Version control for code and infrastructure scripts
  • CI/CD Tools: GitHub Actions, Jenkins, or AWS CodePipeline (depending on focus)
  • Key for understanding cloud security and IAM principles.
  • AWS IAM / Azure Active Directory – To manage users, roles, permissions
  • OpenSSL – For certificates, encryption tasks
  • CloudTrail / CloudWatch / Azure Monitor – Monitoring and auditing tools
  • Used to interact with cloud resources programmatically or via GUI.
  • Cloud Consoles – Web interfaces for AWS, Azure, GCP
  • Command-Line Interfaces (CLIs): aws cli (AWS) az cli (Azure) gcloud (GCP)
  • VS Code / IntelliJ – IDEs used for cloud app development
  • Postman – For testing APIs deployed in the cloud
  • AWS CloudWatch / Azure Monitor / GCP Operations Suite – For logs and metrics
  • AWS CloudWatch / Azure Monitor / GCP Operations Suite – For logs and metrics
  • Cost Explorer (AWS) / Azure Cost Management – Budgeting and tracking usage
  • Grafana / Prometheus – For custom dashboards (advanced courses)
  • To give students hands-on practice safely.
  • Qwiklabs (Google) – Guided GCP labs
  • AWS Academy / AWS Educate – Education-focused AWS accounts
  • Microsoft Learn / Azure Sandbox – Free Azure learning environment
  • Katacoda (now part of O'Reilly) – Interactive browser-based labs (no setup needed)
  • For working with cloud data services.
  • Amazon S3 / Azure Blob Storage / GCP Cloud Storage – Object storage
  • Amazon RDS / Azure SQL / Google Cloud SQL – Managed relational databases
  • DynamoDB / Cosmos DB / Firestore – NoSQL databases
  • Serverless Framework / AWS SAM – For deploying serverless apps
  • Cloud SDKs (Python Boto3, Azure SDK for .NET, etc.) – For programmatic cloud control
  • Apache Airflow / Dataflow / Glue – For cloud data pipelines (data engineering focus)

🌩️ Cloud Computing Course Content Outline

Module 1: Introduction to Cloud Computing
  • What is Cloud Computing?
  • History and evolution
  • Characteristics of cloud computing
  • Benefits and challenges
  • Types of Cloud Environments: Public, Private, Hybrid, Community
  • Cloud Service Models: IaaS, PaaS, SaaS
  • Key Cloud Providers (AWS, Azure, Google Cloud, IBM Cloud)
  • Front-end and back-end architecture
  • Cloud delivery models explained
  • Virtualization in the cloud
  • Multi-tenancy and resource pooling
  • Load balancing, scalability, elasticity
  • Introduction to virtualization
  • Hypervisors (Type 1 and Type 2)
  • Virtual machines vs containers
  • Introduction to Docker and Kubernetes
  • Container orchestration basics
  • Deploying applications in the cloud
  • Serverless computing (FaaS)
  • Microservices architecture
  • Edge computing and fog computing
  • Amazon Web Services (AWS) EC2, S3, Lambda, RDS, VPC
  • Microsoft Azure Virtual Machines, Blob Storage, Azure Functions, SQL Database
  • Google Cloud Platform (GCP) Compute Engine, Cloud Storage, App Engine, BigQuery
  • Optionally include hands-on labs using free tiers of each platform.
  • Cloud storage types: Object, Block, File
  • Data redundancy and availability
  • Cloud-native databases (e.g., DynamoDB, Firestore)
  • Backups and disaster recovery
  • IP addressing, subnets, VPCs
  • VPNs, NAT gateways, Load balancers
  • CDN (Content Delivery Networks)
  • DNS and traffic routing
  • Shared responsibility model
  • Identity and Access Management (IAM)
  • Data encryption at rest and in transit
  • Compliance: GDPR, HIPAA, ISO, SOC
  • Cloud security best practices
  • Infrastructure as Code (IaC) with Terraform or AWS CloudFormation
  • CI/CD tools: Jenkins, GitHub Actions, AWS CodePipeline
  • Monitoring & logging: CloudWatch, Azure Monitor, GCP Operations
  • Automation and configuration management (e.g., Ansible)
  • Pricing models (pay-as-you-go, reserved instances)
  • Billing tools and dashboards
  • Cost-saving strategies (auto-scaling, spot instances)
  • Build a cloud-native web application (with serverless backend and database)
  • Deploy a Kubernetes cluster with CI/CD pipeline
  • Implement a multi-region architecture
  • AI and ML in the cloud
  • Quantum computing in the cloud
  • Sustainability in cloud computing
  • Emerging providers and technologies
  • Certification Prep: AWS Certified Cloud Practitioner, Azure Fundamentals, GCP Cloud Digital Leader
  • Capstone Project
  • Industry use cases (e.g., healthcare, finance, IoT)

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Ideal for fast-paced learning with daily live sessions and hands-on projects

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