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Cloud Computing Course Syllabus

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Course Syllabus

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  • Linux architecture and command line basics
  • File system management, permissions, users, and groups
  • Process management, services, networking, and troubleshooting
  • Real-time Linux server administration practices
  • Bash scripting fundamentals
  • Variables, loops, conditions, and functions
  • Automating routine administrative tasks
  • Script-based monitoring and job scheduling
  • Cloud computing concepts and AWS global infrastructure
  • AWS regions, availability zones, and services overview
  • Shared responsibility model and cloud best practices
  • EC2 – Virtual servers, AMIs, key pairs, and security groups
  • VPC – Subnets, routing tables, internet gateways, NAT
  • S3 – Buckets, storage classes, lifecycle policies
  • IAM – Users, roles, policies, MFA, and access control
  • RDS & Aurora – Managed databases, backups, and availability
  • Route 53 – DNS, routing policies, and domain management
  • Elastic Load Balancer (ALB / NLB)
  • Auto Scaling Groups and high availability design
  • Fault tolerance and scalability concepts
  • CloudWatch – Metrics, logs, alarms, and alerts
  • CloudTrail – Auditing AWS account activities
  • SNS – Notifications and alert automation
  • AWS CloudFormation – Template-based resource provisioning
  • Terraform – Infrastructure automation, state management, and modules
  • Git repositories, branching, merging, and workflows
  • Version control for DevOps pipelines and automation code
  • Jenkins installation and configuration
  • Build pipelines, jobs, and automation workflows
  • CI/CD pipelines for application deployment
  • Docker architecture and components
  • Creating Docker images and containers
  • Docker networking, volumes, and registries
  • Working with Amazon ECR
  • Kubernetes architecture and components
  • Pods, services, deployments, and scaling
  • Kubernetes networking and configuration
  • Working with Amazon EKS
  • Ansible architecture and inventory
  • Playbooks, roles, variables, and automation tasks
  • Server configuration and application deployment automation
  • Prometheus – Metrics collection and monitoring
  • Grafana – Dashboards and performance visualization
  • Using AI tools to assist with scripting and automation
  • AI support for infrastructure templates and monitoring logic
  • Optimizing DevOps workflows using AI-assisted techniques
  • End-to-end AWS DevOps project implementation
  • CI/CD pipeline creation and deployment
  • Monitoring, scaling, and optimization
  • Team-based Codeathon experience simulating real IT environments

By completing this Cloud Computing Course Syllabus, learners gain practical exposure to cloud infrastructure, automation tools, deployment workflows, and monitoring practices. The training helps build confidence in working with cloud environments through hands-on learning and project-based practice.

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