An intensive overview of cloud infrastructure and their role in data science. Topics will include storage as a service, ephemeral computing resources, auto-scaling, and event-driven workloads. Special attention will be paid to cloud-native design patterns, which are built assuming the unique functionality of cloud computing resources.
- EC2 Bootstrapping
- Python Scripting - with example
- Working with S3
- Working with SQS
- Working with CloudFormation
- Custom Jupyter Instance
- Terraform
- Lab 1 - Reference Architecture 1 (due 1/23/2026)
- Lab 2 - Creating & Managing EC2 Instances (due 1/30/2026)
- Lab 3 - EC2 Instance Management and IAM Roles (due 2/6/2026)
- Lab 4 - Infrastructure as Code with Python and
boto3(due 2/13/2025) - Lab 5 - Event-Driven Architecture with SNS and S3 (due 2/20/2025)
- Lab 6
- Lab 7
- Lab 8
- Lab 9
- Lab 10
To be completed by end of semester:
- Grad Lab 11 - Run A Hypervisor
- Grad Lab 12 - Work with a data lake in S3
- Lab 13
- Lab 14
- Lab 15