Running Jupyter Notebooks locally is great, but having a persistent, always-available server in the cloud takes your work to another level. This course is designed to guide you through exactly that process. You will learn to build a secure, custom Virtual Private Cloud (VPC) on AWS from scratch, launch an EC2 instance within it, and configure that server to run Jupyter Notebooks automatically. The focus is on giving you a permanent, accessible environment where you can pick up your work from anywhere, with all your code and results intact.
  This Course Offers
  · A Complete Walkthrough of Building a Custom AWS VPC: You will learn to create and configure the core network components, including subnets, internet gateways, and route tables, to host your resources securely.
  · Hands-On Experience Launching and Configuring an EC2 Instance: The course guides you through provisioning an Ubuntu server on EC2, the compute foundation for your Jupyter setup.
  · Skills to Set Up and Secure a Persistent Jupyter Server: You will configure nginx, supervisor, and Jupyter itself to create a robust, automatically running server accessible via a public IP.
  · An Understanding of Running Jupyter in the Cloud: By the end, you will have a live, cloud-based Jupyter environment and a clear understanding of the infrastructure that powers it.
  Why We Love This Course
  1. It teaches a highly practical, real-world skill: For data scientists and Python developers, having a persistent cloud notebook is incredibly useful. This course provides a direct path to building that tool.
  2. It bridges networking, cloud, and data science tools: You gain exposure to AWS networking concepts (VPCs) alongside application setup, building a well-rounded cloud skillset.
  3. It has helped over 23,000 students: The large student base suggests the course's approach has been valuable for many learners seeking this specific setup.
  4. The focus is on a complete, working system: You are not just learning individual parts; you are building an integrated, functional environment from the ground up.
  A Note on Course Currency
  This course was last updated in 2019. The AWS Management Console, EC2 launch wizards, and even some Jupyter security recommendations have changed. The concepts (VPCs, subnets, EC2, nginx) are still completely relevant, but the specific buttons, menus, and commands shown may not match the current AWS interface. You should be prepared to adapt and potentially consult current AWS documentation for the exact steps.

· Data scientists and Python developers who want a persistent, cloud-based Jupyter environment they can access from anywhere.
  · Data scientists looking for "pick up where you left off" functionality with all code and results preserved.
  · Pythonistas and programmers interested in getting into data science and needing a robust, scalable setup.
  · Anyone wanting to learn how to deploy a real application on AWS by building the underlying network and server infrastructure.

· Some experience with Python and/or the command line is recommended.
  · An interest in cloud computing and a willingness to work with AWS services is essential.
  · No prior deep knowledge of AWS networking is required, as the VPC is built from scratch.

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