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Cutting edge data science projects.

Docker is the world’s leading software container platform. Developers use Docker to eliminate “works on my machine” problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.

Docker Terminology:

1. Docker Containers: Small user-level virtualization (isolation) that helps you install, build and run your code/workflow. All the code would be continuosly running in these containers.
2. Docker Images: An image is an inert, immutable, file that’s essentially a snapshot of a container. These are your actual committed containers (ones that have the process running, data stored, ports exposed to be used). Docker images are essentially the stored instances that you can (actually move around).
3. Dockerfile: It is a YAML (almost) based file from which Docker creates an image. It can be thought of as an automated script that has all the steps you want to execute.

Use this curated list of resources to learn data science!

Many people are seeking to learn data science these days. It’s become a trendy topic associated with high salaries and some of the most interesting problems in the world. This demand has created many different resources in the data science space.