Data Science Playground

This repository is meant to provide quick ways to get started in reproducible data-science docker containers for:

• python
• r

Getting started

1. Fork the repository
2. Clone the repository locally
3. Open a new VS Code session in python or r depending on which language you would like to use.
4. Follow the README.md instructions in the respective folder.

Strengths and Weakness of the languages

Strengths for python:

• More users, so:
  • More extensions with easier setup
  • More tutorials and useful hints from GitHub Copilot
  • More standardized formatting rules
• More modular imports when building own packages
• Less overhead to build own packages
• Fewer dependencies to get it running for use in Jupyter
• Much smaller diskspace and memory footprint

Strengths for r:

• The piping syntax from tidyverse can make data analysis very fast
• The documentation is more standardized and easier to retrieve (?<function name>)

Next steps

• See if you can find a way to make all of your packages 'slim'?
  • YouTube videos by 'slim.ai' called 'SlimDevOps', and use 'DockerSlim' to try to reduce the docker container sizes