Data Science Getting Started Platform To get started quickly with data science, I started looking at python and its powerful set of libraries (like pandas, NumPy, Scikit-Learn, etc) that makes data analysis easier. I wanted to have a platform that is accessible over the internet so I can get to it from any laptop/PC that has internet access. I decided to get a minimal Virtual Private Server (VPS) that supports containers so I can set up a Docker container with all the languages and frameworks/libraries/tools and mount a path on the VPS that contains all the projects I am working on, which will be checked in to git.
Installing Docker on Ubuntu This post is essentially my notes on getting started quickily with Docker. I set this up in my lab machines running Ubuntu 16.04.1 LTS, the steps are based on the excellent instructions written on the Docker getting started guide Add the Docker project repository to APT sources sudo apt-get install apt-transport-https ca-certificates sudo apt-key adv \ --keyserver hkp://ha.pool.sks-keyservers.net:80 \ --recv-keys 58118E89F3A912897C070ADBF76221572C52609D echo "deb https://apt.
When developing a package(any piece of reusable code, like a class library to be loaded or a web service that’s accessible through HTTP) that has a published API it is necessary to have a clear separation between the API version and the codebase version of the package. The API is what is exposed from the package for the users to consume, this should be documented clearly and the module should have thorough tests included that tests the entire published API to assert it conforms to the documented.