Markdown is one of the world’s most popular markup languages used in data science. Both R Markdown and Jupyter Notebooks use Markdown to provide an unified authoring framework for data science, combining code (Python, R, SQL,…), its results, and commentary in Markdown. The documents are fully reproducible and support dozens of output formats, like PDFs, Word files, slideshows, and more.
According to Wickham & Grolemund (2016), Markdown files are designed to be used in three ways:
For communicating to decision makers, who want to focus on the conclusions, not the code behind the analysis.
For collaborating with other data scientists, who are interested in both your conclusions, and how you reached them (i.e. the code).
As an environment in which to do data science, as a modern day lab notebook where you can capture not only what you did, but also what you were thinking.
Learn the most important basics of Markdown in this excellent interactive “60 Seconds Markdown Tutorial”.