Today I was composing a template for writing code tutorials using Jupyter notebooks. The idea behind this tutorial is that I want to use it as a boilerplate for an upcoming course in deep learning which I am authoring (more on that later). What meant to be a simple personal guide to maintain consistency and clarity while preparing my educational material turned out to be a primer for data scientists who want to properly communicate their research or code using collaborative technologies such as Jupyter, Distill, and Google Colab.
I am calling it the “Writing Primer for Data Scientists”. It could have well been called “Writing Primer for Programmers”, but I want to keep it focused to a specific group, which includes deep learning and natural language processing researchers and engineers who want to communicate their work to a diverse audience in order to have more reach and impact.
Although it is just a recent idea that came up while playing around with boilerplate code on my Jupyter notebook, I believe it has potential to reach a wider audience who may need a jumpstart for properly and effectively communicating their research, findings, and ideas. In addition, I am sharing the primer on collaborative websites like Google Colab and GitHub, so other expert writers can reuse or even chip in and enhance the template.
One of our top missions is to transform the way we communicate about AI technologies, so I see this as a nice little resource to help accomplish our vision while at the same time assisting other young and hungry coders and writers from all backgrounds. The writing techniques I am sharing in this primer are the same ones I use to communicate about trending and emerging AI technologies at dair.ai. It has brought tremendous success to the publication over the past few months, and I want to share it with you.