A slide deck from Netflix, mentions using Nteract as their programming notebook, and prompted a mini exploration.
This blog post by Safia Abdalla, (a maintainer/ developer of Nteract) introduces Nteract as an open source, desktop-based, interactive computing application that was designed to overcome a bunch of limitations in Jupyter Notebook’s design philosophy. One key difference (among many others) is the ability to execute code in a variety of languages within a single notebook, and it also appears that that the electron based desktop app should make it easier for beginners to start coding.
Matt Dancho’s course DSB-101-R is an awesome course to step into ROI driven business analytics fueled by Data Science. In this course, among many other things - he teaches methods to understand and use cheatsheets to gain rapid level-ups, especially to find information connecting various packages and functions and workflows. I have been hooked to this approach and needed a way to quickly refer to the different cheatsheets as needed.
Title: Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice
Author: Yehia Elkhatib
This are my notes on the above paper, which mainly deals with detailing the methods explored and implemented to impart a high quality of education in data science. The paper also provides an interesting breakup of the different roles in data science workflows.
The importance of being able to work in a team is highlighted.