Learn Python by Building Data Science Applications

Understanding Jupyter

Finally, there is Jupyter. We're familiar with this tool already, as it proved invaluable for teaching—and learning Python on simple examples, but it especially shines for data science; given its rich media and visualization capabilities, Jupyter is an excellent environment for data analysis. It allows quick iteration and experimentation, supports markdown documentation and rich media—images, plots, interactive widgets, video, and so on. Of course, Jupyter is 100% open source and free. 

Jupyter is also language agnostic. At the moment, there is a handful of languages to use with Jupyter, including Ruby, C, Rust, R, and many more. It also supports third-party plugins, for example, leaflet and Mapbox viewers for GeoJSON ...