Data science notebooks and Project Jupyter
In programming, when you want to illustrate a concept or explore new coding techniques, it’s normal to create a small application such as a console program and write the code you need there. This can be helpful for validating and communicating ideas, making small examples, reproducing errors, or building small demos.
Like programmers, data scientists also sometimes need to perform small experiments. While it’s entirely possible to perform data science experiments by creating a new Python or .NET program, a far more common approach for data scientists and data analysts is to create a notebook.
A notebook is a combination of documentation in the form of Markdown cells mixed together with code cells. This combination of code and documentation allows you to provide rich formatted documentation via Markdown while also providing live executable code in code cells.
For years, when people have talked about notebooks in data...
