Jupyter Notebooks have become the workhorse IDE of choice for data science and analytics teams. Most data scientists use them for running analysis, but few take advantage of the full power of Jupyter Notebooks user interface layer. In this talk we share how to build applications using Jupyter Notebook as the front-end interface for everyday users. While the base coding language is Python, we take advantage of the Javascript layer of Jupyter Notebook’s interface to deliver fully interactive notebooks-as-an-application.
The talk will cover everything from setting constants and variables, to creating user-friendly input widgets to testing and deployment. Using Python 3 and Jupyter Notebooks, teams can democratize data analysis, empowering subject matter experts and non-technical users to perform complex data analysis.
Developer team: Kas Stohr, Yusuf Khan, Yicen Shi, Clint Green, Miguel Galeano, Sophie Richards
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