Lecture: Data Science App Development with Jupyter
How data science benefits from easy-to-use Jupyter applications
Jupyter Notebooks as web app for data science? This talk will demonstrate that Jupyter is more than simply a pretty version of your code without any real debugging functionality. We will give an insight on how a biotech - data science company can leverage Jupyter as a platform to develop innovative, easy-to-use data science web apps; collaboratively, and quickly.
‘Everyone’ knows Jupyter. It is convenient to quickly show something to your coworkers, experiment with the structure, but then code it properly in a serious application if you ever intend on using the results. However, there is another application: using Jupyter as platform for interactive web apps for data science tasks. At Exputec, an innovative biotech – data science company, we develop a web-based bioprocess data management and data analytics software called inCyght. InCyght uses interactive Jupyter notebooks for fast development of customized applications. We coded our own statistics package in PyCharm (the ‘official’ IDE), as well as the GUI functionalities used in the Jupyter notebooks. To get the web app feeling, most elements of the notebook are hidden and only the outputs (markups, ipywidgets, plotly plots) are visualized. From there, all users of the software can benefit from underlying statistics and modeling workflows to quickly develop innovative, custom data science apps in the Jupyter interface.
In this talk I’ll show a live presentation of our data science web apps, discuss the opportunities and difficulties when developing these applications, and finally explain the underlying technology.