Formalizing Reusable data science: building apps in Jupyter notebooks in Watson Studio with Pixiedust
Formalizing Reusable data science: building apps in Jupyter notebooks in Watson Studio with Pixiedust


The Jupyter notebook has quickly become one of data scientists’ favorite tools. When used within Watson Studio, you get a complete platform for building an application - from data preparation and analytics to building and deploying machine learning models. Jupyter notebook is It’s a big step up from executing code at the command line, but the basic notebook environment doesn’t do much to automate repetitive tasks. This is where Pixiedust comes in. It puts some of the most common visualization tasks behind a convenient GUI so you don’t have to remember all those obscure arguments that go into the creation of a simple bar chart. Even better, Pixiedust is extensible, so if the function you want to automate isn’t available, you can write a “PixieApp” – a Python class that extends Pixiedust – to do the job. Come learn how to use Pixiedust and build PixieApps, and also get a sneak peek at a new PixieApp for doing basic data cleaning and exploration we’ve been working on.


Raj is a Developer Advocate for Watson and Cloud at IBM. He specializes in data science and all things geospatial. Raj pioneered Web mapping-as-a-service in the late 1990s with Syncline, a startup he co-founded, and worked with many startups on data issues. Prior to IBM, he led geospatial data interoperability projects for the Open Geospatial Consortium. He has a PhD in Information Systems in Planning from MIT.

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