Summary

For years, academics and research scientists have used data science notebooks as scratchpads for writing code, refining algorithms, and sharing and proving their work. Today, itb s a workflow that lends itself well to web developers experimenting with data sets in Node.js. In this code pattern, learn how to use a Jupyter Notebook to run Node.js code in Watson Studio or a local environment using the pixiedust_node library, a Jupyter Notebooks add-on that allows Node.js and JavaScript to run inside notebook cells.

Description

Notebooks are where data scientists process, analyze, and visualize data in an iterative, collaborative environment. They typically run environments for languages like Python, R, and Scala that are popular among data scientists. pixiedust_node is an add-on for Jupyter Notebooks that allows Node.js and JavaScript to run inside Notebook cells. Not only can web developers use the same workflow for collaborating in Node.js, they can also use the same tools to work with existing data scientists working in Python.

This Jupyter Notebook outlines how to run Node.js code in Watson Studio or a local environment using the pixiedust_node library. By following the getting started instructions, you will learn how to:

• Use variables, functions, and promises
• Work with remote data sources, such as Apache CouchDB (or its managed sibling Cloudant)
• Visualize data
• Share data between Python and Node.js

Flow

1. Install Node.js in the target environment (Watson Studio or a local machine).
2. Open a Node.js notebook in the target environment.
3. Run the Node.js notebook.

Instructions

Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.