Data are becoming the new raw material of business
The Economist

Ranking Popular JavaScript Visualization Packages for Data Science

At The Data Incubator, we strive to provide the most up-to-date data science curriculum available. Using feedback from our corporate and government partners, we deliver training on the most sought after data science tools and techniques in industry. We wanted to include a more data-driven approach to developing the curriculum for our corporate data science training and our free Data Science Fellowship program for PhD and master’s graduates looking to get hired as professional Data Scientists. To achieve this goal, we started by looking at and ranking popular deep learning libraries for data science, then ranking popular distributed computing packages for data science. Next, we wanted to analyze the popularity of JavaScript visualization packages for data science. Here are the results:

The Rankings

Below is a ranking of the top 20 of 110 JavaScript data visualization packages that are useful for Data Science, based on Github and Stack Overflow activity, as well as npmjs (javascript package manager)downloads. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, chart.js is 3.29 standard deviations above average in Github activity, while plotly.js is close to average. See below for methods.
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Embedding D3 in an IPython Notebook

Christian Moscardi is the Director of Technology at The Data Incubator. This was originally posted on his blog.


coding-924920_960_720Jupyter is a fantastic tool that we use at The Data Incubator for instructional purposes. In particular, we like to keep our curriculum compartmentalized via Jupyter notebooks. It allows us to test our code samples across any language there’s a Jupyter kernel for* and keep things in one place, so our Fellows don’t have to rifle through a wide variety of file formats before getting to the information they need.

One area where we only recently integrated Jupyter was frontend web visualization. Our previous structure involved a notebook, possibly with code snippets, that contained links to various HTML files. We expected our Fellows to dig through the code to

  • Look at the HTML source for the basic layout.
  • Expose the Javascript powering the visualization.
  • View the styles making everything pretty.

Oh, and any data processing code was separate/output to a file. Obviously not ideal. We knew IPython had %%javascript magic, and started rifling around to see what we could improve.  Continue reading