I’ve spend the past couple months working on various geospatial / prediction models. But this week I wanted to post an update on a side interest which is D3. I borrowed a book called D3.js in Action. So far it has been a lot of fundamental on how to utilize the DOM and D3 in combination and how to use the values in the DOM to influence the visualization. Why learn D3.js? Well it appears to be the next evolution in non-proprietary open source data visualization. While many data scientists probably just throw together a couple of plots in R or matplotlib in Python D3 serves a different purpose. Rather than generic plots or proprietary libraries I think D3 is meant to get root access to displaying data on the web. I have been extremely focused on geo-spatial the past couple of months and have looked at various mapping solutions like MapBox, CartoDB, Fusion Tables, etc.. just to name a few. What I like about D3.js is that while it may not be as polished for maps yet I think it is a library that will serve me long into my data career for more than just maps or bar charts. Data tool sets that will serve me 10 years from now are the tool sets that I invest my time in learning.