D3.js

Any programmer worth their salt has a few libraries that serve as their de facto standards. Being a quality coder is as much about writing efficient as it is about writing clean code, and these libraries help coders write in shorthand, to focus on accomplishing their goals rather than writing out complex scripts for components that already have viable solutions. Data visualization is one of the most important, complex, and expanding disciplines in the field of programming, and that complexity means that solid libraries are doubly important, particularly since the field often draws multi-discipline practitioners who may not treat coding as their highest proficiency. If you're working in data science, and you use JavaScript to accomplish your visualizations, D3.js needs to be a part of the libraries you rely on. It's a rich and meaningful library with a lot of options to expand your capabilities and make your job significantly easier. D3 is short for Data Driven Documents, and it's a library designed explicitly for use with data visualization projects. Its seamless integration with Excel means that you can very simply translate raw collected data into visualizations. A number of different visualizations are included right with the library, but one of the real strengths of the platform is the level of customization you have over your visualizations. CSS is integrated with into the DOM, so you can personalize visualizations through the use of style sheets. That allows you to create visualizations that don't just help you parse through the data available but also make it easier to present them to colleagues, students, or investors in a meaningful and appealing manner. But where D3 really shines is in its use as a framework rather than as a simple library. There are a number of visualization styles that you can use right out of the box, but experienced data scientists are likely going to want to create their own visualizations. While it requires a fairly robust understanding of programming, you can create complex visual templates from scratch and ensure that they work with the importation of spreadsheets. The D3 library even includes a drag and drop interface that lets you quickly translate data sets into visualizations without the need to run complex scripts. This makes it a great choice for coders who want to create custom profiles for themselves or less programming-inclined peers and know that the results will churn out accurate readings regardless of their context. While D3 is primarily written in JavaScript, it's based on a number of languages, including DOM, HTML, CSS, and SVG. It also supports Canvas. While this expands the functionality of the framework, it also means that you'll want to have a broad understanding of the languages at play to make the most off it. D3 can require an investment of time, but the functionality make it one of the best data visualization tools you'll find.