These tools emphasize easily importing data, viewing large tables and variables, and viewing visualizations in an easily accessible way. In this article, we will cover 6 of the more common and best of these tools specifically with functionality that benefits projects in data science. There are a lot of great tools available. IDEs and notebook platforms are both great tools for data scientists to quickly write code and analysis for data projects. This approach to coding makes the code more readable and the analysis more like a writeup that you and others can follow the logic of. Notebooks allow you to write code, view outputs, and add commentary in the form of markdown. Notebook platforms provide similar benefits to IDEs but are packaged in a different format. Many provide helpful features like code completion, syntax highlighting, debugging tools, variable explorers, visualization tools, and many other features. Integrated Development Environments (IDEs) are coding tools that make writing, debugging, and testing your code easier.
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