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When using Workspace, you can connect to your organization's data warehouse and share notebooks with your data team like you would on Google Docs. Workspace also offers templates to help you get into specific types of projects faster and collaboration features that make it easy to edit and comment on projects. It offers ready-to-use datasets which allow you to quickly start practicing and analyzing real-world data.

Workspace can be used to both learn data science and actually do data science work as a standalone notebook platform. DataCamp WorkspaceĭataCamp Workspace is a cloud-based notebook that allows anyone to analyze data, collaborate, and share insights with their team. 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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