A workspace is a collection of source, data and configuration files that represents all of the work you are doing for a certain data analysis. It corresponds to what would typically be a GitHub repository or an RStudio project.
Creating a new workspace
There are three ways to create a workspace: start a blank workspace, create a workspace from one of DataCamp's curated templates or from a (public) GitHub repository. For now, you can create R or Python workspaces, with support for SQL coming soon.
Creating a blank workspace
From the workspace dashboard, select New Python Workspace or New R Workspace to get started. This workspace functions as a bare-bones notebook. You decide what you want to do.
Example of a blank Workspace in JupyterLab
The next logical step would be to upload a dataset, connect to an external data source, or upload a notebook you have been working on in the past. To learn more about this, refer to Working in Workspace.
Creating a workspace from a template
In case starting from blank does not suit your needs, we offer a growing collection of workspace templates to kickstart your own analysis. Templates can be accessed under the Templates heading in the sidebar.
Templates contain example data to experiment with and guided information to help get you started. All required packages are included in the template.
Example of the collection of templates available
There are three types of templates you can choose from:
Dataset: Choose an intriguing dataset to analyze; the workspace you create will contain all necessary data and packages.
Recipe: Experiment with small code chunks that accomplish common data science tasks.
Playbook: Swap out the data source in these end-to-end analyses for business problems to build good-looking reports in the blink of an eye.
Watch a video on how Ramnath quickly visualizes DataCamp's curriculum through an interactive tree map using a template.
Creating a workspace from a (public) GitHub repository
From the dashboard, select From GitHub Repository and enter in the URL or repository name (as shown below) and technology (JupyterLab or RStudio) that you want associated with the files.
This will create a Workspace containing all the files included in the specified repository.