Creating a Workspace
How to get started with workspace
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: create a blank Python or R workspace, create a workspace from one of DataCamp's curated templates, or create a workspace from a (public) GitHub repository.
Create a workspace

Creating a blank Python or R workspace

  1. 1.
    From the workspace dashboard, select New Python Workspace or New R Workspace. A blank notebook displays. You decide what you want to do.
  2. 2.
    You can do any of the following:
    • Upload a dataset.
    • Connect to an external data source.
    • Upload an existing notebook.
This creates a blank notebook in the DataCamp's editor for the Python workspace or a blank notepad in RStudio for the R workspace. To change the editor, see Switching the editor.

Creating a workspace from a dataset or a template

DataCamp provides a rich and growing collection of datasets and templates to kickstart your own analysis. They can be accessed under the Datasets and Templates tab in the sidebar.
Datasets consist of intriguing datasets to analyze; the workspace you create will contain the data files and a data dictionary.
Example of some of the available datasets
To create a workspace from a DataCamp dataset
Start from a template
  1. 1.
    From the Workspace dashboard, select Datasets to choose from a collection of datasets.
  2. 2.
    Select a dataset. A dialog box displays. It provides an Overview of the selected dataset including a data dictionary and a link to the dataset source, possible choice of language (Python or R), and a list of Publications to explore publications with this dataset created by other Workspace users.
  3. 3.
    Select Use Dataset to add or Close to exit the dialog box.
This creates a workspace that contains all the necessary data and packages.
Templates contain pre-written code on a certain topic, example data to experiment with, and guided information to help get you started. All required packages are included in the Templates.
There are two types of templates you can choose from:
  • 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.
Eaxmple of some of the available templates
Watch a video on how Ramnath quickly visualizes DataCamp's curriculum through an interactive tree map using a template.
To create a workspace from a DataCamp template
Start from a template
  1. 1.
    From the Workspace dashboard, select Templates or Start from a template to choose from a collection of templates.
  2. 2.
    Select one of the following:
    • All: List of all available recipes and playbooks
    • Recipes: List of available recipes.
    • Playbooks: List of available playbooks.
  3. 3.
    Select a recipe or playbook. A dialog box displays. It provides an Overview of the selected template, possible choice of language (Python or R), and a list of Publications to explore publications with this template created by other Workspace users.
  4. 4.
    Select Use Template to add or Close to exit the template dialog box.
This creates a workspace with the selected template's content.

Creating a workspace from a (public) GitHub repository

Creating a workspace from a public GitHub repository
  1. 1.
    From the dashboard, select From GitHub Repository.
  2. 2.
    Enter the URL or repository name and select the technology (Python or R) that you want to associate with the files.
This creates a workspace containing all the files included in the specified repository.
Last modified 18d ago
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