Community docs

Analyzing Data

The platform provides several ways for educators and students to explore and analyze data on the platform as well as connect to some of your favorite tools.

Exploring Data

It is often helpful to begin data exploration by looking at a "high-level" view of the data. The easiest way to do that on the platform is to:

  1. Navigate to a particular dataset that contains tabular files (e.g., .csv, .tab, .xlsx etc.)

  2. Click the button labelled "Switch to column overview" on the bottom right for each file you'd like to explore.


The column overview gives high-level information about each column in the dataset, including how many rows are empty, the number of distinct values in the column, a distribution of values for numeric columns, and either samples or most common values for text-based data columns.


Users can also click on the information icon next to any column name for more information about that column, including a text-based description of the column from the data dictionary.

Querying data with SQL or SPARQL

The platform provides sandbox environments for your students to practice their data querying skills without leaving the platform or having to deal with complicated installations and setups.

If students are practicing their querying skills unrelated to an assignment or project, they can navigate directly to a dataset that they're interested in and click "Explore this dataset". This will generate a new, untitled project for them with the dataset already connected.

If students want to practice their querying skills in an assignment you've already created, they can:

  1. Navigate to the project

  2. Click "Launch Workspace" to open up an editable and collaborative workspace

  3. Next to "Project Directory" click "Add"

  4. Then select either "New SQL query" or "New SPARQL query" depending on your querying language of choice

  5. This will create a new, untitled query. Students can then type directly into the workspace and click "Run query" whenever they want the query to run. They will see the results of their query directly in their browser window. Information about all datasets connected to the project is available in the panel on the right-hand side of the workspace to remain accessible.

  6. If students wish to save a query so that their classmates can also run it or so that they can access the same query again at another time, click the "save" button. Adding a detailed title and description detailing the purpose and output of each query will make them more reusable.

For more information about querying data on the platform, refer to our SQL and SPARQL tutorials.

Visualizing Data

Just as users can query data directly on the platform, they can also visualize data without leaving the platform using our Chart Builder tool.

If you'd like to use a different tool for data visualization (e.g., Tableau, Excel, R, Python etc.) jump ahead to our documentation on Integrations .

Users can access the Chart Builder tool from a few locations:

From a dataset

  • Click "View" in the upper-right hand corner of a file. This will open a project workspace.

From a project workspace

  1. Select a file from a connected dataset, if one isn't already selected

  2. Click the arrow next to "Open in app" and select "Open with Chart Builder". This will open a new window with the data automatically uploaded into the chart builder tool


Chart Builder comes with two options for creating and modifying charts: a Visual Builder and a Vega-Lite Editor. The easiest way to use it is to create your initial chart on the Visual Builder tab and then switch over to the Vega-Lite Editor to make any changes outside the scope of the Visual Builder. See our article on using Vega-Lite or the Vega-Lite website for more information.


You can find more information about using our Chart Builder tool in our Data Visualization with Chart Builder article.