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diff --git a/getting-started/data-scientists.md b/getting-started/data-scientists.md
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@@ -0,0 +1,119 @@
+---
+title: Data scientists
+description: Get started with Coder as a data scientist.
+---
+
+This article will walk you through the process of getting started with a Coder
+workspace capable of supporting data science projects. You'll learn how to:
+
+- Connect Coder to your Git provider (this example assumes that you're using
+  GitHub, but Coder supports GitLab and Bitbucket as well));
+- Create a workspace with Jupyter Notebook and other data science packages
+  present;
+- Add a sample project to your workspace, specifically
+  [one as a Jupyter Notebook using IMDB movie data](https://github.com/khorne3/data-science-imdb-sample);
+- Create a dev URL and preview changes to your project.
+
+## Prerequisites
+
+This guide assumes that you have a Coder deployment available to you and that
+you have the credentials needed to access the deployment.
+
+## Step 1: Log in and connect Coder to your Git provider
+
+In this step, you'll log into Coder, then link your Coder account with your Git
+provider. This will allow you to do things like pull repositories and push
+changes.
+
+1. Navigate to the Coder deployment using the URL provided to you by your site
+   manager, and log in.
+
+1. Click on your avatar in the top-right, and select **Account**.
+
+   ![Set account preferences](../assets/getting-started/account-preferences.png)
+
+1. Provide Coder with your SSH key to connect and authenticate to GitHub.
+
+   If your site manager has configured OAuth, go to **Linked Accounts** and
+   follow the on-screen instructions to link your GitHub account.
+
+   ![Link GitHub account](../assets/getting-started/linked-accounts.png)
+
+   If your site manager has _not_ configured OAuth or you are using a Git
+   provider that Coder does not support, go to **SSH keys**. Copy your public
+   SSH key and
+   [provide it to GitHub](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account).
+
+   ![Add SSH key](../assets/getting-started/ssh-keys.png)
+
+## Step 2: Import an image
+
+At this point, you'll import your image, which you can think of as a template
+for your workspace. This template contains the language version, tooling, and
+dependencies you need to work on the project. In this case, the image also
+contains a `configure` script that will clone the data science project from
+GitHub to your workspace.
+
+To import an image:
+
+1. In the top navigation bar, click **Images**. Then, click on **Import Image**.
+
+1. Leave the default registry (which is **dockerhub**) selected.
+
+1. Under **repository**, provide **kmhcdr/python**. Provide **latest** as the
+   **tag**. Optionally, you can provide a **description** of the image
+
+1. Specify the minimum amount of resources (cores, memory, and disk space) the
+   workspace should have when using this image. For this project, we recommend 4
+   cores, 8 GB memory, and 10 GB disk space as a starting point.
+
+1. Click **Import Image**.
+
+![Import data science image](../assets/getting-started/import-ds-image.png)
+
+## Step 3: Create your workspace
+
+You will now create the workspace where you'll work on your development project.
+
+1. Return to **Workspaces** using the top navigation bar.
+
+1. Click **New workspace** to launch the workspace-creation dialog.
+
+1. Provide a **Workspace Name**.
+
+1. In the **Image** section, select the **kmhcdr/python** image you just
+   imported.
+
+1. Under **Workspace providers**, leave the default option (which is
+   **built-in**) selected.
+
+1. Expand the **Advanced** section. If the **Run as a container-based virtual
+   machine** option is selected, _unselect_ the box. Leave the **CPU**,
+   **Memory**, **Disk**, and **GPU** allocations as-is.
+
+1. Scroll to the bottom, and click **Create workspace**. The dialog will close,
+   allowing you to see the main workspace page. You can track the workspace
+   build process using the **Build log** on the right-hand side.
+
+   Due to the number of packages present in the image, this might take few
+   minutes.
+
+![Create a workspace](../assets/getting-started/create-ds-workspace.png)
+
+Once your workspace is ready for use, you'll see a chip that says **Running**
+next to the name of your workspace.
+
+## Step 4: Open up the sample project
+
+At this point, you're ready to open up Jupyter to access your notebook.
+
+1. Under **Browser applications**, click **Jupyter** to open the IDE in a new
+   browser tab.
+
+1. Under **Files**, click to open the **data-science-imdb-sample** project.
+
+1. Click **Data Science Workflow.ipynb** to launch the notebook.
+
+   You're now ready to proceed with work on the project.
+
+![Jupyter in the browser](../assets/getting-started/jupyter.png)
diff --git a/getting-started/index.md b/getting-started/index.md
index 74406b159..b5ce65535 100644
--- a/getting-started/index.md
+++ b/getting-started/index.md
@@ -23,3 +23,6 @@ guides for both [Kubernetes deployments](developers.md) and
 deployment available to them, these end-to-end guides will walk them through
 logging in and getting set up with a sample project they can use to experience
 Coder.
+
+Additionally, we have a guide for those interested in leveraging Coder for data
+science, specifically using Python with Jupyter notebooks.
diff --git a/manifest.json b/manifest.json
index 5961bfb28..160aa80fb 100644
--- a/manifest.json
+++ b/manifest.json
@@ -29,14 +29,17 @@
     {
       "path": "getting-started/index.md",
       "children": [
+        {
+          "path": "getting-started/docker.md"
+        },
         {
           "path": "getting-started/admin.md"
         },
         {
-          "path": "getting-started/developers.md"
+          "path": "getting-started/data-scientists.md"
         },
         {
-          "path": "getting-started/docker.md"
+          "path": "getting-started/developers.md"
         }
       ]
     },