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.

This guide assumes that you have a Coder deployment available to you and that

you have the credentials needed to access the deployment.

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**.


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.


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).


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**.


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.


Once your workspace is ready for use, you'll see a chip that says **Running**

next to the name of your workspace.

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.


Additionally, we have a guide for those interested in leveraging Coder for data

science, specifically using Python with Jupyter notebooks.

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"path": "getting-started/docker.md"

},

"path": "getting-started/data-scientists.md"

"path": "getting-started/developers.md"