|
1 | 1 | # Data Science for Beginners - A Curriculum
|
2 | 2 |
|
| 3 | +[](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198) |
| 4 | + |
3 | 5 | [](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
|
4 | 6 | [](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
|
5 | 7 | [](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
|
@@ -95,14 +97,34 @@ In addition, a low-stakes quiz before a class sets the intention of the student
|
95 | 97 | | 18 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Training models using Low Code tools. |[lesson](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) |
|
96 | 98 | | 19 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Deploying models with Azure Machine Learning Studio. | [lesson](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) |
|
97 | 99 | | 20 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Data science driven projects in the real world. | [lesson](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
|
| 100 | + |
| 101 | +## GitHub Codespaces |
| 102 | + |
| 103 | +Follow these steps to open this sample in a Codespace: |
| 104 | +1. Click the Code drop-down menu and select the Open with Codespaces option. |
| 105 | +2. Select + New codespace at the bottom on the pane. |
| 106 | +For more info, check out the [GitHub documentation](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace). |
| 107 | + |
| 108 | +## VSCode Remote - Containers |
| 109 | +Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension: |
| 110 | + |
| 111 | +1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in [the getting started documentation](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started). |
| 112 | + |
| 113 | +To use this repository, you can either open the repository in an isolated Docker volume: |
| 114 | + |
| 115 | +**Note**: Under the hood, this will use the Remote-Containers: **Clone Repository in Container Volume...** command to clone the source code in a Docker volume instead of the local filesystem. [Volumes](https://docs.docker.com/storage/volumes/) are the preferred mechanism for persisting container data. |
| 116 | + |
| 117 | +Or open a locally cloned or downloaded version of the repository: |
| 118 | + |
| 119 | +- Clone this repository to your local filesystem. |
| 120 | +- Press F1 and select the **Remote-Containers: Open Folder in Container...** command. |
| 121 | +- Select the cloned copy of this folder, wait for the container to start, and try things out. |
| 122 | + |
98 | 123 | ## Offline access
|
99 | 124 |
|
100 | 125 | You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
|
101 | 126 |
|
102 | 127 | > Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel.
|
103 |
| -## PDF |
104 |
| - |
105 |
| -A PDF of all of the lessons can be found [here](https://microsoft.github.io/Data-Science-For-Beginners/pdf/readme.pdf) |
106 | 128 |
|
107 | 129 | ## Help Wanted!
|
108 | 130 |
|
|
0 commit comments