# Data Science Notebook

Data Science Notebooks provide users an interface for interactive computing. It allows users to write and execute code, visualize the output, and then share the results with others. Notebooks are popular among data scientists and widely used by them for data analysis and exploration tasks.

## Jupyter Notebook

Jupyter Notebook is an popular Python based (but not limited to Python) web application that allows users to create and share documents. The documents can contain live code, text, equations, and visualizations. 

Jupyter notebook can be used for following use cases: 

* Data cleaning and transformation
* Statistical modeling
* Data visualization
* Numerical simulation 
* Machine learning

For more info, visit <https://jupyter.org/>

### JupyterLab

JupyterLab is a web-based interactive development environment (IDE) for Jupyter notebooks, code, and data. It provides a configurable user interface, which can be modified to support a variety of workflows in data science and scientific computing. JupyterLab is extensible and modular, once can write plugins that add new components and integrate with existing ones.

For details, refer [https://jupyterlab.readthedocs.io/en/stable/](https://jupyterlab.readthedocs.io/en/stable/#)

### JupyterHub

JupyterHub builds on the power of Jupyter notebooks and makes it available to multiple users. Since it is available as a managed environment, it gives the users access to computational environments and resources without worrying about local setup and maintenance. Users can have their own separate workspaces but the server runs on shared resources that can be managed centrally by system administrators.

Invariant data science notebooks can run both in the cloud or on premise. This makes it possible to serve a pre-configured environment that can be customized and scaled to suit your needs.

For more information about the Jupyter project, see <https://jupyter.org/hub>


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