> For the complete documentation index, see [llms.txt](https://helx.gitbook.io/helx-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://helx.gitbook.io/helx-documentation/helx/helx-workspaces/jupyter-datascience.md).

# Jupyter-DataScience

{% hint style="success" %}
Begin by starting the App as described in the section [Creating an Application](/helx-documentation/helx/starting-an-existing-app.md). Select the Jupyter Datascience application.
{% endhint %}

## Introduction

**Jupyter/datascience** includes popular packages for data analysis from the Python, Julia and R communities and also packages are included from its ancestor images **jupyter/sci-py notebook**, **jupyter/r-notebook** and **jupyter/minimal-notebook**.

Some of the packages it includes are,

[**dask**](https://dask.org/)**,** [**pandas**](https://pandas.pydata.org/)**,** [**numexpr**](https://github.com/pydata/numexpr)**,** [**matplotlib**](https://matplotlib.org/)**,** [**scipy**](https://www.scipy.org/)**,** [**seaborn**](https://seaborn.pydata.org/)**,** [**scikit-learn**](http://scikit-learn.org/stable/)**,** [**scikit-image**](http://scikit-image.org/)**,** [**sympy**](http://www.sympy.org/en/index.html)**,** [**cython**](http://cython.org/)**,** [**patsy**](https://patsy.readthedocs.io/en/latest/)**,** [**statsmodel**](http://www.statsmodels.org/stable/index.html)**,** [**cloudpickle**](https://github.com/cloudpipe/cloudpickle)**,** [**dill**](https://pypi.python.org/pypi/dill)**,** [**numba**](https://numba.pydata.org/)**,** [**bokeh**](https://bokeh.pydata.org/en/latest/)**,** [**sqlalchemy**](https://www.sqlalchemy.org/)**,** [**hdf5**](http://www.h5py.org/)**,** [**vincent**](http://vincent.readthedocs.io/en/latest/)**,** [**beautifulsoup**](https://www.crummy.com/software/BeautifulSoup/)**,** [**protobuf**](https://developers.google.com/protocol-buffers/docs/pythontutorial)**,** [**xlrd**](http://www.python-excel.org/)**,** [**bottleneck**](https://bottleneck.readthedocs.io/en/latest/)**, and** [**pytables**](https://www.pytables.org/) packages

[**ipywidgets**](https://ipywidgets.readthedocs.io/en/stable/) **and** [**ipympl**](https://github.com/matplotlib/jupyter-matplotlib) for interactive visualizations and plots in Python notebooks.

[**Facets**](https://github.com/PAIR-code/facets) for visualizing machine learning datasets.

The [**Julia**](https://julialang.org/) **compiler and base** environment.

[**IJulia**](https://github.com/JuliaLang/IJulia.jl) to support Julia code in Jupyter notebooks.

[**HDF5**](https://github.com/JuliaIO/HDF5.jl)**,** [**Gadfly**](http://gadflyjl.org/stable/)**, and** [**RDatasets**](https://github.com/johnmyleswhite/RDatasets.jl) packages.

## Working with jupyter-datascience notebook in HeLx

{% hint style="success" %}
Begin by starting the App as described in the section [Starting An Existing App](https://helx.gitbook.io/helx-documentation/appstore/appstore/app-deployment/starting-an-existing-app). Select the Jupyter-DataScience application.
{% endhint %}

**Step-1:**

Launch a jupyter-datascience notebook from HeLx by clicking on “Launch Application” button.

![](/files/-MEZ8E5FP3jLVNtuiD5a)

**Step-2:**

This brings us to the jupyter-lab panel where we can select the environment that we want to work on (Python, Julia, R).

![](/files/-MEZ8JT-CuGmP5wLTZ0t)

Step-3:

Start working on it. Below code shows loading iris dataset (features, labels) from sklearn package to train/test our machine learning model.

![](/files/-MEZ8PTNgzENK4oE77NZ)