diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
index f5121b33c89566620cf9d737416938c328cc8d63..d3250433b2fefea217dc8ad4e4fc04f301d86525 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
@@ -1,134 +1,24 @@
# Python for Data Analytics
-Python is a high-level interpreted language widely used in research and
-science. Using HPC allows you to work with python quicker and more
-effective. Taurus allows working with a lot of available packages and
-libraries which give more useful functionalities and allow use all
-features of Python and to avoid minuses.
+Python is a high-level interpreted language widely used in research and science. Using ZIH system
+allows you to work with python quicker and more effective. Here the general introduction to working
+with python on ZIH system is given. For specific machine learning frameworks see respective
+documentation in [machine learning](machine_learning.md) section.
-**Prerequisites:** To work with PyTorch you obviously need [access](../access/ssh_login.md) for the
-Taurus system and basic knowledge about Python, Numpy and SLURM system.
+## Python Virtual Environments
-**Aim** of this page is to introduce users on how to start working with Python on the
-[HPC-DA](../jobs_and_resources/power9.md) system - part of the TU Dresden HPC system.
-
-There are three main options on how to work with Keras and Tensorflow on the HPC-DA: 1. Modules; 2.
-[JupyterNotebook](../access/jupyterhub.md); 3.[Containers](containers.md). The main way is using
-the [Modules system](modules.md) and Python virtual environment.
-
-Note: You could work with simple examples in your home directory but according to
-[HPCStorageConcept2019](../data_lifecycle/hpc_storage_concept2019.md) please use **workspaces**
-for your study and work projects.
-
-## Virtual environment
-
-There are two methods of how to work with virtual environments on
-Taurus:
-
-1. **Vitualenv** is a standard Python tool to create isolated Python environments.
- It is the preferred interface for
- managing installations and virtual environments on Taurus and part of the Python modules.
-
-2. **Conda** is an alternative method for managing installations and
-virtual environments on Taurus. Conda is an open-source package
-management system and environment management system from Anaconda. The
-conda manager is included in all versions of Anaconda and Miniconda.
-
-**Note:** Keep in mind that you **cannot** use virtualenv for working
-with the virtual environments previously created with conda tool and
-vice versa! Prefer virtualenv whenever possible.
-
-This example shows how to start working
-with **Virtualenv** and Python virtual environment (using the module system)
-
-```Bash
-srun -p ml -N 1 -n 1 -c 7 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash #Job submission in ml nodes with 1 gpu on 1 node.
-
-mkdir python-environments # Optional: Create folder. Please use Workspaces!
-
-module load modenv/ml # Changing the environment. Example output: The following have been reloaded with a version change: 1 modenv/scs5 => modenv/ml
-ml av Python #Check the available modules with Python
-module load Python #Load default Python. Example output: Module Python/3.7 4-GCCcore-8.3.0 with 7 dependencies loaded
-which python #Check which python are you using
-virtualenv --system-site-packages python-environments/envtest #Create virtual environment
-source python-environments/envtest/bin/activate #Activate virtual environment. Example output: (envtest) bash-4.2$
-python #Start python
-
-from time import gmtime, strftime
-print(strftime("%Y-%m-%d %H:%M:%S", gmtime())) #Example output: 2019-11-18 13:54:16
-deactivate #Leave the virtual environment
-```
-
-The [virtualenv](https://virtualenv.pypa.io/en/latest/) Python module (Python 3) provides support
-for creating virtual environments with their own sitedirectories, optionally isolated from system
-site directories. Each virtual environment has its own Python binary (which matches the version of
-the binary that was used to create this environment) and can have its own independent set of
-installed Python packages in its site directories. This allows you to manage separate package
-installations for different projects. It essentially allows us to create a virtual isolated Python
-installation and install packages into that virtual installation. When you switch projects, you can
-simply create a new virtual environment and not have to worry about breaking the packages installed
-in other environments.
-
-In your virtual environment, you can use packages from the (Complete List of
-Modules)(SoftwareModulesList) or if you didn't find what you need you can install required packages
-with the command: `pip install`. With the command `pip freeze`, you can see a list of all installed
-packages and their versions.
-
-This example shows how to start working with **Conda** and virtual
-environment (with using module system)
-
-```Bash
-srun -p ml -N 1 -n 1 -c 7 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash # Job submission in ml nodes with 1 gpu on 1 node.
-
-module load modenv/ml
-mkdir conda-virtual-environments #create a folder
-cd conda-virtual-environments #go to folder
-which python #check which python are you using
-module load PythonAnaconda/3.6 #load Anaconda module
-which python #check which python are you using now
-
-conda create -n conda-testenv python=3.6 #create virtual environment with the name conda-testenv and Python version 3.6
-conda activate conda-testenv #activate conda-testenv virtual environment
-
-conda deactivate #Leave the virtual environment
-```
-
-You can control where a conda environment
-lives by providing a path to a target directory when creating the
-environment. For example, the following command will create a new
-environment in a workspace located in `scratch`
-
-```Bash
-conda create --prefix /scratch/ws/<name_of_your_workspace>/conda-virtual-environment/<name_of_your_environment>
-```
-
-Please pay attention,
-using srun directly on the shell will lead to blocking and launch an
-interactive job. Apart from short test runs, it is **recommended to
-launch your jobs into the background by using Slurm**. For that, you can conveniently put
-the parameters directly into the job file which you can submit using
-`sbatch [options] <job file>.`
+Often it is useful to create an isolated development environment, which can be shared among
+a research group and/or teaching class. For this purpose python virtual environments can be used.
+For more details see [here](python_virtual_environments.md).
## Jupyter Notebooks
-Jupyter notebooks are a great way for interactive computing in your web
-browser. Jupyter allows working with data cleaning and transformation,
-numerical simulation, statistical modelling, data visualization and of
-course with machine learning.
-
-There are two general options on how to work Jupyter notebooks using
-HPC.
-
-On Taurus, there is [JupyterHub](../access/jupyterhub.md) where you can simply run your Jupyter
-notebook on HPC nodes. Also, you can run a remote jupyter server within a sbatch GPU job and with
-the modules and packages you need. The manual server setup you can find [here](deep_learning.md).
-
-With Jupyterhub you can work with general
-data analytics tools. This is the recommended way to start working with
-the Taurus. However, some special instruments could not be available on
-the Jupyterhub.
+Jupyter notebooks are a great way for interactive computing in a web
+browser. They allow working with data cleaning and transformation,
+numerical simulation, statistical modelling, data visualization and machine learning.
-**Keep in mind that the remote Jupyter server can offer more freedom with settings and approaches.**
+On ZIH system a [JupyterHub](../access/jupyterhub.md) is available, which can be used to run
+a Jupyter notebook on an HPC node, as well using a GPU when needed.
## Dask