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Data Science course materials

This repository contains the course materials of the Data Science course for the MSc in Mechanical Engineering at Aarhus University.

To download the contents of the repository, use the green button Code->Download ZIP in this page. Then unzip the file into any folder on your hard drive. Please check for updates regularly and, if needed, download the zip file again and overwrite the contents of the folder.

Setting up a Python environment

To run the Python scripts contained in the repository, first you need to create a conda environment.

If you already have Anaconda installed on your computer please follow these instructions:

  • Open the Anaconda Prompt (Windows)/Terminal (MacOS/Linux) and execute the following commands: 
    conda install -n base conda-libmamba-solver
conda config --set solver libmamba
  • Then, download the environment.yml from Brightspace and execute 
    conda env create -f environment.yml
    to create the datascience environment and install all the Python libraries required for this course.
  • Activate the new environment to start coding and running the Python scripts/notebooks: 
    conda activate datascience
    (this step must be done every time you open a new Prompt/Terminal.)

If you DO NOT have Anaconda already installed on your computer, we recommend using mamba as a package manager.

Windows

  • Download the latest miniforge installer from here (click on the link Miniforge-Windows-x86_64).
  • Run the installer and leave the default options during the installation procedure. Warning: you cannot use the default settings (in particular, the installation path) if your username contains spaces. In this case, open File Explorer and create a new directory without spaces (e.g. "mamba") under C:\. Then, run the installer and choose this path as a destination folder. You may also need to move the directory where you unzipped the contents of this repository in a location such that the path does not contain spaces.
  • From the Start menu, launch the Miniforge Prompt.
  • Navigate into the directory where you unzipped the contents of this repository.
  • Execute the command 
    mamba env create -f environment.yml
    to create the datascience environment and install all the Python libraries required for this course.
  • Activate the new environment to start coding and running the Python scripts/notebooks contained in this repository: 
    mamba activate datascience
    (this step must be done every time you open a new Miniforge Prompt in order to use the scripts of this repository.)

MacOS / Linux

  • Open a terminal (e.g. Terminal under MacOS) and execute the following commands to download the installer using curl or wget and start the installation, e.g.
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh

or

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-$(uname)-$(uname -m).sh
  • From the command prompt, enter the directory where you unzipped the contents of the repository.
  • Execute the command 
    mamba env create -f environment.yml
    to create the datascience environment and install all the Python libraries required for this course.
  • Activate the new environment to start coding and running the Python scripts/notebooks contained in this repository: 
    mamba activate datascience
    (this step must be done every time you open a new terminal in order to use the scripts of this repository.)

Using Jupyter notebooks

To edit/run jupyter notebooks (*.ipynb), start JupyterLab by executing the following command from the command prompt

jupyter lab

and use the web interface to open notebooks. Press CTRL+C while on the prompt to terminate JupyterLab.

Contacts

Prof. Alessandro Lucantonio ([email protected])