Introduction to scientific data analysis and visualization with Python

The course will introduce you to using Python and interactive Jupyter Notebooks to analyze and visualize scientific data (e.g. data measured during laboratory work, or calculated based on various physical models).

For this course we will make extensive use of Jupyter notebooks and several Python packages, including Numpy, Scipy, Matplotlib, and Pandas. These packages will be installed using conda in a self-contained conda environment. Please follow the installation instructions below.

 Setting up a Conda Environment

Miniconda installation

If you are working on your own laptop and you don't already have conda, you need a conda installation first. The recommendation is to use the minimal conda installer called miniconda. Download the Miniconda installer for your operating system and follow the installation instructions on the miniconda website. 

Creating a Conda environment with yml files

Once you have miniconda, you can create conda environments from the command line, or by using a yml file. For Linux and MacOs, you can do this via a terminal, while forWindows, you can use the Anaconda Powershell prompt.

Download the yml file we will use for this course and navigate to the folder with the prompt command:

$ cd path/to/your/yml/folder

Create the environment:

$ conda env create -f scidata.yml

Check that the installation worked correctly by activating the environment and starting a Jupyter notebook:

$ conda activate scidata-env

$ jupyter-lab

Removing an environment

If you would like to completely remove a conda environment, first deactivate it:

$ conda deactivate

Then, remove and clean:

$ conda remove -n scidata-env --all

$ conda clean --all