Python "Neuro-Practical" Course Material

This is the course material for the advanced Python course Python: Neuro-Practical.

The course is a collection of short tutorials tailored to practical Data Science problems in Neuroscience. The aim of these short tutorials is to demonstrate, how to think about problem solution in Python and how to find strategies and individual solutions for own specific problems beyond the scope of the tutorials.

I will add new tutorials to this collection from time to time.

Chapters

• Tutorial 1: Statistical data analysis with Pandas and Pingouin.ipynb)
• Tutorial 2: Basic time series analysis
• Tutorial 3: Data I/O - An extended guide (a cheat sheet on my website)
• Tutorial 4: Analyzing patch clamp recordings
• Tutorial 5: Using Fourier transform for time series decomposition
• Tutorial 6: Improving matplotlib plots
• Further Readings (⟶ course website)

New tutorials will be added from time to time:

• Linear mixed models in practice: when ANCOVA is enough and when you really need random effects

Course requirements

Please visit the course website for further details.

Course announcements

Please visit the course website for further details.

Installation

For reproducibility, install the following conda environment:

conda create -n pythonneuropractical python=3.12 -y
conda activate pythonneuropractical
conda install -y pandas numpy scipy matplotlib seaborn pingouin statsmodels scikit-learn ipython ipykernel

License

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0). How to give attribution (example):

"Python Neuro-Practical Course" by Fabrizio Musacchio is licensed under CC BY-NC-SA 4.0.