Python: Neuro-Practical

Description: 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.

Next course time: -
Venue: -

duration: each pipeline tutorial approx. 1-4 hours (we will choose max. 1 or 2 tutorials from the pool)
course held in: 2021, 2020, 2019
published: July 08, 2021
latest update: October 10, 2022 (11:17 pm)

Jump to Syllabus

Current announcements

Nothing at the moment.

Course requirements

  • basic Python programming skills, e.g., presented in the Python: Basics for Data Scientists course
  • a laptop or desktop computer (no specific requirements except an internet connection) with a working Anaconda installation
  • please download in advance the course material from the course’s GitHub repository: Generic badge
    1. on the GitHub repository page, click on the green “Code” button and choose “Download Zip” (example)
    2. extract the Zip package and move the unpacked folder to your desired location on your hard drive (e.g., create a course folder in your documents folder)
  • during the course, please visit this website to stay up to date (see Current announcements section).

Important note: Before the course starts, please make sure, that Anaconda is working on your device. We can not provide installation or technical assistance during the course.

Trouble shootings: If you have problems with your computer and/or Anaconda, you can use an online Python compiler, e.g., Google Colab . Please, ensure before the beginning of the course, that you can access the online compiler of your choice (e.g., create a Google account) and that you know how to operate it (again, during the course we can not provide installation or technical assistance).


Tutorial 1: Statistical data analysis with Pandas and Pingouin (extended)

Tutorial 2: Basic time series analysis

Tutorial 3: Python Data I/O

Tutorial 4: Analyzing patch clamp recordings

Tutorial 5: Using Fourier transform for time series decomposition

Tutorial 6: Improving matplotlib plots

Further Readings

Info: The chapters of this course are also available as Jupyter notebooks on Generic badge, which can additionally be opened on Open In Colab

Past courses

  • 2021, March: DZNE Workshop series (2 days)
  • 2020-2021: Lab internal course series (weekly, closed)
  • 2020, October: DZNE Workshop series (2 days)
  • 2020, May: DZNE Workshop series (2 days)

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).


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