Nothing at the moment.
- 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 interntet conncetion) with a working Anaconda ꜛ installation
- please download in advance the course material from the course’s GitHub repository:
- on the GitHub repository page, click on the green “Code” button and choose “Download Zip” (example)
- 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 ꜛ. 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).
LifeSize tip: If you join the course online via LifeSize, I recommend to pop-out the screen I will share during the course (see screenshot), and arrange the pop-out window in that way, that you have the shared screen on one side of your desktop, and, e.g., your Jupyter notebook browser window or the Spyder editor on the other side (see screenshot).
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
Info: The chapters of this course are also available as Jupyter notebooks on , which can additionally be opened on
- 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).