Python: Basics for Data Scientists
Nothing at the moment.
- 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).
Chapter 1: Scientific programming languages
Chapter 2: Getting started with Anaconda and Spyder
Chapter 3: Jupyter Notebooks
Chapter 4: Variables
Chapter 5: Formatted printing
Chapter 6: Deep vs. shallow copy
Chapter 7: for-loops
Chapter 8: if-conditions
Chapter 9: Function definitions
Chapter 10: NumPy - Our data container
Chapter 11: Data visualization with Matplotlib
Chapter 12: Reading data with Pandas
Chapter 13: Statistical Analysis with Pingouin
Voluntary homework: After the first part of this course, i.e., after Chapter 9, feel free to solve this voluntary homework.
Info: Chapters 4 - 13 are available as Jupyter notebooks on , which can also be opened on
Don’t miss the Python Course: Neuro-Practical course, where you can apply your newly learned programming skills.
- 2022, September: DZNE Workshop series (2.5 days)
- 2022, January: DZNE Workshop series (2.5 days)
- 2021, September: DZNE Workshop series (2 days)
- 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).