Python: Basics for Data Scientists
Current announcements
Nothing at the moment.
Course requirements
- Please install Miniforgeꜛ before the course starts, unless you already have a working Conda installation that you want to keep.
- Please install VSCodeꜛ before the course starts.
- Once you have installed Miniforge and VSCode, please also install the Python extension for VS Codeꜛ, the Python Environments extensionꜛ, and the Jupyter extensionꜛ. Also install the vscode-pdf extensionꜛ to be able to open PDF files directly in VS Code.
- Please install Gitꜛ on you computer, if it is not already installed.
- We will use VS Code as our main Python IDE during the course. If you prefer another IDE, please make sure that it supports Jupyter notebooks and a recent Python 3 version.
- During the course, please visit this website regularly to stay up to date (see Current announcements).
Important for users with an existing Anaconda installation
If you already have Anaconda installed, you have two options:
Recommended option: Uninstall Anaconda and install Miniforge instead. This is the cleanest setup for the course.
Alternative option: Keep your existing Anaconda installation and do not install Miniforge in parallel. In that case, please make sure that conda works correctly in your terminal and in VS Code, and use conda-forge consistently for this course.
If you keep Anaconda, we recommend configuring conda to use conda-forge consistently and to avoid mixing channels. In some institutional environments, access to the default Anaconda repositories may also be restricted. If needed, you can switch your configuration with:
conda config --remove channels defaults
conda config --add channels conda-forge
conda config --set channel_priority strict
After that, please verify that your environment works in the terminal, in Python scripts, and in Jupyter notebooks inside VS Code.
Important note: Before the course starts, please make sure that Miniforge and VS Code are installed and working on your computer. We cannot provide installation or technical assistance during the course.
Troubleshooting: If you run into problems with your computer or your local Python and/or Miniforge installation, you can use an online Python environment such as Google Colabꜛ. Please make sure before the course starts that you can access the online platform of your choice.
Syllabus
Chapter 1: Scientific programming languages
Chapter 2: Getting started with Python: Miniforge, VS Code and conda environments
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
Further Readings
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 
Follow-up
Don’t miss the Python Course: Neuro-Practical course, where you can apply your newly learned programming skills.
Past courses
- 2026, April: DZNE Workshop series (2.5 days)
- 2023, March: DZNE Workshop series (2.5 days)
- 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).
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