This is the course material for the introductory course into Python Basics for Data Scientists.

• Chapter 1: Scientific programming languages (⟶ course website)
• Chapter 2: Getting started with Anaconda and Spyder (⟶ course website)
• Chapter 3: Jupyter Notebooks (⟶ course website)
• 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 (⟶ course website)

After the first part of this course, i.e., after Chapter 9, you can voluntarily solve the following intermediate exercises:

• Voluntary homework

Please visit the course website for further details.

Please visit the course website for further details.

Don’t miss the Python Neuro-Practical course, where you can apply your newly learned programming skills ‍🧑‍💻.

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: Basics for Data Scientists" by Fabrizio Musacchio is licensed under CC BY-NC-SA 4.0.