Python: Basics for Data Scientists

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

Chapters

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

Course requirements

Please visit the course website for further details.

Course announcements

Please visit the course website for further details.

Follow-up

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

License

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.