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)
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 🧑💻.
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