Python Data Science Mistakes to Avoid

Learn about the most common mistakes that emerging data scientists make while using Python, as well as how to avoid these missteps in your own work.

Introduction

• Avoiding common Python mistakes
• Getting the most from this course

1. Avoid Mistakes in Coding Practices

• Not writing comments
• Not organizing your directory
• Not testing
• Not sharing data referenced in code
• Hard coding inaccessible paths
• Name clashing with Python standard library
• Not importing relevant libraries and modules
• Naming vaguely

2. Avoid Mistakes in Structuring Code

• Modifying a list while iterating over it
• Using for loops instead of vectorized functions
• Using class variables vs. instance variables
• Calling functions before defining
• Creating circular dependencies

3. Avoid Mistakes in Handling Data

• Not choosing the right data structure
• Skimming data
• Not using the right visualization type
• Not addressing outliers
• Not updating your dataset
• Not cleaning data

4. Avoid Mistakes in Machine Learning

• Using features that will be unavailable later
• Using redundant features

Conclusion

• Get started with Python