Overview
-
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.
Syllabus
-
Introduction
- Avoiding common Python mistakes
- Getting the most from this course
- 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
- 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
- Not choosing the right data structure
- Skimming data
- Not using the right visualization type
- Not addressing outliers
- Not updating your dataset
- Not cleaning data
- Using features that will be unavailable later
- Using redundant features
- Get started with Python
