Python Data Science Mistakes to Avoid

48mIntermediate2020-10-01

Authors

Lavanya Vijayan

Coding instructor who is passionate about STEM education and diversity

Course details

Whether you're a master in Python or you're still learning, chances are you're making simple mistakes that cost you time and productivity. In this course, learn about the most common mistakes that data scientists make while using Python, as well as how to avoid these missteps in your own work. Discover issues to avoid in the area of coding practices, such as giving objects vague names. Learn about mistakes that developers make when structuring code, including creating circular dependencies. Plus, explore common missteps developers make when handling data and working on machine learning projects. By the end of this course, you'll be equipped with a list of tools, strategies, and best practices to improve your effectiveness when working with data in Python.

Skills covered

PythonPersonaProgramming LanguagesOpen SourceSoftware Development

Concepts

0. Introduction

01 - Avoiding common Python mistakes
02 - Getting the most from this course

1. Avoid Mistakes in Coding Practices

03 - Not writing comments
04 - Not organizing your directory
05 - Not testing
06 - Not sharing data referenced in code
07 - Hard coding inaccessible paths
08 - Name clashing with Python standard library
09 - Not importing relevant libraries and modules
10 - Naming vaguely

2. Avoid Mistakes in Structuring Code

11 - Modifying a list while iterating over it
12 - Using for loops instead of vectorized functions
13 - Using class variables vs. instance variables
14 - Calling functions before defining
15 - Creating circular dependencies

3. Avoid Mistakes in Handling Data

16 - Not choosing the right data structure
17 - Skimming data
18 - Not using the right visualization type
19 - Not addressing outliers
20 - Not updating your dataset
21 - Not cleaning data

4. Avoid Mistakes in Machine Learning

22 - Using features that will be unavailable later
23 - Using redundant features

Conclusion

24 - Get started with Python