6 Common Mistakes to Avoid in Data Science Code

And How to Overcome Them

Motivation

Data scientists often work in iterative and exploratory environments. Thus, there is often a focus on rapid results rather than creating maintainable or scalable code.

However, data scientists must avoid writing poor code for the following reasons:

1. Reduced code readability: Badly written code can be difficult to read and understand, making it harder for both the original author and other team members to maintain or modify the code in the future.
2. Increased chances of introducing bugs: Poorly structured or inefficient code is more prone to errors, potentially affecting the accuracy of analyses or models.
3. Integration challenges: Badly written code can hinder integration with production systems and handovers to other team members, including data engineers and machine learning engineers.

To write better code in data science projects, it’s crucial to recognize and address common bad practices, which may include:

1. Excessive use of Jupyter Notebooks
2. Vague variable names
3. Redundant code