Documentation in Data Science & Curated List of Articles

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Hi All! Hope you all had a great weekend. Last few weeks, I posted on LinkedIn how important is documentation and its purpose. During the weekends, I felt like just emphasizing the importance of documenting your codes is not enough but learning ways to implement the best ways to document your codes.

Personally, I feel I can never be a full-fledged data scientist if I don't know how to document.

When I started out my data science career journey, I was tasked to write documentation for someone else code. Does this ring a bell to you!! If so, perhaps, you would know how tiring the whole process is. To avoid such difficulties, It is always a good habit of documenting your code for future purposes.

What are the pros and cons of "Documenting" your codes:

Pros:

1. Understandability: This is the basic need of documenting your code. Having clear documentation will benefit another user to understand your code without many thoughts.

2. Reproducible: Documenting your code will enable future developers to enhance your code and reuse your code for some similar tasks.

3. Keeping track of your progress: Tracking the project process is essential for any task. This will give you a clear picture of where you stand and how far is the project to completion.

Cons:

Time-consuming, but worth the time and effort put in.

Below are some of the curated list of articles on documentation in Data Science by the leaders:

• Importance of Documentation in Data Science by Prince Javier

• Why You Should Document Your Work As a Data Scientist by Admond Lee

• Writing Professional Data Science Documentation by Adam Gajtkowski

• Importance of Documentation in Data Science by Koo Ping Shung

• Winning Model Documentation Guidelines

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