Before jumping into charts, models, or predictions, there’s one step that decides everything — data cleaning.
Raw data is messy.
It has missing values, duplicates, wrong formats, and hidden errors.
If you skip cleaning:
Your analysis becomes misleading
Your insights become unreliable
Your model learns the wrong patterns
Data cleaning helps you:
✔ Understand what your data truly represents
✔ Remove noise and inconsistencies
✔ Build trust in your analysis and decisions
In simple words:
Good data → Good insights
Bad data → Wrong conclusions
That’s why experienced data analysts spend most of their time cleaning before analyzing.
Clean first. Analyze later. Always.
Linkedin
Github
Top comments (0)