Cleaning Messy Data: Why It’s 80% of the Job let us Talk About it 🧹📊
When people think of data science, they imagine machine learning models, fancy dashboards, and mind-blowing insights.
But the real tea? Most of the time is spent cleaning messy, chaotic data before you even touch the fun stuff.
Why Data Cleaning Matters
- Garbage in = garbage out.
- Models can’t save bad data.
- Clean data = faster insights.
Common Data Cleaning Struggles
- Missing values that mysteriously disappear 👻
- Duplicates that never seem to end
- Columns with 20 different spellings of the same thing (looking at you, "Nairobi"/"nairobii"/"Nairobiii") see!!
My Go-To Tools
- Python + Pandas: the classic combo
- Excel: don’t sleep on it!
- SQL: when datasets get big and messy
Takeaway
Data cleaning isn’t glamorous, but it’s the backbone of every project.
Think of it like doing dishes before cooking you can’t ignore it if you want a great meal.
💬 What’s the messiest dataset you’ve ever had to clean?
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