Data Cleaning
📋 What it is
Real data is messy — cleaning means fixing typos, filling or flagging missing values, and removing duplicates.
🗣️ Coach says
Before analysis, data usually needs a scrub. Typos, blanks, and duplicate rows quietly wreck results. "Garbage in, garbage out" — a clean dataset is the unglamorous secret behind every trustworthy chart.
🧠 Memory hook
Garbage in → garbage out. CLEAN first: fix typos, handle blanks, drop duplicates. Then analyze.
😂 Giggle
Why did the outlier feel lonely?
Because it didn't fit in with the rest of the data!
😲 Whoa!
Data scientists say they spend up to 80% of their time just CLEANING data — the analysis is the quick part at the end.
✅ Quick check: Why is cleaning data important before analyzing it?
Say your answer out loud first — then reveal.
Messy data (typos, blanks, duplicates) produces wrong or misleading results.
"Garbage in, garbage out" — bad inputs corrupt every calculation.
🧪 Try it! (2 minutes)
Write a list of 8 items with a deliberate typo and a duplicate. Clean it and note what you fixed.