Data cleaning = making raw data ready for analysis.

Let’s take an example dataset of daily sales:

👉 Step 1: Check for spelling mistakes

• “Prodct A” vs “Product A” → same product but typed wrong.

  • Excel fix: use Find & Replace or create a clean “Product List” and use Data Validation.

👉 Step 2: Handle missing values

• 05-08-25 has no sales value.

  • Option 1: Replace with 0 if it means “no sales.”

  • Option 2: Fill with average if it’s truly missing data.

👉 Step 3: Handle wrong/illogical values

• Sales = -50 (negative sales) → maybe a refund? Need to confirm.

• Sales = 100000 → looks like a typo (maybe it was 1000).

  • Excel fix: use Filters or Conditional Formatting to highlight outliers.

👉 Step 4: Consistent formatting

• Dates should all be in one format (dd-mm-yy).

• Product names consistent (no extra spaces).

✅ After cleaning, the table looks like this:

Now it’s ready for analysis.

👉 Question for you:
If you see a row with negative sales, what would you do first — delete it, or try to understand why it’s negative?