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Lesson 7 of 13

Spreadsheets, Data Cleanup & Accuracy

Output: Cleaned Client Spreadsheet

Step 1 of 8

Learn

Before you start

By the end of this lesson you will be able to:

  • Perform basic spreadsheet cleanup.
  • Standardize information consistently.
  • Identify duplicates and missing data.
  • Use sorting, filtering, and basic formulas appropriately.
  • Quality-check a spreadsheet before delivery.
Key idea

Accuracy is part of the deliverable. A neatly formatted spreadsheet containing incorrect data is still poor work.

Learn

Core spreadsheet skills

  • Rows, columns, and headers
  • Sorting and filtering
  • Basic formatting
  • Find/replace
  • Duplicate identification
  • Date and text consistency
  • Basic formulas such as SUM, COUNT, and simple IF logic
  • Data validation where appropriate
Learn

Data-cleaning workflow

INSPECT DEFINE RULES CLEAN VALIDATE SPOT-CHECK SAVE REPORT
Learn

Standardization examples

ProblemInconsistent examplesStandard approach
Namesmaria cruz / Maria Cruz / MARIA CRUZUse one agreed capitalization convention
Phone0917-123-4567 / +639171234567Use the client's required format
Dates01/02/26 / 2026-02-01Use one date standard
Statusactive / Active / ACTIVEUse controlled values
Practice

Pick the standardized version

0 of 4 checked.

Practice

Spot the duplicates

Eight records below include two people entered twice, each with slightly different formatting. Click the later, duplicate entry for each — not the first occurrence.

Practice → Decide

Missing required field

While cleaning a contact list, you find a record missing a required email address. The client didn't say what to do about missing fields.

What's the right move?

Apply

Work sample: Cleaned Client Spreadsheet

Keep both the original and cleaned version of a few records, and document the rules you used so another person can understand your changes.

💾 Save your work now. Copy this into a Google Sheet — that's part of your starter portfolio.
Preview / export
✓ Lesson 7 Processed

Nice work.

You've practiced standardizing messy data, spotting duplicates, and handling missing information without guessing.