Suppose you receive this dataset:
Employee
john smith
SARAH JONES
mike brown
DAVID WILSON
Task 1 — Remove extra spaces
=TRIM(A2)
Task 2 — Convert to proper case
=PROPER(TRIM(A2))
Task 3 — Count characters
=LEN(A2)
Task 4 — Convert to uppercase
=UPPER(A2)
Task 5 — Extract the first 3 characters
=LEFT(A2,3)
🏆 Key Lesson
Text functions aren't just about manipulating words.
For a Data Analyst, they're data-cleaning tools.
When you receive messy data, think:
Remove unwanted spaces → Standardize → Extract → Replace → Combine → Validate
For example:
=PROPER(TRIM(A2))
can turn:
" jOhN sMiTh "
into:
John Smith
That may look like a small task, but cleaning and standardizing data correctly is an important part of professional analytics.
Double Tap ❤️ For Part-7
Employee
john smith
SARAH JONES
mike brown
DAVID WILSON
Task 1 — Remove extra spaces
=TRIM(A2)
Task 2 — Convert to proper case
=PROPER(TRIM(A2))
Task 3 — Count characters
=LEN(A2)
Task 4 — Convert to uppercase
=UPPER(A2)
Task 5 — Extract the first 3 characters
=LEFT(A2,3)
🏆 Key Lesson
Text functions aren't just about manipulating words.
For a Data Analyst, they're data-cleaning tools.
When you receive messy data, think:
Remove unwanted spaces → Standardize → Extract → Replace → Combine → Validate
For example:
=PROPER(TRIM(A2))
can turn:
" jOhN sMiTh "
into:
John Smith
That may look like a small task, but cleaning and standardizing data correctly is an important part of professional analytics.
Double Tap ❤️ For Part-7