🚀 Data Analyst Roadmap — Part 3

📊 Excel — Level 2: Essential Formulas

Now that you understand Excel's basic structure, the next step is learning the formulas that every Data Analyst should know.

For every function, understand:

What does it do? → When should I use it? → What problem does it solve?

1️⃣ SUM()

SUM() adds numbers together.

Syntax

=SUM(number1, [number2], ...)

Example

Suppose:

Product Sales

Laptop 80,000

Mouse 2,000

Keyboard 5,000

To calculate total sales:

=SUM(B2:B4)

Result: 87,000

2️⃣ AVERAGE()

AVERAGE() calculates the arithmetic mean.

=AVERAGE(B2:B4)

For:

80,000

2,000

5,000

the result is: 29,000

Business example

What is the average order value?

If each row represents an order:

=AVERAGE(SalesColumn)

This gives you the average sales amount per order.

3️⃣ MIN()

Returns the smallest numeric value.

=MIN(B2:B100)

Example:

50,000

25,000

80,000

10,000

Result:

10,000

Common analytical uses

• Lowest sales

• Lowest salary

• Minimum transaction value

• Earliest numeric measurement

4️⃣ MAX()

Returns the largest numeric value.

=MAX(B2:B100)

Example:

50,000

25,000

80,000

10,000

Result:

80,000

Common use

Find the highest sales transaction.

=MAX(SalesRange)

5️⃣ COUNT()

COUNT() counts cells containing numbers.

Example:

Sales

50,000

60,000

70,000

—

80,000

=COUNT(A2:A6)

Result:

4

The blank cell isn't counted.

COUNT() counts numeric values, not all non-empty cells.

6️⃣ COUNTA()

COUNTA() counts non-empty cells.

Example:

Employee

John

Sarah

Mike

David

=COUNTA(A2:A5)

Result:

4

It can count text, numbers, dates, etc., as long as the cell isn't empty.

7️⃣ COUNTBLANK()

Counts empty cells.

=COUNTBLANK(A2:A100)

This is particularly useful for data-quality checks.

Example

Suppose you have 100 customer records and 7 customers have missing email addresses.

=COUNTBLANK(EmailColumn)

Result:

7

That immediately tells you something about data completeness.

8️⃣ ROUND()

Data often contains too many decimal places.

For example:

83.456789

You may want:

83.46

Use:

=ROUND(A2,2)

The 2 means two decimal places.

Examples

=ROUND(A2,0)

Rounds to a whole number.

=ROUND(A2,1)

Rounds to one decimal place.

=ROUND(A2,2)

Rounds to two decimal places.

9️⃣ ROUNDUP()

ROUNDUP() always rounds away from zero.

Example:

=ROUNDUP(83.451,2)

Result:

83.46

Compare this with ROUND() where the result depends on the next digit.

This can be useful when business rules require conservative upward rounding.

🔟 ROUNDDOWN()

ROUNDDOWN() always rounds toward zero.

=ROUNDDOWN(83.459,2)

Result:

83.45

Understanding the difference between:

ROUND → ROUNDUP → ROUNDDOWN

is useful when working with financial and operational calculations.

1️⃣1️⃣ SUM vs COUNT vs AVERAGE

This is a common beginner confusion.

Suppose:

Sales:

10,000

20,000

30,000

SUM

=SUM(A2:A4)

Result:

60,000

COUNT

=COUNT(A2:A4)

Result:

3

AVERAGE

=AVERAGE(A2:A4)

Result:

20,000

Remember:

SUM → Total

COUNT → Number of numeric records

AVERAGE → Mean

1️⃣2️⃣ Combining Functions

The real power of Excel comes from combining functions.

For example, suppose you want:

Total sales divided by number of orders.

You could write:

=SUM(B2:B100)/COUNT(B2:B100)

This calculates the average sales per numeric record.

Or simply:

=AVERAGE(B2:B100)

Understanding both approaches helps you understand what Excel is actually calculating.

1️⃣3️⃣ Using Cell References Instead of Hardcoding

Avoid unnecessary hardcoding.

Instead of:

=SUM(B2:B100)_1.18

you could put the tax rate in another cell.

For example:

F1 = 18%

Then:

=SUM(B2:B100)_(1+$F$1)

Now if the tax rate changes, you only change F1.

This makes your analysis more flexible.

1️⃣4️⃣ Relative References

Consider:

=B2_C2

If you copy this formula to row 3, Excel changes it to:

=B3_C3

This is a relative reference.

It's extremely useful when applying the same calculation to many rows.

1️⃣5️⃣ Absolute References

Suppose:

F1 = 18%

You want to apply this percentage to every row.

Use:

=C2_$F$1

When copied down:

=C3_$F$1

=C4_$F$1

=C5_$F$1

F1 stays fixed.

The $ tells Excel:

Don't move this reference.

1️⃣6️⃣ Mixed References

You may also encounter:

$A1

A$1

$A1

Column A is fixed, row can change.

A$1

Row 1 is fixed, column can change.

These become particularly useful when building complex Excel models.

🧪 Practical Example

Suppose you have:

Employee Sales

John 50,000

Sarah 75,000

Mike 60,000

David 90,000

Alice 45,000

You can calculate:

Total Sales

=SUM(B2:B6)

320,000

Average Sales

=AVERAGE(B2:B6)

64,000

Highest Sales

=MAX(B2:B6)

90,000

Lowest Sales

=MIN(B2:B6)

45,000

Number of Employees

=COUNT(B2:B6)

5

🎯 Mini Interview Challenge

Your interviewer gives you this dataset:

Employee Sales

John 45,000

Sarah 80,000

Mike 65,000

David 95,000

Alice 55,000

They ask:

Q1. What is total sales?

=SUM(B2:B6)

Q2. What is average sales?

=AVERAGE(B2:B6)

Q3. What is the highest sales?

=MAX(B2:B6)

Q4. What is the lowest sales?

=MIN(B2:B6)

Q5. How many employees have sales values?

=COUNT(B2:B6)

If you can answer these comfortably, you've covered the core of Excel Level 2.

🏆 Quick Recap

"What is the total?" → SUM()

"What is the average?" → AVERAGE()

"What is the highest?" → MAX()

"What is the lowest?" → MIN()

"How many numeric records?" → COUNT()

"How many non-empty records?" → COUNTA()

"How many missing values?" → COUNTBLANK()

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🗄️ How to Solve SQL Problems

If you are a beginner, don't try to write the entire SQL query immediately. The easiest approach is to break the problem into small steps.

📌 Step 1: Understand What the Question Is Asking

Read the question carefully and identify the final output.

Example:

Find the total sales for each customer.

Ask yourself:

👉 What do I need to display?

Answer:

Customer

Total Sales

📌 Step 2: Identify the Table

Find which table contains the required information.

Suppose you have:

sales

customer_id

product

quantity

price

You need the sales table.

📌 Step 3: Identify the Required Columns

For:

Find total sales for each customer.

You need:

customer_id

quantity

price

Because: Sales = quantity × price

📌 Step 4: Decide Whether You Need Filtering

Ask:

Do I need only certain rows?

For example:

Find total sales for customers who purchased in 2026.

Now you need a WHERE condition.

WHERE order_date >= '2026-01-01'

📌 Step 5: Decide Whether You Need GROUP BY

Look for words such as: Each customer, Each department, Per product, By region, By month

These usually indicate GROUP BY.

For example:

Find total sales for each customer.

GROUP BY customer_id

📌 Step 6: Identify the Required Aggregate Function

Look for words like:

Total → SUM()

Average → AVG()

Count → COUNT()

Maximum → MAX()

Minimum → MIN()

For total sales:

SUM(quantity _ price)

📌 Step 7: Build the Query Step by Step

Instead of writing everything at once:

1.

SELECT customer_id FROM sales;

2.

Add the calculation:

SELECT customer_id, SUM(quantity _ price) AS total_sales FROM sales;

3.

Add grouping:

SELECT

customer_id,

SUM(quantity ** price) AS total_sales

FROM sales

GROUP BY customer_id;

Now the query is complete.

📌 Step 8: Check Whether You Need HAVING

Suppose the question changes to:

Find customers whose total sales are greater than ₹50,000.

You cannot use WHERE on SUM(). Use HAVING:

SELECT

customer_id,

SUM(quantity ** price) AS total_sales

FROM sales

GROUP BY customer_id

HAVING SUM(quantity ** price) > 50000;

📌 Step 9: Check Whether You Need a JOIN

Suppose the question says:

Find the names of customers and their total sales.

You have:

customers: customer_id, customer_name

sales: customer_id, quantity, price

Now you need a JOIN.

SELECT

c.customer_name,

SUM(s.quantity ** s.price) AS total_sales

FROM customers c

JOIN sales s

ON c.customer_id = s.customer_id

GROUP BY c.customer_name;

📌 Step 10: Validate Your Answer

Before considering the problem solved, check:

✓ Did I use the correct table?

✓ Did I select the correct columns?

✓ Is my JOIN correct?

✓ Did I handle NULL values?

✓ Did I accidentally create duplicates?

✓ Did I use WHERE or HAVING correctly?

✓ Does the output actually answer the question?

🧠 Use This SQL Problem-Solving Framework

Whenever you get a SQL question, think:

1. What is being asked?

2. Which table(s) do I need?

3. Which columns do I need?

4. Do I need filtering?

5. Do I need a JOIN?

6. Do I need aggregation?

7. Do I need GROUP BY?

8. Do I need HAVING?

9.

Do I need a window function?

10. Validate the result

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🚀 Data Analyst Roadmap — Part 4

📊 Excel — Level 3: Conditional Functions

Now that you understand basic Excel formulas, the next step is learning how to make Excel make decisions based on conditions.

This is a very important skill for Data Analysts because real-world questions are rarely just:

"What is the total?"

Instead, you'll get questions like:

"What are the total sales for the IT department?"

"How many employees earn more than ₹80,000?"

"What is the average sales for the North region?"

"Which employees achieved their target?"

To answer these questions, you need conditional functions.

1️⃣ IF()

IF() is one of the most important Excel functions.

It allows Excel to make a decision.

Syntax

=IF(condition, value_if_true, value_if_false)

Think of it as:

If something is true → do this; otherwise → do that.

Example

Suppose sales are in B2.

You want to classify employees:

Sales ≥ 50,000 → High

Sales < 50,000 → Low

=IF(B2>=50000,"High","Low")

If B2 is:

75,000

Result: High

If B2 is:

35,000

Result: Low

2️⃣ IF() in Real-World Data Analysis

Suppose you have:

Employee | Sales

John | 75,000

Sarah | 45,000

Mike | 90,000

David | 30,000

You can create a performance column:

=IF(B2>=50000,"Target Achieved","Target Not Achieved")

Result:

Employee | Sales | Status

John | 75,000 | Target Achieved

Sarah | 45,000 | Target Not Achieved

Mike | 90,000 | Target Achieved

David | 30,000 | Target Not Achieved

This is called data categorization.

3️⃣ Multiple Conditions with Nested IF()

Sometimes you need more than two categories.

For example:

≥ 80,000 → Excellent

≥ 60,000 → Good

≥ 40,000 → Average

< 40,000 → Poor

You can use:

=IF(B2>=80000,"Excellent",IF(B2>=60000,"Good",IF(B2>=40000,"Average","Poor")))

Excel checks the conditions from left to right.

Important: The order matters. You should generally check the highest threshold first.

4️⃣ IFS()

IFS() is a cleaner alternative when you have multiple conditions.

=IFS(
B2>=80000,"Excellent",
B2>=60000,"Good",
B2>=40000,"Average",
TRUE,"Poor"
)

The first condition that evaluates to TRUE determines the result.

IF vs IFS

Use:

IF() → simple decisions

IFS() → multiple conditions

5️⃣ AND()

AND() checks whether all conditions are true.

Example

You want to identify employees who:

Belong to IT AND earn more than ₹80,000

=AND(B2="IT",C2>80000)

Both conditions must be true.

6️⃣ Combining IF() + AND()

This is more useful in real analysis.

=IF(AND(B2="IT",C2>80000),"Eligible","Not Eligible")

Meaning:

If the employee is from IT AND salary is greater than ₹80,000, return "Eligible".

Otherwise: "Not Eligible"

7️⃣ OR()

OR() checks whether at least one condition is true.

Example:

You want to identify employees who belong to either:

IT OR Finance

=OR(B2="IT",B2="Finance")

If either condition is true, the result is TRUE.

8️⃣ Combining IF() + OR()

=IF(
OR(B2="IT",B2="Finance"),
"Technical Department",
"Other"
)

This is extremely useful for business analysis.

1️⃣8️⃣ Understand IF vs IF Functions

This distinction is important.

IF()

Used to make a decision.

Example: =IF(C2>=50000,"High","Low")

SUMIF()

Used to calculate a sum based on a condition.

Example: =SUMIF(B2:B100,"IT",C2:C100)

COUNTIF()

Used to count records based on a condition.

Example: =COUNTIF(B2:B100,"IT")

AVERAGEIF()

Used to calculate an average based on a condition.

Example: =AVERAGEIF(B2:B100,"IT",C2:C100)

Think:

IF → Decision

SUMIF → Conditional Total

COUNTIF → Conditional Count

AVERAGEIF → Conditional Average

🧪 Practical Interview Challenge

Suppose you have:

Employee | Department | Salary

John | IT | 75,000

Sarah | HR | 60,000

Mike | IT | 82,000

David | Finance | 90,000

Alice | HR | 65,000

Your interviewer asks:

Q1. Is John earning more than ₹70,000?

=IF(C2>70000,"Yes","No")

Q2. How many employees are in IT?

=COUNTIF(B2:B6,"IT")

Q3. What is the total IT salary?

=SUMIF(B2:B6,"IT",C2:C6)

Q4. What is the average IT salary?

=AVERAGEIF(B2:B6,"IT",C2:C6)

Q5. How many IT employees earn more than ₹80,000?

=COUNTIFS(B2:B6,"IT",C2:C6,">80000")

Q6. What is the total salary of IT employees earning more than ₹70,000?

=SUMIFS(C2:C6,B2:B6,"IT",C2:C6,">70000")

🏆 Key Lesson

Understand the question first.

"Should I classify this record?"

→ IF()

"How much in total?"

→ SUMIF() / SUMIFS()

"How many?"

→ COUNTIF() / COUNTIFS()

"What's the average?"

→ AVERAGEIF() / AVERAGEIFS()

One condition?

→ IF version

Multiple conditions?

→ IFS version

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📊 Excel Basics #32 – Data Validation

When multiple people enter data into an Excel sheet, incorrect or inconsistent entries can easily create data-quality problems.

For example:

❌ Someone enters "Pending"

❌ Someone enters "pending"

❌ Someone enters "Pendng"

Data Validation helps control what users can enter into a cell.

📌 What is Data Validation?

Data Validation allows you to set rules that restrict or control the type of data entered into a cell.

Go to:

Data → Data Validation

📌 1. Create a Drop-Down List

One of the most common uses of Data Validation is creating a dropdown.

Example:

You want users to select only:

• Pending

• In Progress

• Completed

Steps:

1️⃣ Select the cells.

2️⃣ Go to Data → Data Validation.

3️⃣ Under Allow, select List.

4️⃣ Enter:

Pending,In Progress,Completed

5️⃣ Click OK.

Now users can select a status from a dropdown instead of typing it manually.

📌 2. Restrict Numbers

You can restrict users to entering numbers within a specific range.

Example:

Allow marks only between 0 and 100.

Go to:

Data Validation → Allow → Whole Number

Then set:

between → 0 → 100

If someone enters "150", Excel can reject the entry.

📌 3. Restrict Dates

You can also control which dates users can enter.

Example:

Allow dates only between:

01-Jan-2026 and 31-Dec-2026

This is useful for project trackers, financial reports, and attendance sheets.

📌 4. Restrict Text Length

You can limit the number of characters entered.

Example:

Employee ID must contain a maximum of 10 characters.

Go to:

Data Validation → Allow → Text Length

Then specify the required limit.

📌 5. Create an Input Message

Data Validation can display instructions when a user selects the cell.

Example:

Input Message:

"Select a valid project status from the dropdown."

This helps users understand what they are expected to enter.

📌 6. Create an Error Alert

You can decide what happens when someone enters invalid data.

Excel provides options such as:

Stop → Prevent invalid entry.

Warning → Warn the user but allow them to continue.

Information → Display an informational message.

For important business data, Stop is usually the safest option.

📌 Real-World Example

Imagine a project tracker:

Employee | Status | Priority

Rahul | Completed | High

Priya | In Progress | Medium

Amit | Pending | Low

Instead of allowing users to type anything, create dropdowns for:

Status:

• Pending

• In Progress

• Completed

Priority:

• High

• Medium

• Low

This keeps the dataset consistent and easier to analyze.

📌 Common Mistakes

❌ Allowing users to type values manually when a dropdown would be better.

❌ Not setting an error alert.

❌ Applying validation to only part of the required data range.

❌ Using inconsistent values in the source list.

✅ Best Practices

• Use dropdowns for fixed categories.

• Restrict numbers and dates where appropriate.

• Add helpful input messages.

• Use meaningful error messages.

• Apply validation before distributing the workbook.

• Keep the allowed values standardized.

💡 Remember:

Data Validation doesn't just make Excel look professional.

It helps improve data quality by controlling what users can enter.

For data analysts, this is especially important because clean and consistent input data leads to more reliable analysis.

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🚀 Data Analyst Roadmap — Part 6

📊 Excel — Level 5: Text Functions for Data Cleaning & Transformation

As a Data Analyst, you'll rarely receive perfectly clean data.

You may encounter:

" John"

"John "

"JOHN"

"john"

"John Smith"

"John Smith"

You may also have data such as:

EMP-001-IND

Mumbai, India

john.smith@email.com

+91-9876543210

Before analyzing this data, you often need to clean, extract, combine, split, or standardize text.

That's why Excel's text functions are extremely useful.

1️⃣ TRIM()

What does it do?

TRIM() removes unnecessary spaces from text.

For example:

" John Smith "

becomes:

"John Smith"

Formula:

=TRIM(A2)

Why is this important?

Suppose you have:

IT

IT

IT

IT

They may look identical, but hidden spaces can cause lookup and filtering problems.

For example:

=XLOOKUP("IT",A2:A100,B2:B100)

may not behave as expected if the underlying values contain unwanted spaces.

Data Analyst use cases:

Use TRIM() for:

• Customer names

• Department names

• Product names

• Country names

• Category values

2️⃣ CLEAN()

CLEAN() removes many non-printing characters from text.

Formula:

=CLEAN(A2)

This can be useful when data is copied from:

• Websites

• External systems

• Reports

• PDFs

• Legacy applications

Sometimes invisible characters are present even though the text looks normal.

TRIM vs CLEAN:

TRIM() → Removes unnecessary spaces.

CLEAN() → Removes non-printing characters.

You can combine them:

=TRIM(CLEAN(A2))

This is a very useful basic data-cleaning pattern.

3️⃣ UPPER()

Converts text to uppercase.

=UPPER(A2)

Example:

india

becomes:

INDIA

Why use it?

Suppose your dataset contains:

India

india

INDIA

You can standardize them using:

=UPPER(A2)

Now they all become:

INDIA

4️⃣ LOWER()

Converts text to lowercase.

=LOWER(A2)

Example:

JOHN.SMITH@EMAIL.COM

becomes:

john.smith@email.com

This is particularly useful for standardizing:

• Email addresses

• Usernames

• IDs

• Text categories

——————————

5️⃣ PROPER()

Converts text into proper case.

=PROPER(A2)

Example:

john smith

becomes:

John Smith

And:

mumbai

becomes:

Mumbai

Important:

PROPER() is useful for presentation, but don't automatically use it for every dataset.

Some names, product codes, or abbreviations should remain uppercase.

For example:

IBM

SQL

USA

may become undesirable results if automatically converted to proper case.

6️⃣ LEN()

LEN() returns the number of characters in a text string.

=LEN(A2)

Example:

A2 = "John"

Result:

4

Why is this useful?

It can help identify:

• Invalid IDs

• Incorrect phone numbers

• Unexpected text lengths

• Data-quality issues

For example:

Employee IDs should always contain 6 characters.

You could check:

=IF(LEN(A2)=6,"Valid","Check")

7️⃣ LEFT()

LEFT() extracts characters from the beginning of a text string.

Syntax:

=LEFT(text,num_chars)

Example:

EMP-001-IND

To extract the first three characters:

=LEFT(A2,3)

Result:

EMP

8️⃣ RIGHT()

RIGHT() extracts characters from the end of a text string.

Example:

EMP-001-IND

Formula:

=RIGHT(A2,3)

Result:

IND

This can be useful for extracting:

• Country codes

• File extensions

• Product suffixes

• Transaction codes

9️⃣ MID()

MID() extracts text from the middle of a string.

Syntax:

=MID(text,start_num,num_chars)

Suppose:

EMP-001-IND

You want:

001

Use:

=MID(A2,5,3)

Result:

001

Because:

Start at character 5

Extract 3 characters

🔟 FIND()

FIND() tells you where one piece of text appears inside another.

Example:

john.smith@gmail.com

You can find the position of @:

=FIND("@",A2)

This returns the position of the @ character.

Why is this useful?

You can use the position to extract:

• Email username

• Domain

• Product components

• Codes

• Identifiers

1️⃣1️⃣ SEARCH()

SEARCH() is similar to FIND() but has some differences.

For example:

=SEARCH("india",A2)

Unlike FIND(), SEARCH() is not case-sensitive.

Simple distinction:

FIND() → Case-sensitive

SEARCH() → Not case-sensitive

This difference can matter when cleaning real-world data.

1️⃣2️⃣ SUBSTITUTE()

SUBSTITUTE() replaces specific text with another value.

Suppose:

A2 = Mumbai, India

You want to replace the comma with a hyphen.

=SUBSTITUTE(A2,",","-")

Result:

Mumbai- India

You can also replace words.

=SUBSTITUTE(A2,"India","IND")

Result:

Mumbai, IND

1️⃣3️⃣ CONCAT()

CONCAT() combines text.

Suppose:

First Name | Last Name

John | Smith

Formula:

=CONCAT(A2," ",B2)

Result:

John Smith

This is useful when you need to create:

• Full names

• IDs

• Labels

• Descriptions

1️⃣4️⃣ TEXTJOIN()

TEXTJOIN() is particularly useful when combining multiple values with a delimiter.

Example:

Suppose:

A2 = John

B2 = Smith

C2 = India

Formula:

=TEXTJOIN(", ",TRUE,A2:C2)

Result:

John, Smith, India

The second argument:

TRUE

tells Excel to ignore empty cells.

1️⃣5️⃣ TEXTSPLIT()

Modern Excel includes TEXTSPLIT(), which is extremely useful for breaking text into multiple columns.

Suppose:

A2 = John,IT,Pune

Use:

=TEXTSPLIT(A2,",")

Excel can split it into:

John | IT | Pune

This is particularly useful when data arrives in a delimited format.

1️⃣6️⃣ Extract an Email Username

Suppose:

A2 = john.smith@gmail.com

You want:

john.smith

Using modern Excel:

=TEXTBEFORE(A2,"@")

Result:

john.smith

1️⃣7️⃣ Extract an Email Domain

Using the same data:

john.smith@gmail.com

Use:

=TEXTAFTER(A2,"@")

Result:

gmail.com

These modern text functions can make data preparation much easier.

1️⃣8️⃣ Combining Text Functions

The real power comes from combining functions.

Suppose your data contains:

"  JOHN SMITH  "

You want:

John Smith

You could use:

=PROPER(TRIM(A2))

First:

TRIM() removes unnecessary spaces.

Then:

PROPER() formats the name.

Result:

John Smith

1️⃣9️⃣ Real-World Data Cleaning Example

Suppose your department column contains:

IT

IT

it

IT

It

These values may represent the same department.

You could standardize them with:

=UPPER(TRIM(A2))

Results become:

IT

IT

IT

IT

IT

Now filtering, counting and lookups become much more reliable.

2️⃣0️⃣ Data Quality Check Using Text Functions

Suppose all employee IDs should contain exactly 6 characters.

You can use:

=IF(LEN(A2)=6,"Valid","Check")

If:

A2 = EMP001

Result:

Valid

If:

A2 = EMP01

Result:

Check

This is a simple example of using Excel for data-quality validation.

🧪 Practical Interview Challenge

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.

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