𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀

Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.

📅 Date: 24 September 2026
⏰ Time: 7:00 PM–9:00 PM IST
🌐 Mode: Online
🎓 Certificate: Available to all attendees

Eligibility :- Graduates Passing In 2025 or earlier

🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇

https://pdlink.in/4xAMeGW

⚡ Register now and take your first step towards a successful career in AI!

🚀 Complete Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 1: SQL Basics — SELECT

SQL is one of the most important skills for a Data Scientist because real-world data is often stored in relational databases.

Before using Python, Machine Learning, or advanced analytics, you will frequently need to:

Retrieve data

Filter data

Combine tables

Aggregate information

Create datasets for analysis

Answer business questions

We'll start from the foundation: SELECT.

🔹 1. What Is SQL?

SQL stands for:

Structured Query Language

It is used to communicate with relational databases.

For example, a company might store:

Customers

customer_id | name  | city   | age
101         | Alice | Mumbai | 28
102         | Bob   | Pune   | 32
103         | Carol | Delhi  | 25

You can use SQL to retrieve specific information from this table.

🔹 2. What Is a Database?

A database is a structured system used to store and manage data.

A relational database stores information in tables.

For example:

Customers

Contains customer information.

Orders

Contains order information.

Products

Contains product information.

These tables can be related using common columns such as:

customer_id

This becomes extremely important when we learn JOINs.

🔹 3. What Is a Table?

A table consists of:

Rows

Each row generally represents one record.

Example: One customer

Columns

Each column represents an attribute.

Example: customer_id, name, city, age

So:

Row → Record

Column → Attribute

🔹 4. Your First SQL Query

The basic SQL query is:

SELECT *
FROM customers;

Let's break it down:

SELECT

Specifies what data you want.

*

Means: Select all columns.

FROM

Specifies the table from which you want the data.

customers

The table name.

So the query means:

Give me all columns from the customers table.

🔹 5. Selecting Specific Columns

You don't always need every column.

Suppose you only want:

Customer ID

Customer name

Use:

SELECT customer_id, name
FROM customers;

Result:

customer_id | name
101         | Alice
102         | Bob
103         | Carol

This is usually better than using SELECT * when you only need a few columns.

🔹 6. Selecting One Column

You can select a single column:

SELECT name
FROM customers;

Result:

name
Alice
Bob
Carol

🔹 7. Selecting Multiple Columns

Separate column names using commas:

SELECT name, city, age
FROM customers;

This returns only those three columns.

🔹 8. What Does * Mean?

The asterisk:

*

means: All columns.

Example:

SELECT *
FROM customers;

If the table has 10 columns, the query returns all 10.

However, in production environments, it is often better to explicitly specify the columns you need.

Instead of:

SELECT *
FROM customers;

prefer:

SELECT customer_id, name, city
FROM customers;

when those are the only fields required.

🔹 9. SQL Statements and Semicolon

SQL statements are commonly terminated with:

;

Example:

SELECT name
FROM customers;

The semicolon indicates the end of the SQL statement in many SQL environments.

🔹 10. SQL Is Declarative

This is an important concept.

When you write:

SELECT name
FROM customers;

you tell the database:

What data you want

You generally don't tell the database exactly how to retrieve it internally.

The database's query optimizer determines an efficient execution strategy.

This is one reason SQL is called a declarative language.

🔹 11. SQL Keywords

SQL uses keywords such as:

SELECT

FROM

WHERE

GROUP BY

ORDER BY

HAVING

JOIN

These keywords define the structure of the query.

For example:

SELECT name
FROM customers;

Here:

SELECT → What to retrieve

FROM → Where to retrieve it from

🔹 12. SQL Case Sensitivity

SQL keywords are commonly written in uppercase:

SELECT

FROM

WHERE

This improves readability.

For example:

SELECT customer_id, name
FROM customers;

is easier to read than:

select customer_id,name from customers;

Most SQL database systems treat keywords as case-insensitive, although behavior regarding identifiers such as table and column names can vary by database system and configuration.

Recommended style:

Use:

UPPERCASE for SQL keywords

lowercase or snake_case for column/table names

🔹 13. Column Aliases

You can temporarily give a column a different name using AS.

Example:

SELECT
    name AS customer_name
FROM customers;

The result will display:

customer_name
Alice
Bob
Carol

The original column name in the database is not changed.

The alias only changes how the result is displayed.

🔹 14. Aliases Without AS

In many SQL systems, you can also write:

SELECT
    name customer_name
FROM customers;

However, using AS is generally clearer:

SELECT
    name AS customer_name
FROM customers;

🔹 15. Calculations in SELECT

SQL can perform calculations.

Suppose we have:

price

quantity

We can calculate total sales:

SELECT
    price,
    quantity,
    price * quantity AS total_amount
FROM orders;

This creates a calculated column:

total_amount = price × quantity

This ability becomes extremely useful in Data Analytics.

🔹 16. Using SELECT with Expressions

You can perform various calculations.

Example:

SELECT
    salary,
    salary * 12 AS annual_salary
FROM employees;

If monthly salary is:

₹50,000

then:

annual_salary = ₹600,000

The original database isn't modified.

The calculation is performed when the query runs.

🔹 17. Selecting Constants

SQL can also return constant values.

Example:

SELECT
    'Data Science' AS course;

Result:

course
Data Science

You can also use numbers:

SELECT
    2026 AS year;

Result:

year
2026

This becomes useful when constructing analytical datasets.

🔹 18. DISTINCT

DISTINCT is part of the roadmap and we'll study it properly later.

For now, understand its basic purpose:

It returns unique values.

Suppose:

city

Pune

Mumbai

Pune

Delhi

Mumbai

Query:

SELECT DISTINCT city
FROM customers;

Result:

Pune
Mumbai
Delhi

Duplicate values are removed from the result.

🔹 19. SELECT DISTINCT on Multiple Columns

You can use multiple columns:

SELECT DISTINCT city, department
FROM employees;

Important:

DISTINCT applies to the combination of selected columns.

So if two rows have the same city but different departments, they are considered different combinations.

🔹 20. SQL Query Example

Imagine an orders table:

order_id | customer_id | product | price
1        | 101         | Laptop  | 60000
2        | 102         | Phone   | 30000
3        | 101         | Mouse   | 1000

To retrieve order details:

SELECT
    order_id,
    customer_id,
    product,
    price
FROM orders;

To calculate price after adding a hypothetical 10% increase:

SELECT
    product,
    price,
    price * 1.10 AS increased_price
FROM orders;

🔹 21. SELECT in Real-World Data Science

SQL is often the first step in a Data Science workflow.

For example:

Business Question

"Give me all customer transactions from the sales database."

You might start with:

SELECT
    customer_id,
    order_date,
    product_id,
    amount
FROM transactions;

Then later:

WHERE → Filter data

GROUP BY → Aggregate data

JOIN → Combine tables

ORDER BY → Sort results

Window Functions → Advanced analysis

Eventually, you may load the SQL result into Pandas:

import pandas as pd
df = pd.read_sql(query, connection)

So SQL and Python often work together.

🔹 22. Common Beginner Mistakes

❌ Mistake 1: Forgetting the FROM clause

Incorrect:

SELECT name;

when you intend to retrieve a column from a table.

Correct:

SELECT name
FROM customers;

❌ Mistake 2: Using commas incorrectly

Correct:

SELECT name, city, age
FROM customers;

❌ Mistake 3: Confusing column names and values

A column:

city

is different from a text value:

'Pune'

We'll explore this more when we learn WHERE.

❌ Mistake 4: Using SELECT * everywhere

SELECT * is useful while learning and exploring, but for production queries, selecting only the required columns can be more efficient and clearer.

🔹 23. Interview Questions

💡 What does SELECT do?

SELECT specifies the columns or expressions that should appear in the query result.

💡 What does SELECT * mean?

It selects all columns from the specified table.

💡 What is an alias?

An alias gives a temporary name to a column or expression in the query result.

💡 What does DISTINCT do?

It removes duplicate rows from the selected result.

🎯 Practice Questions

Q1. Write a query to select all columns from a table called employees.

Q2. Write a query to select only employee_id and salary from employees.

Q3. Write a query to display salary as monthly_salary.

Q4. Write a query to calculate price * quantity as total_amount from an orders table.

Q5. Write a query to return unique values from the department column of an employees table.

🎯 Key Takeaways

✅ SQL is used to communicate with relational databases.

✅ SELECT specifies what you want to retrieve.

✅ FROM specifies the table.

✅ * means all columns.

✅ You can select one or multiple columns.

✅ AS creates a temporary alias.

✅ SQL can perform calculations.

✅ DISTINCT returns unique results.

✅ SQL is one of the most important tools for extracting data before analysis and Machine Learning.

🧭 Double Tap ❤️ For More

🎓 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀! 🚀

Explore free online learning opportunities from Stanford University across technology, business and more!

💻 Tech & Programming
🤖 Artificial Intelligence & Data Science
💼 Business & Entrepreneurship
💡 Leadership & Innovation

🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇

https://pdlink.in/4hlnZGw

🎯 Great for students, freshers and working professionals looking to expand their knowledge.

🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊

Want to start a career in Data Analytics?

Explore these 7 free Microsoft-backed learning resources covering Power BI, Excel, SQL and data fundamentals

🔗 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇

https://pdlink.in/3Tm2D3Z

💡 Ideal for students, freshers and professionals who want to build practical data skills.

Soft skills questions will be part of your next data job interview!

Here is what you should prepare for:

1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you ensure that your data insights are understood and get used by non-technical stakeholders?”

2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”

3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”

4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you handle sudden changes in project priorities or scope?”

5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Tell me about a time when you were under tight deadlines. How did you manage to meet them?”

6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.

𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you approach understanding the needs of different stakeholders when starting a new project?”


Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.

By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.

You can find useful tips to improve your soft skills here: 👇 https://t.me/englishlearnerspro/

🚀 Data Science Roadmap 2026

**

📍 Phase 3: SQL for Data Science**

📖 Topic 2: SQL Basics — WHERE

After learning SELECT, the next essential SQL concept is WHERE.

In real-world Data Science, databases can contain millions or billions of records. You usually don't want to retrieve everything.

You want to answer questions such as:

Which customers are from Mumbai?

Which orders are above ₹10,000?

Which employees joined after 2023?

Which transactions were successful?

Which products belong to a particular category?

The WHERE clause allows you to filter rows based on conditions.

🔹 1. What Is WHERE?

WHERE is used to filter records based on a specified condition.

Basic syntax:

SELECT column1, column2
FROM table_name
WHERE condition;

Example:

SELECT *
FROM customers
WHERE city = 'Mumbai';

This returns only customers whose city is Mumbai.

🔹 2. WHERE with Text Values

Text values are generally written inside single quotes.

Example:

SELECT customer_id, name
FROM customers
WHERE city = 'Pune';

This retrieves customers from Pune.

Another example:

SELECT *
FROM employees
WHERE department = 'Finance';

🔹 3. WHERE with Numbers

For numeric values, quotes are generally not required.

Example:

SELECT *
FROM customers
WHERE age > 30;

This returns customers older than 30.

Another example:

SELECT *
FROM orders
WHERE amount > 10000;

This returns orders where the amount is greater than 10,000.

🔹 4. Comparison Operators

The most commonly used comparison operators are:

Operator Meaning

= Equal to

<> Not equal to

!= Not equal to

Greater than

< Less than

= Greater than or equal to

<= Less than or equal to

Example:

SELECT *
FROM employees
WHERE salary >= 50000;

This returns employees whose salary is at least 50,000.

🔹 5. Equal To =

The = operator checks whether two values are equal.

SELECT *
FROM customers
WHERE city = 'Delhi';

Only records where city equals Delhi are returned.

🔹 6. Not Equal <>

You can retrieve records that don't match a value.

SELECT *
FROM customers
WHERE city <> 'Delhi';

This returns customers whose city isn't Delhi.

You may also see:

WHERE city != 'Delhi'

Both are commonly supported, although <> is the standard SQL operator.

🔹 7. Greater Than >

Example:

SELECT *
FROM orders
WHERE amount > 50000;

Returns orders above 50,000.

🔹 8. Less Than <

Example:

SELECT *
FROM products
WHERE price < 1000;

Returns products priced below 1,000.

🔹 9. Greater Than or Equal To >=

Example:

SELECT *
FROM employees
WHERE experience >= 5;

This includes employees with exactly 5 years as well as those with more than 5 years.

🔹 10. Less Than or Equal To <=

Example:

SELECT *
FROM products
WHERE price <= 500;

This includes products priced exactly at 500.

🔹 11. WHERE with Multiple Conditions

Real-world queries often require more than one condition.

For this, SQL provides logical operators:

• AND

• OR

• NOT

🔹 12. AND

AND means all conditions must be true.

Example:

SELECT *
FROM customers
WHERE city = 'Pune'
AND age > 30;

This returns customers who:

1.

Are from Pune

2.

Are older than 30

Both conditions must be satisfied.

🔹 13. OR

OR means at least one condition must be true.

Example:

SELECT *
FROM customers
WHERE city = 'Pune'
OR city = 'Mumbai';

This returns customers from either Pune or Mumbai.

🔹 14. AND vs OR

Consider:

WHERE age > 30
AND city = 'Pune'

A customer must satisfy both conditions.

But:

WHERE age > 30
OR city = 'Pune'

A customer only needs to satisfy one or both conditions.

This difference is extremely important.

🔹 15. NOT

NOT reverses a condition.

Example:

SELECT *
FROM customers
WHERE NOT city = 'Pune';

This returns customers who aren't from Pune.

You can also commonly write:

SELECT *
FROM customers
WHERE city <> 'Pune';

🔹 16. Combining AND and OR

You can combine multiple logical operators.

Example:

SELECT *
FROM employees
WHERE department = 'Finance'
AND salary > 60000;

Another example:

SELECT *
FROM employees
WHERE department = 'Finance'
OR department = 'Analytics'
AND salary > 60000;

When conditions become complex, use parentheses to make your intended logic explicit.

For example:

SELECT *
FROM employees
WHERE
    (department = 'Finance' OR department = 'Analytics')
    AND salary > 60000;

This means:

Employees from Finance or Analytics who earn more than 60,000.

🔹 17. Why Parentheses Matter

Consider:

WHERE city = 'Pune'
OR city = 'Mumbai'
AND age > 30

SQL's logical evaluation rules can make this behave differently from what a beginner might expect.

A safer and clearer version is:

WHERE
    (city = 'Pune' OR city = 'Mumbai')
    AND age > 30;

This clearly communicates the intended logic.

Best practice:

Use parentheses whenever combining AND and OR in a complex condition.

🔹 18. WHERE with Dates

You can also filter dates.

Example:

SELECT *
FROM orders
WHERE order_date >= '2026-01-01';

This retrieves orders on or after January 1, 2026.

Another example:

SELECT *
FROM orders
WHERE order_date < '2026-07-01';

This retrieves orders before July 1, 2026.

Date syntax can vary slightly across database systems, so always consider the SQL dialect you're using.

🔹 19. Filtering a Date Range

Suppose you want orders during a particular period.

You can use:

SELECT *
FROM orders
WHERE order_date >= '2026-01-01'
AND order_date < '2026-04-01';

This retrieves orders from January through March.

Using a half-open range like this is particularly useful when working with timestamps because it avoids accidentally excluding records with time components.

🔹 20. BETWEEN

SQL provides BETWEEN for range filtering.

Example:

SELECT *
FROM products
WHERE price BETWEEN 1000 AND 5000;

BETWEEN is inclusive of both boundaries in standard SQL.

So this includes:

1000

and:

5000

as well as values between them.

🔹 21. BETWEEN with Dates

Example:

SELECT *
FROM orders
WHERE order_date BETWEEN '2026-01-01' AND '2026-01-31';

For a date-only column, this can be useful.

However, if order_date contains timestamps, using:

order_date >= '2026-01-01'
AND order_date < '2026-02-01'

is often safer because it includes the entire final day regardless of the timestamp.

🔹 22. IN Operator

Suppose you want customers from:

Pune

Mumbai

Delhi

You could write:

SELECT *
FROM customers
WHERE city = 'Pune'
OR city = 'Mumbai'
OR city = 'Delhi';

But IN makes this much cleaner:

SELECT *
FROM customers
WHERE city IN ('Pune', 'Mumbai', 'Delhi');

IN checks whether a value belongs to a specified list.

🔹 23. NOT IN

You can also exclude multiple values.

SELECT *
FROM customers
WHERE city NOT IN ('Pune', 'Mumbai');

This returns customers whose city isn't Pune or Mumbai.

🔹 24. LIKE

LIKE is used for pattern matching.

Suppose we want names beginning with A.

SELECT *
FROM customers
WHERE name LIKE 'A%';

Here:

% → Any sequence of characters

So this could match:

Alice

Amit

Ananya

🔹 25. LIKE with %

Example:

SELECT *
FROM customers
WHERE name LIKE '%an%';

This searches for names containing the sequence an.

The exact behavior can depend on database collation and case-sensitivity settings.

🔹 26. LIKE with _

The underscore _ generally represents exactly one character.

Example:

SELECT *
FROM products
WHERE product_code LIKE 'A_1';

This could match:

A11

AB1

AX1

But not:

A123

A1

because _ represents one character.

🔹 27. NULL Values

One of the most important concepts in SQL filtering is NULL.

NULL generally means:

Missing, unknown, or unavailable value.

It does not mean:

Zero

Empty string

False

For example:

customer_id name phone

101 Alice 9999999999

102 Bob NULL

Bob's phone number is missing or unknown.

🔹 28. Checking for NULL

You should not normally write:

WHERE phone = NULL

Instead, use:

SELECT *
FROM customers
WHERE phone IS NULL;

To find records where the value exists:

SELECT *
FROM customers
WHERE phone IS NOT NULL;

This is extremely important in Data Analytics.

🔹 29. WHERE and NULL Logic

Suppose:

WHERE salary > 50000

What happens when salary is NULL?

The condition isn't considered true.

The row won't be returned.

SQL uses three-valued logic involving:

TRUE

FALSE

UNKNOWN

This is one reason NULL handling requires special attention.

🔹 30. WHERE with SELECT

WHERE works together with SELECT.

Example:

SELECT
    customer_id,
    name,
    city
FROM customers
WHERE city = 'Pune';

The query:

1.

Retrieves selected columns

2.

From the customers table

3.

Keeps only rows satisfying the condition

🔹 31. WHERE in Real-World Data Science

Imagine a transaction database containing millions of records.

A Data Scientist needs:

Successful transactions above ₹10,000 from January 2026 onward.

A query might look like:

SELECT
    transaction_id,
    customer_id,
    transaction_date,
    amount
FROM transactions
WHERE status = 'Success'
AND amount > 10000
AND transaction_date >= '2026-01-01';

This is much more efficient for analysis than extracting the entire table and filtering everything later in Python.

🔹 32. WHERE Before Python

A common Data Science workflow is:

Database → SQL → Filter/Transform → Python → Analysis → Model

For example:

SELECT
    customer_id,
    amount,
    transaction_date
FROM transactions
WHERE status = 'Success';

Then load the result into Pandas:

import pandas as pd

df = pd.read_sql(query, connection)

SQL handles the database-side filtering, while Python can then handle deeper analysis.

🔹 33. Common Mistakes

❌ Mistake 1: Using = with NULL

Incorrect:

WHERE phone = NULL;

Correct:

WHERE phone IS NULL;

❌ Mistake 2: Forgetting quotes around text

Incorrect:

WHERE city = Pune;

Correct:

WHERE city = 'Pune';

❌ Mistake 3: Using AND when you mean OR

Incorrect if you want either city:

WHERE city = 'Pune'
AND city = 'Mumbai';

A single city value cannot normally be both at the same time.

Correct:

WHERE city = 'Pune'
OR city = 'Mumbai';

Or:

WHERE city IN ('Pune', 'Mumbai');

❌ Mistake 4: Forgetting parentheses

For complex conditions, use parentheses:

WHERE
    (city = 'Pune' OR city = 'Mumbai')
    AND age > 30;

❌ Mistake 5: Assuming BETWEEN excludes the boundaries

BETWEEN is generally inclusive.

🔹 34. Interview Questions

💡 What is the purpose of WHERE?

WHERE filters rows based on a condition.

💡 What is the difference between WHERE and SELECT?

SELECT → Determines what columns/expressions appear in the result.

WHERE → Determines which rows are included.

💡 How do you check for NULL?

Use:

IS NULL

or:

IS NOT NULL

💡 What is the difference between IN and OR?

IN provides a concise way to test whether a value matches any value in a list.

💡 Is BETWEEN inclusive?

Yes, BETWEEN generally includes both boundary values.

🎯 Practice Questions

Q1. Write a query to retrieve employees whose salary is greater than 50,000.

Q2. Write a query to retrieve customers from Pune or Mumbai.

Q3. Write a query to retrieve products priced between 1,000 and 5,000.

Q4. Write a query to retrieve customers whose phone number is missing.

Q5. Write a query to retrieve orders where the status is Success and the amount is greater than 10,000.

🎯 Key Takeaways

✅ WHERE is used to filter rows.

✅ = checks equality.

✅ <> and != can be used for not equal.

✅ AND requires all specified conditions to be true.

✅ OR requires at least one condition to be true.

✅ IN is useful for matching multiple values.

✅ BETWEEN is useful for ranges and is generally inclusive.

✅ LIKE is used for pattern matching.

✅ % represents a sequence of characters.

✅ _ represents one character.

✅ Use IS NULL and IS NOT NULL for NULL values.

✅ Parentheses make complex AND/OR logic clearer and safer.

🧭 Double Tap ❤️ For More