U - Unstructured Data:
•  Data that does not have a predefined format (e.g., text documents, images, videos, social media posts).
•  Requires specialized tools and techniques for analysis.

V - Visualizations:
•  Charts, graphs, maps, and other visual elements used to represent data.
•  Choose the right visualization to effectively communicate your insights.

W - WHERE Clause (SQL):
•  A SQL clause used to filter rows based on specified conditions.
•  Essential for retrieving specific data from a table.

X - Exploratory Data Analysis (EDA):
•  An approach to analyzing data to summarize its main characteristics, often with visual methods.
•  Goal: To gain a better understanding of the data before performing more formal analysis.

Y - Y-axis (Charts):
•  The vertical axis in a chart, typically used to represent the dependent variable or the value being measured.

Z - Zero-Based Thinking:
•  An approach to data analysis that encourages you to question existing assumptions and look at the data with fresh eyes.

React ❤️ if you found this helpful!

𝗧𝗢𝗣 𝟱 𝗙𝗥𝗘𝗘 𝗧𝗘𝗖𝗛 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 𝗜 𝗪𝗜𝗦𝗛 𝗜 𝗛𝗔𝗗 𝗧𝗔𝗞𝗘𝗡 𝗦𝗢𝗢𝗡𝗘𝗥! 💻🎓

Discover 5 FREE tech courses to kick-start your learning journey and strengthen your resume. 🚀

✅ Learn Valuable Technical Skills
✅ Beginner-Friendly Learning Resources
✅ Build Practical Knowledge
✅ Strengthen Your Resume & Portfolio
✅ Prepare for Tech Career Opportunities

💡 The best investment you can make in 2026 is upgrading your skills!

🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :-

https://pdlink.in/4Bw1PL9

⚡ Start learning today. Your future self will thank you!

𝗔𝗜 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 | 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍

AI is changing the way we learn and work. Ready to take your first step?

- Learn the Skills, Hiring Trends, and Preparation Strategies That Matter

🎓 Students | 💼 Working Professionals | 💡 AI Enthusiasts

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇 :-

https://pdlink.in/4BAxISK

Date:- 31st October 2026 Time:- 5:00 PM (IST)

💫Share this opportunity with your friends and classmates.

𝗕𝗘𝗦𝗧 𝗙𝗥𝗘𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗖𝗘𝗥𝗧𝗜𝗙𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦 𝗜𝗡 𝟮𝟬𝟮𝟲! 🎓

Discover some of the best FREE Python learning and certification opportunities

✅ Free Python Learning Resources
✅ Beginner-Friendly Courses
✅ Practical Programming Knowledge
✅ Explore Available Certificates & Badges
✅ Strengthen Your Resume & LinkedIn Profile

🔗 𝗟𝗶𝗻𝗸 :- 👇

https://pdlink.in/4iVDpmN

📌 SAVE this post & SHARE with friends preparing for tech careers! 🔥

🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 10: UNION and UNION ALL

In this lesson, we'll learn how to combine data from multiple tables using UNION and UNION ALL.

🔹 1. What is UNION in SQL?

UNION is used to combine the results of two or more SELECT queries into a single result.

It combines rows vertically, meaning one result is added below another.

For example, imagine you have two tables containing customer information.

Table 1: customers_2025

• 101: Alice

• 102: Bob

Table 2: customers_2026

• 103: Charlie

• 102: Bob

You want to combine customers from both tables.

SELECT customer_id, name
FROM customers_2025

UNION

SELECT customer_id, name
FROM customers_2026;

Result:

• 101: Alice

• 102: Bob

• 103: Charlie

Notice that Bob appears only once.

Why?

Because UNION automatically removes duplicate rows.

🔹 2. What is UNION ALL?

UNION ALL also combines results from multiple SELECT queries.

However, it does not remove duplicates.

Example:

UNION ALL

• 101: Alice

• 102: Bob

• 103: Charlie

• 102: Bob

Bob appears twice because UNION ALL keeps duplicate rows.

🔹 3. Difference Between UNION and UNION ALL

• Removes duplicate rows: Keeps duplicate rows

• Performs duplicate elimination: Does not eliminate duplicates

• Can require more processing: Usually faster

• Useful for unique results: Useful when all records are needed

Important: Prefer UNION ALL when you need every record and don't need duplicate removal.

🔹 4. Rules of UNION

There are some important rules you must follow when using UNION.

Rule 1: Same number of columns

Both SELECT statements must return the same number of columns.

Correct:

SELECT customer_id, name
FROM customers_2025

UNION

SELECT customer_id, name
FROM customers_2026;

Incorrect:

SELECT customer_id, name
FROM customers_2025

UNION

SELECT customer_id
FROM customers_2026;

The second query has only one column.

Rule 2: Columns must have compatible data types

For example, combining an integer column with another integer column is valid.

The corresponding columns should have compatible data types.

Rule 3: Column order matters

SQL combines columns based on their position, not their names.

SELECT customer_id, name
FROM customers_2025

UNION

SELECT name, customer_id
FROM customers_2026;

This is incorrect if the corresponding data types are incompatible.

Rule 4: ORDER BY is generally placed at the end

SELECT customer_id, name
FROM customers_2025

UNION

SELECT customer_id, name
FROM customers_2026

ORDER BY customer_id;

This sorts the final combined result.

🔹 5. Using WHERE with UNION

You can filter records separately in each SELECT query.

Get customers from Pune from both tables.

SELECT customer_id, name, city
FROM customers_2025
WHERE city = 'Pune'

UNION 

SELECT customer_id, name, city
FROM customers_2026
WHERE city = 'Pune';

Each SELECT applies its own filtering condition before the results are combined.

🔹 6. UNION with Different Tables

You don't need to combine only tables with the same name or structure.

You can combine data from different tables as long as the number and types of corresponding columns are compatible.

Suppose you have:

• online_orders

• store_orders

Both contain order ID, customer ID, and order amount.

SELECT order_id, customer_id, amount
FROM online_orders

UNION ALL

SELECT order_id, customer_id, amount
FROM store_orders;

This combines online and physical store orders into one result.

🔹 7. UNION vs JOIN

This is a very important SQL interview concept.

UNION combines rows vertically.

JOIN combines columns horizontally by matching related records.

• UNION: Combine customer records from two different tables.

• JOIN: Combine customer details with their order details using customer ID.

🔹 8. Real-World Data Science Use Cases

UNION and UNION ALL are useful in many data analysis tasks.

• Combining monthly sales data from different tables.

• Merging customer records from different regions.

• Combining online and offline transactions.

• Consolidating historical and current records.

• Creating a unified dataset for reporting.

• Combining data collected from different sources.

Example: Combining sales from three regions.

SELECT product_id, sales
FROM north_sales

UNION ALL

SELECT product_id, sales
FROM south_sales

UNION ALL

SELECT product_id, sales
FROM west_sales;

This creates one combined sales dataset.

🔹 9. Common Mistakes to Avoid

• Using different numbers of columns in SELECT statements.

• Combining incompatible data types.

• Forgetting that UNION removes duplicate rows.

• Assuming UNION matches columns by their names.

• Using ORDER BY between individual SELECT statements instead of at the end.

• Using UNION when UNION ALL is sufficient, causing unnecessary duplicate elimination.

🔹 10. Interview Questions

Q1. What is UNION in SQL?

UNION combines the results of multiple SELECT statements and removes duplicate rows.

Q2. What is UNION ALL?

UNION ALL combines results while keeping duplicate rows.

Q3. Which is generally faster: UNION or UNION ALL?

UNION ALL is generally faster because it does not perform duplicate elimination.

Q4. Can UNION combine more than two SELECT statements?

Yes. You can combine multiple SELECT statements using UNION or UNION ALL.

Q5. Does UNION require the same column names?

No. It requires the same number of columns with compatible data types in corresponding positions.

🎯 Practice Questions

• Create a query to combine customer records from two tables using UNION.

• Combine two sales tables using UNION ALL.

• Combine employee records from three different regional tables.

• Find unique product IDs from two product tables using UNION.

• Combine online and offline orders while keeping duplicate records.

🎯 Key Takeaways

• UNION combines results vertically.

• UNION removes duplicate rows.

• UNION ALL keeps duplicate rows.

• Both require the same number of columns.

• Corresponding columns must have compatible data types.

• UNION and JOIN serve different purposes.

• UNION ALL is generally faster when duplicates don't need to be removed.

Double Tap ❤️ For More

𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 - 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 😍

💰 Highest Salary: ₹41 LPA
📈 Average Salary: ₹7.4 LPA
🎓 2,000+ Students Placed
🏢 500+ Hiring Partners

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-

https://pdlink.in/3SuUeuD

🔥 Take the first step towards your high-paying tech career in 2026!

𝗝.𝗣. 𝗠𝗢𝗥𝗚𝗔𝗡 𝗙𝗥𝗘𝗘 𝗩𝗜𝗥𝗧𝗨𝗔𝗟 𝗣𝗥𝗢𝗚𝗥𝗔𝗠𝗦 𝟮𝟬𝟮𝟲! 🎓

Explore J.P. Morgan's FREE Virtual Job Simulation Programs

✅ 100% FREE Virtual Learning
✅ Work on Practical, Industry-Based Tasks
✅ Learn at Your Own Pace
✅ Build Valuable Professional Skills

🎯 Perfect For:- College Students | Freshers | Graduates | Job Seekers

🔗 𝗟𝗶𝗻𝗸 👇:-

https://pdlink.in/3X8KJmE

⚡ Learn New Skills • Gain Practical Exposure • Boost Your Career!

🚀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 📊

Learn Excel, SQL, Python & Power BI Live Online Data Analytics Program

💰 Highest Salary: ₹12.6 LPA
📈 Average Salary: ₹5.7 LPA
🏢 500+ Partner Companies

𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-

https://pdlink.in/45vk5ph

🔥 Turn your interest in data into practical, job-ready skills!

🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 11: Views in SQL

Guys, today we'll learn about one of the useful SQL concepts: Views.

Views help us simplify complex queries, organize data, and create reusable SQL logic.

🔹 1. What is a View in SQL?

A View is a virtual table created using the result of a SQL query.

It does not normally store the actual data separately. Instead, it stores the SQL query definition and retrieves data from the underlying tables when accessed.

Example:

Suppose you have an employees table with thousands of records.

You frequently need only employee names, departments, and salaries.

Instead of writing the same SELECT query repeatedly, you can create a View.

CREATE VIEW employee_details AS
SELECT employee_id,
       employee_name,
       department,
       salary
FROM employees;

Now you can access it like a regular table:

SELECT *
FROM employee_details;

🔹 2. Why Do We Use Views?

Views are useful for several reasons:

• Simplify complex SQL queries.

• Reuse frequently executed queries.

• Restrict access to sensitive columns.

• Organize data for reporting.

• Provide a consistent structure for analysts.

• Make dashboards and reports easier to maintain.

🔹 3. How to Create a View

Syntax:

CREATE VIEW view_name AS
SELECT column1, column2
FROM table_name
WHERE condition;

Create a View containing employees earning more than 60,000.

CREATE VIEW high_salary_employees AS
SELECT employee_id,
       employee_name,
       salary
FROM employees
WHERE salary > 60000;

Access the View:

SELECT *
FROM high_salary_employees;

The View displays only employees matching the condition.

🔹 4. How to Update a View

If you want to change the query behind an existing View, you can use CREATE OR REPLACE VIEW in databases that support it.

CREATE OR REPLACE VIEW employee_details AS
SELECT employee_id,
       employee_name,
       department,
       salary,
       joining_date
FROM employees;

This replaces the existing View definition.

Note: SQL syntax varies by database. SQL Server commonly uses CREATE OR ALTER VIEW instead.

🔹 5. How to Delete a View

If you no longer need a View, use DROP VIEW.

DROP VIEW employee_details;

This removes the View definition.

Important: Dropping a View does not delete the original table or its records.

🔹 6. Views with JOIN

You can create Views using multiple tables and JOIN operations.

Suppose you have two tables:

• employees

• departments

Create a View combining employee and department details.

CREATE VIEW employee_department AS
SELECT e.employee_id,
       e.employee_name,
       d.department_name
FROM employees e
INNER JOIN departments d
    ON e.department_id = d.department_id;

Now you can access the combined information:

SELECT *
FROM employee_department;

This saves you from repeatedly writing the JOIN query.

🔹 7. Views with GROUP BY

You can also create Views using aggregate functions.

Create a View showing average salary by department.

CREATE VIEW department_salary AS
SELECT department,
       AVG(salary) AS average_salary,
       COUNT(*) AS employee_count
FROM employees
GROUP BY department;

Access it:

SELECT *
FROM department_salary;

This is especially useful for business reporting.

🔹 8. What is an Updatable View?

An Updatable View allows certain changes to be made through the View, such as:

• INSERT

• UPDATE

• DELETE

CREATE VIEW pune_employees AS
SELECT employee_id,
       employee_name,
       city
FROM employees
WHERE city = 'Pune';

You may be able to update records through this View:

UPDATE pune_employees
SET city = 'Mumbai'
WHERE employee_id = 101;

Important: The underlying table may also be updated.

Not all Views are updatable. Views containing GROUP BY, aggregate functions, DISTINCT, or complex JOINs often have restrictions. Exact rules depend on the database.

🔹 9. View vs Table vs Materialized View

These are important concepts for interviews.

Table

• Stores actual data.

• Data is physically stored in the database.

• Supports regular data operations.

View

• Stores a query definition.

• Normally retrieves data from underlying tables.

• Useful for simplifying queries and controlling access.

Materialized View

• Stores the result of a query physically.

• Can make repeated analytical queries faster.

• Needs refreshing to reflect changes in underlying data.

CREATE MATERIALIZED VIEW sales_summary AS
SELECT product_id,
       SUM(amount) AS total_sales
FROM sales
GROUP BY product_id;

Note: Materialized View syntax and refresh behavior depend on the database. For example, PostgreSQL supports this syntax.

🔹 10. Real-World Data Science Use Cases

Views are commonly used in data analytics and reporting.

• Creating customer summary datasets.

• Preparing data for Power BI dashboards.

• Combining multiple tables for reporting.

• Hiding sensitive employee information.

• Creating reusable sales reports.

• Simplifying complex business queries.

A company wants to create a sales summary for its reporting team.

CREATE VIEW monthly_sales AS
SELECT
    YEAR(order_date) AS sales_year,
    MONTH(order_date) AS sales_month,
    SUM(amount) AS total_sales
FROM orders
GROUP BY
    YEAR(order_date),
    MONTH(order_date);

The reporting team can use this View without rewriting the aggregation query.

🔹 11. Common Mistakes to Avoid

• Assuming a normal View stores data physically.

• Thinking DROP VIEW deletes the original table.

• Assuming every View can be updated.

• Forgetting that changes in underlying tables can affect View results.

• Creating unnecessarily complicated Views.

• Assuming Views automatically improve query performance.

🔹 12. SQL Interview Questions

Q1. What is a View in SQL?

A View is a virtual table based on the result of a SQL query.

Q2. Does a View store actual data?

A normal View generally stores the query definition, not a separate copy of the data.

Q3. Can we create a View using JOIN?

Yes. A View can combine multiple tables using JOIN.

Q4. What is a Materialized View?

A Materialized View physically stores the result of a query and can be refreshed when needed.

Q5. Can we update data through a View?

Yes, if the View satisfies the database's updatability rules.

🎯 Practice Questions

• Create a View displaying employee names and departments.

• Create a View for employees earning more than 50,000.

• Create a View combining customers and orders.

• Create a View showing total sales by product.

• Delete an existing View using DROP VIEW.

🎯 Key Takeaways

• A View is a virtual table based on a SQL query.

• CREATE VIEW creates a View.

• CREATE OR REPLACE VIEW modifies its definition in supported databases.

• DROP VIEW removes a View.

• Views can use JOIN, GROUP BY, and aggregate functions.

• Materialized Views physically store query results.

• Views are useful for reporting, analytics, and data security.

Double Tap ❤️ For More