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

🚀 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔

🎯 Choose Your Learning Track:

💻 Java Full Stack + AI Engineering
🌐 MERN Full Stack + AI Engineering

Placement Highlights: ₹41 LPA highest package | ₹7.4 LPA average package | 2,000+ students placed | 500+ hiring partners

🔗 𝗕𝗼𝗼𝗸 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 :- https://pdlink.in/4fWJVID

⚡ AI is creating new career opportunities—start building the skills companies need in 2026!

Important Excel, Tableau, Statistics, SQL related Questions with answers

1. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues

2. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

3. How do you make a dropdown list in MS Excel?

First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.

4. How do you subset or filter data in SQL?

To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.

5. What is a Gantt Chart in Tableau?

A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project

🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜! 📊

Explore these 4 Google learning programs and develop practical, career-relevant skills.

🎓 Explore the programs:
1️⃣ Google Data Analytics Professional Certificate
2️⃣ Google Business Intelligence Professional Certificate
3️⃣ Google AI Essentials
4️⃣ Google Advanced Data Analytics Professional Certificate

🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-

https://pdlink.in/4htgIEW

📌 Save this post and share it with someone interested in Data Analytics or AI!

𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!
​
Looking to learn practical, in-demand skills? These courses cover Generative AI, Cybersecurity, AI tools and Digital Marketing.

💫 Learn at your own pace
⚡Build career-relevant skills
🔥Practical learning opportunities

𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 :-

https://pdlink.in/4z3vOYU

Save this post and share with your friends

Complete Data Analytics Mastery: From Basics to Advanced 🚀

Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials

Grasp these essentials in just a week to build a solid foundation in data analytics.

Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics

Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.

Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics

These advanced concepts can be mastered in a couple of weeks with focused study and practice.

Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets

Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.

Best platforms to learn:
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Interview Questions
- Free SQL Resources

Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩‍💻👨‍💻

Join @free4unow_backup for more free resources.

Like this post if it helps 😄❤️

ENJOY LEARNING 👍👍

🎓 𝗛𝗔𝗥𝗩𝗔𝗥𝗗 𝗨𝗡𝗜𝗩𝗘𝗥𝗦𝗜𝗧𝗬 𝗙𝗥𝗘𝗘 𝗢𝗡𝗟𝗜𝗡𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 😍

Dreaming of learning from one of the world’s most prestigious universities? Explore Harvard’s online courses and build valuable, career-ready skills from home!

💡 Beginner-friendly options
⏰ Learn at your own pace
🌍 Accessible online worldwide
🎯 Ideal for students, freshers and working professionals

🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇

https://pdlink.in/4xPUdzU

📢 Share this valuable opportunity with your friends and classmates!

🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 3 — ORDER BY

ORDER BY is used to sort the rows returned by a SQL query. It helps you arrange data in a meaningful order, such as:

• Highest salary to lowest salary

• Lowest price to highest price

• Newest date to oldest date

• A–Z or Z–A

• Highest sales to lowest sales

🔹 Basic Syntax

SELECT column1, column2
FROM table_name
ORDER BY column_name;

By default, SQL sorts in ascending order.

SELECT *
FROM employees
ORDER BY salary;

This generally returns employees from the lowest salary to the highest salary.

🔹 ASC — Ascending Order

ASC means ascending.

SELECT *
FROM employees
ORDER BY salary ASC;

For numbers: 10000, 25000, 40000, 75000, 100000

For text: Amazon, Apple, Google, Microsoft

For dates: 2024-01-01, 2024-05-15, 2024-12-20, 2025-03-10

ASC is usually the default, so ORDER BY salary; is equivalent to ORDER BY salary ASC;

🔹 DESC — Descending Order

DESC means descending.

SELECT *
FROM employees
ORDER BY salary DESC;

Example: 100000, 75000, 40000, 25000, 10000

This is especially useful when you want to find:

• Highest-paid employees

• Top-selling products

• Most expensive products

• Most recent transactions

• Highest-performing regions

🔹 Sorting Text

You can sort text columns alphabetically.

SELECT employee_name, department
FROM employees
ORDER BY employee_name ASC;

SELECT employee_name, department
FROM employees
ORDER BY employee_name DESC;

🔹 Sorting Dates

To find the most recent transactions:

SELECT transaction_id, transaction_date, amount
FROM transactions
ORDER BY transaction_date DESC;

To see transactions from oldest to newest:

SELECT transaction_id, transaction_date, amount
FROM transactions
ORDER BY transaction_date ASC;

🔹 Sorting by Multiple Columns

This is very important. Suppose you want to sort employees:

1. By department

2. Within each department, by salary from highest to lowest

SELECT employee_name, department, salary
FROM employees
ORDER BY department ASC, salary DESC;

SQL first sorts by department. When multiple rows have the same department, it then uses salary to determine their order.

Example:

Finance 90000

Finance 70000

Finance 50000

HR 85000

HR 60000

IT 120000

IT 95000

🔹 ORDER BY with Calculations

You can also sort using an expression.

SELECT product_name, quantity, price,
       quantity * price AS total_value
FROM products
ORDER BY quantity * price DESC;

You can also use the alias in many SQL databases:

SELECT product_name,
       quantity * price AS total_value
FROM products
ORDER BY total_value DESC;

🔹 ORDER BY with DISTINCT

SELECT DISTINCT department
FROM employees
ORDER BY department ASC;

This returns each department once and sorts them alphabetically.

🔹 ORDER BY and NULL Values

NULL represents a missing or unknown value. The position of NULL values when using ORDER BY can vary between SQL databases.

Some databases also support explicit control such as:

ORDER BY salary ASC NULLS LAST;

or

ORDER BY salary DESC NULLS FIRST;

Always check the syntax supported by your SQL database.

🔹 ORDER BY with WHERE

WHERE filters the rows first, and ORDER BY sorts the resulting rows.

SELECT employee_name, salary
FROM employees
WHERE department = 'IT'
ORDER BY salary DESC;

🔹 Real-World Data Science Example

Suppose you are analyzing sales data. You want to identify the highest-value transactions.

SELECT customer_id, product_id, quantity, price,
       quantity * price AS transaction_value
FROM sales
ORDER BY transaction_value DESC;

This can help with:

• Finding high-value transactions

• Identifying important customers

• Investigating unusually large purchases

• Preparing data for further analysis

🔹 Common Mistakes

Mistake 1 — Forgetting DESC

If you want the highest salary first: ORDER BY salary DESC; Not ORDER BY salary;

Mistake 2 — Wrong column

Make sure the column used for sorting actually represents what you want to analyze.

Mistake 3 — Incorrect multiple-column ordering

ORDER BY department, salary DESC; does NOT mean both columns are descending. It means: department → ASC, salary → DESC

If you want both descending: ORDER BY department DESC, salary DESC;

Mistake 4 — Confusing WHERE and ORDER BY

WHERE filters rows. ORDER BY sorts rows. They perform different jobs.

🔹 Interview Questions

1. What is ORDER BY?

ORDER BY sorts query results based on one or more columns.

2. What is the default sorting order?

Ascending order (ASC) is generally the default.

3. How do you find the highest salary?

SELECT * FROM employees ORDER BY salary DESC;

4. Can you sort using multiple columns?

Yes. ORDER BY department ASC, salary DESC;

5. What is the difference between ASC and DESC?

ASC sorts from low to high or A–Z. DESC sorts from high to low or Z–A.

🎯 Practice Questions

1. Write a query to display all products from the cheapest to the most expensive.

2. Write a query to display employees from the highest salary to the lowest salary.

3. Write a query to display customers alphabetically by name.

4. Write a query to sort sales by date, showing the newest sales first.

5. Write a query to sort employees by department alphabetically and salary from highest to lowest within each department.

🎯 Key Takeaways

• ORDER BY sorts query results

• ASC means ascending

• DESC means descending

• Ascending is generally the default

• You can sort numbers, text and dates

• You can sort using multiple columns

• WHERE filters data; ORDER BY sorts data

• ORDER BY is especially useful when analyzing rankings and top/bottom records

👉 Double Tap ❤️ For More

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟲🚀
​
Explore 6 free resources covering AI fundamentals, tools, deep learning, research and real-world applications.

✅ 100% Free Learning
✅ Beginner-Friendly
✅ AI • ML • Deep Learning
✅ Real-World Applications

🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇

https://pdlink.in/4AFHq5R

📢 Share this valuable opportunity with your friends and classmates!

Data Visualisation tips for beginners

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘! 🔥

Learn Power BI through these FREE learning resources
​
✨ What You'll Learn:
📊 Interactive Dashboards
📈 Data Visualization
🧹 Data Transformation
💼 Real-World Reporting Skills
🎯 Beginner-Friendly — No Coding Required

𝗦𝘁𝗮𝗿𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘
​
​https://pdlink.in/4hznwlu

💫Perfect for Students • Freshers • Data Analyst Aspirants • Working Professionals

🚀𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 | 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗪𝗜𝘁𝗵 𝗚𝗲𝗻𝗔𝗜

Start a high-paying tech career—even without prior coding experience

🏆 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
💰 ₹41 LPA highest salary
📈 ₹7.4 LPA average salary
🎓 2,000+ students placed
🏢 500+ hiring partners
✅ 100% job assistance
📜 Skill India–authenticated certificate

🔗 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇:-

https://pdlink.in/3SuUeuD

🎯 HurryUp.....Limited Seats Available

🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 4 — LIMIT

LIMIT is used to control how many rows a SQL query returns.

It is especially useful when you want to see only the first few records, such as:

• Top 5 highest salaries

• Top 10 products by sales

• First 20 customers

• Latest 10 transactions

🔹 Basic Syntax

SELECT *
FROM employees
LIMIT 5;

This returns only 5 rows from the result.

🔹 LIMIT with ORDER BY

This is where LIMIT becomes especially powerful.

Suppose you want the 5 highest-paid employees:

SELECT employee_name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

The logical idea is:

Filter → Sort → Take required number of rows

For example:

Salary
120000
110000
95000
90000
85000

Only the top 5 rows are returned.

🔹 LIMIT Without ORDER BY

You can write:

SELECT *
FROM employees
LIMIT 5;

But without ORDER BY, you should not assume these are the "first" or "top" 5 meaningful records.

The database does not guarantee a particular order unless you explicitly specify one.

So for analysis, this is usually better:

SELECT *
FROM employees
ORDER BY salary DESC
LIMIT 5;

🔹 LIMIT with WHERE

You can combine WHERE, ORDER BY, and LIMIT.

Example:

SELECT employee_name, salary
FROM employees
WHERE department = 'IT'
ORDER BY salary DESC
LIMIT 3;

This finds the 3 highest-paid employees in the IT department.

🔹 LIMIT for Top-N Analysis

Top-N queries are extremely common in data analysis.

Top 10 products by sales

SELECT product_name, sales
FROM products
ORDER BY sales DESC
LIMIT 10;

5 most recent transactions

SELECT transaction_id, transaction_date, amount
FROM transactions
ORDER BY transaction_date DESC
LIMIT 5;

3 cheapest products

SELECT product_name, price
FROM products
ORDER BY price ASC
LIMIT 3;

🔹 LIMIT with OFFSET

OFFSET allows you to skip a certain number of rows before returning results.

Example:

SELECT *
FROM employees
LIMIT 5 OFFSET 10;

This means:

Skip 10 rows

↓

Return the next 5 rows

This is commonly used for pagination.

For example:

• Page 1 → LIMIT 10 OFFSET 0

• Page 2 → LIMIT 10 OFFSET 10

• Page 3 → LIMIT 10 OFFSET 20

Syntax can vary between SQL databases, so always check the specific database you are using.

🔹 LIMIT vs TOP vs FETCH

Different SQL databases may use different syntax.

MySQL/PostgreSQL/SQLite commonly use:

LIMIT 5;

SQL Server commonly uses:

SELECT TOP 5 *
FROM employees;

Standard SQL also supports:

FETCH FIRST 5 ROWS ONLY;

The concept is the same: restrict the number of returned rows.

🔹 Real-World Data Science Example

Suppose an analyst wants to investigate the 10 largest customer transactions.

SELECT customer_id,
       transaction_id,
       amount
FROM transactions
ORDER BY amount DESC
LIMIT 10;

This can help identify:

• High-value transactions

• Potential anomalies

• Important customers

• Transactions requiring further investigation

🔹 Common Mistakes

Mistake 1 — Using LIMIT without sorting

SELECT *
FROM products
LIMIT 10;

This does not necessarily mean "10 best products."

For that:

ORDER BY sales DESC
LIMIT 10;

Mistake 2 — Using ASC when you need the highest values

Wrong:

ORDER BY salary ASC
LIMIT 5;

This returns the lowest salaries.

For the highest salaries: