✅ SQL JOINS 🔗📘
JOINS let you combine data from two or more tables based on related columns.
1️⃣ INNER JOIN
Returns only matching rows from both tables.
Problem: Get customers with their orders.
✅ Only shows customers who have orders.
2️⃣ LEFT JOIN (or LEFT OUTER JOIN)
Returns all rows from the left table + matching rows from the right table. If no match, fills with NULL.
Problem: Show all customers, even if they didn’t order.
✅ Includes customers without orders.
3️⃣ RIGHT JOIN
Opposite of LEFT JOIN: keeps all rows from the right table.
4️⃣ FULL OUTER JOIN
Returns all rows from both tables. Where there’s no match, it shows NULL.
✅ Includes customers with or without orders and orders with or without customers.
5️⃣ SELF JOIN
Table joins with itself.
Problem: Show employees and their managers.
✅ Links each employee to their manager using a self join.
💡 Quick Summary:
• INNER JOIN → Only matches
• LEFT JOIN → All from left + matches
• RIGHT JOIN → All from right + matches
• FULL OUTER JOIN → Everything
• SELF JOIN → Table joins itself
💬 Tap ❤️ for more!
JOINS let you combine data from two or more tables based on related columns.
1️⃣ INNER JOIN
Returns only matching rows from both tables.
Problem: Get customers with their orders.
SELECT c.name, o.order_id
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id;
✅ Only shows customers who have orders.
2️⃣ LEFT JOIN (or LEFT OUTER JOIN)
Returns all rows from the left table + matching rows from the right table. If no match, fills with NULL.
Problem: Show all customers, even if they didn’t order.
SELECT c.name, o.order_id
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id;
✅ Includes customers without orders.
3️⃣ RIGHT JOIN
Opposite of LEFT JOIN: keeps all rows from the right table.
4️⃣ FULL OUTER JOIN
Returns all rows from both tables. Where there’s no match, it shows NULL.
SELECT c.name, o.order_id
FROM customers c
FULL OUTER JOIN orders o ON c.id = o.customer_id;
✅ Includes customers with or without orders and orders with or without customers.
5️⃣ SELF JOIN
Table joins with itself.
Problem: Show employees and their managers.
SELECT e.name AS employee, m.name AS manager
FROM employees e
JOIN employees m ON e.manager_id = m.id;
✅ Links each employee to their manager using a self join.
💡 Quick Summary:
• INNER JOIN → Only matches
• LEFT JOIN → All from left + matches
• RIGHT JOIN → All from right + matches
• FULL OUTER JOIN → Everything
• SELF JOIN → Table joins itself
💬 Tap ❤️ for more!
❤6
✅ SQL Subqueries & Nested Queries 🧠🔍
Subqueries help you write powerful queries inside other queries. They're useful when you need intermediate results.
1️⃣ What is a Subquery?
A subquery is a query inside
Example: Get employees who earn above average salary.
2️⃣ Subquery in SELECT Clause
You can use subqueries to return values in each row.
Example: Show employee names with department name.
Use when you want to filter or group temporary results.
Example: Get department-wise highest salary.
A subquery that uses a value from the outer query row.
Example: Get employees with highest salary in their department.
💡 Real Use Cases:
• Filter rows based on dynamic conditions
• Compare values across groups
• Fetch related info in SELECT
🎯 Practice Tasks:
• Write a query to find 2nd highest salary
• Use subquery to get customers who placed more than 3 orders
• Create a nested query to show top-selling product per category
✅ Solution for Practice Tasks 👇
1️⃣ Find 2nd Highest Salary
2️⃣ Customers Who Placed More Than 3 Orders
You can join to get customer names:
💬 Tap ❤️ for more!
Subqueries help you write powerful queries inside other queries. They're useful when you need intermediate results.
1️⃣ What is a Subquery?
A subquery is a query inside
() that runs first and passes its result to the outer query.Example: Get employees who earn above average salary.
SELECT name, salary✅ Subquery calculates average salary → main query finds those above it.
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
2️⃣ Subquery in SELECT Clause
You can use subqueries to return values in each row.
Example: Show employee names with department name.
SELECT name,3️⃣ Subquery in FROM Clause
(SELECT dept_name FROM departments d WHERE d.id = e.dept_id) AS department
FROM employees e;
Use when you want to filter or group temporary results.
Example: Get department-wise highest salary.
SELECT dept_id, MAX(salary)4️⃣ Correlated Subquery
FROM (SELECT * FROM employees WHERE active = 1) AS active_emps
GROUP BY dept_id;
A subquery that uses a value from the outer query row.
Example: Get employees with highest salary in their department.
SELECT name, salary✅ Subquery runs for each row using outer query value.
FROM employees e
WHERE salary = (SELECT MAX(salary) FROM employees WHERE dept_id = e.dept_id);
💡 Real Use Cases:
• Filter rows based on dynamic conditions
• Compare values across groups
• Fetch related info in SELECT
🎯 Practice Tasks:
• Write a query to find 2nd highest salary
• Use subquery to get customers who placed more than 3 orders
• Create a nested query to show top-selling product per category
✅ Solution for Practice Tasks 👇
1️⃣ Find 2nd Highest Salary
SELECT MAX(salary) AS second_highest_salary▶️ Finds the highest salary less than the max salary → gives the 2nd highest.
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
2️⃣ Customers Who Placed More Than 3 Orders
SELECT customer_id▶️ Groups orders by customer and filters those with more than 3.
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 3;
You can join to get customer names:
SELECT name3️⃣ Top-Selling Product Per Category
FROM customers
WHERE id IN (
SELECT customer_id
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 3
);
SELECT p.name, p.category_id, p.sales▶️ Correlated subquery finds the highest sales within each category.
FROM products p
WHERE p.sales = (
SELECT MAX(sales)
FROM products
WHERE category_id = p.category_id
);
💬 Tap ❤️ for more!
❤5🎉1
✅ SQL CASE Statement 🎯
The CASE statement lets you apply conditional logic inside SQL queries — like if/else in programming.
1️⃣ Basic CASE Syntax
✅ Categorizes salaries as High, Medium, or Low.
2️⃣ CASE in ORDER BY
Sort based on custom logic.
✅ HR shows up first, then Engineering, then others.
3️⃣ CASE in WHERE Clause
Control filtering logic conditionally.
4️⃣ Nested CASE (Advanced)
🎯 Use CASE When You Want To:
• Create labels or buckets
• Replace multiple IF conditions
• Make results more readable
📝 Practice Tasks:
1. Add a column that shows ‘Pass’ or ‘Fail’ based on marks
2. Create a salary band (Low/Medium/High) using CASE
3. Use CASE to sort products as 'Electronics' first, then 'Clothing'
💬 Tap ❤️ for more!
The CASE statement lets you apply conditional logic inside SQL queries — like if/else in programming.
1️⃣ Basic CASE Syntax
SELECT name, salary,
CASE
WHEN salary > 80000 THEN 'High'
WHEN salary BETWEEN 50000 AND 80000 THEN 'Medium'
ELSE 'Low'
END AS salary_level
FROM employees;
✅ Categorizes salaries as High, Medium, or Low.
2️⃣ CASE in ORDER BY
Sort based on custom logic.
SELECT name, department
FROM employees
ORDER BY
CASE department
WHEN 'HR' THEN 1
WHEN 'Engineering' THEN 2
ELSE 3
END;
✅ HR shows up first, then Engineering, then others.
3️⃣ CASE in WHERE Clause
Control filtering logic conditionally.
SELECT *
FROM orders
WHERE status =
CASE
WHEN customer_type = 'VIP' THEN 'priority'
ELSE 'standard'
END;
4️⃣ Nested CASE (Advanced)
SELECT name, marks,
CASE
WHEN marks >= 90 THEN 'A'
WHEN marks >= 75 THEN
CASE WHEN marks >= 85 THEN 'B+' ELSE 'B' END
ELSE 'C'
END AS grade
FROM students;
🎯 Use CASE When You Want To:
• Create labels or buckets
• Replace multiple IF conditions
• Make results more readable
📝 Practice Tasks:
1. Add a column that shows ‘Pass’ or ‘Fail’ based on marks
2. Create a salary band (Low/Medium/High) using CASE
3. Use CASE to sort products as 'Electronics' first, then 'Clothing'
💬 Tap ❤️ for more!
❤4
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❤3
✅ SQL Programming: Handling NULL Values 🛠️
Missing data is common in databases. COALESCE() helps you fill in defaults and avoid null-related issues.
1️⃣ What is COALESCE?
Returns the first non-null value in a list.
✅ If phone is NULL, it shows ‘Not Provided’.
2️⃣ COALESCE with Calculations
Prevent nulls from breaking math.
✅ If bonus is NULL, treat it as 0 to compute total.
3️⃣ Nested COALESCE
Use multiple fallback options.
✅ Checks email, then alt_email, then default text.
4️⃣ COALESCE in WHERE clause
Filter even when data has nulls.
🎯 Use COALESCE When You Want To:
• Replace NULLs with defaults
• Keep math & filters working
• Avoid errors in reports or dashboards
📝 Practice Tasks:
1. Replace nulls in city with ‘Unknown’
2. Show total amount = price + tax (tax may be null)
3. Replace nulls in denoscription with ‘No Info Available’
✅ Solution for Practice Tasks 👇
1️⃣ Replace NULLs in city with 'Unknown'
2️⃣ Show total amount = price + tax (tax may be NULL)
3️⃣ Replace NULLs in denoscription with 'No Info Available'
💬 Tap ❤️ for more!
Missing data is common in databases. COALESCE() helps you fill in defaults and avoid null-related issues.
1️⃣ What is COALESCE?
Returns the first non-null value in a list.
SELECT name, COALESCE(phone, 'Not Provided') AS contact
FROM customers;
✅ If phone is NULL, it shows ‘Not Provided’.
2️⃣ COALESCE with Calculations
Prevent nulls from breaking math.
SELECT name, salary, COALESCE(bonus, 0) AS bonus,
salary + COALESCE(bonus, 0) AS total_income
FROM employees;
✅ If bonus is NULL, treat it as 0 to compute total.
3️⃣ Nested COALESCE
Use multiple fallback options.
SELECT name, COALESCE(email, alt_email, 'No Email') AS contact_email
FROM users;
✅ Checks email, then alt_email, then default text.
4️⃣ COALESCE in WHERE clause
Filter even when data has nulls.
SELECT *
FROM products
WHERE COALESCE(category, 'Uncategorized') = 'Electronics';
🎯 Use COALESCE When You Want To:
• Replace NULLs with defaults
• Keep math & filters working
• Avoid errors in reports or dashboards
📝 Practice Tasks:
1. Replace nulls in city with ‘Unknown’
2. Show total amount = price + tax (tax may be null)
3. Replace nulls in denoscription with ‘No Info Available’
✅ Solution for Practice Tasks 👇
1️⃣ Replace NULLs in city with 'Unknown'
SELECT name, COALESCE(city, 'Unknown') AS city
FROM customers;
2️⃣ Show total amount = price + tax (tax may be NULL)
SELECT product_name, price, COALESCE(tax, 0) AS tax,
price + COALESCE(tax, 0) AS total_amount
FROM products;
3️⃣ Replace NULLs in denoscription with 'No Info Available'
SELECT product_name, COALESCE(denoscription, 'No Info Available') AS denoscription
FROM products;
💬 Tap ❤️ for more!
❤4
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Learn from IIT faculty and industry experts.
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❤2
✅ SQL Window Functions 🧠🪟
Window functions perform calculations across rows that are related to the current row — without collapsing the result like GROUP BY.
1️⃣ ROW_NUMBER() – Assigns a unique row number per partition
➤ Gives ranking within each department
2️⃣ RANK() & DENSE_RANK() – Ranking with gaps (RANK) or without gaps (DENSE_RANK)
3️⃣ LAG() & LEAD() – Access previous or next row value
➤ Compare salary trends row-wise
4️⃣ SUM(), AVG(), COUNT() OVER() – Running totals, moving averages, etc.
5️⃣ NTILE(n) – Divides rows into n equal buckets
💡 Why Use Window Functions:
• Perform row-wise calculations
• Avoid GROUP BY limitations
• Enable advanced analytics (ranking, trends, etc.)
🧪 Practice Task:
Write a query to find the top 2 earners in each department using ROW_NUMBER().
💬 Tap ❤️ for more!
Window functions perform calculations across rows that are related to the current row — without collapsing the result like GROUP BY.
1️⃣ ROW_NUMBER() – Assigns a unique row number per partition
SELECT name, department,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;
➤ Gives ranking within each department
2️⃣ RANK() & DENSE_RANK() – Ranking with gaps (RANK) or without gaps (DENSE_RANK)
SELECT name, salary,
RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;
3️⃣ LAG() & LEAD() – Access previous or next row value
SELECT name, salary,
LAG(salary) OVER (ORDER BY salary) AS prev_salary,
LEAD(salary) OVER (ORDER BY salary) AS next_salary
FROM employees;
➤ Compare salary trends row-wise
4️⃣ SUM(), AVG(), COUNT() OVER() – Running totals, moving averages, etc.
SELECT department, salary,
SUM(salary) OVER (PARTITION BY department) AS dept_total
FROM employees;
5️⃣ NTILE(n) – Divides rows into n equal buckets
SELECT name, salary,
NTILE(4) OVER (ORDER BY salary DESC) AS quartile
FROM employees;
💡 Why Use Window Functions:
• Perform row-wise calculations
• Avoid GROUP BY limitations
• Enable advanced analytics (ranking, trends, etc.)
🧪 Practice Task:
Write a query to find the top 2 earners in each department using ROW_NUMBER().
💬 Tap ❤️ for more!
❤6
✅ SQL Real-World Use Cases 💼🧠
SQL is the backbone of data analysis and automation in many domains. Here’s how it powers real work:
1️⃣ Sales & CRM
Use Case: Sales Tracking & Pipeline Management
• Track sales per region, product, rep
• Identify top-performing leads
• Calculate conversion rates
SQL Task:
2️⃣ Finance
Use Case: Monthly Revenue and Expense Reporting
• Aggregate revenue by month
• Analyze profit margins
• Flag unusual transactions
SQL Task:
3️⃣ HR Analytics
Use Case: Employee Attrition Analysis
• Track tenure, exits, departments
• Calculate average retention
• Segment by age, role, or location
SQL Task:
4️⃣ E-commerce
Use Case: Customer Order Behavior
• Find most ordered products
• Time between repeat orders
• Cart abandonment patterns
SQL Task:
5️⃣ Healthcare
Use Case: Patient Visit Frequency
• Find frequent visitors
• Analyze doctor performance
• Calculate average stay duration
SQL Task:
6️⃣ Marketing
Use Case: Campaign Performance by Channel
• Track leads, clicks, conversions
• Compare cost-per-lead by platform
SQL Task:
🧪 Practice Task:
Pick a dataset (orders, users, sales)
→ Write 3 queries: summary, trend, filter
→ Visualize the output in Excel or Power BI
💬 Tap ❤️ for more!
SQL is the backbone of data analysis and automation in many domains. Here’s how it powers real work:
1️⃣ Sales & CRM
Use Case: Sales Tracking & Pipeline Management
• Track sales per region, product, rep
• Identify top-performing leads
• Calculate conversion rates
SQL Task:
SELECT region, SUM(sales_amount)
FROM deals
GROUP BY region;
2️⃣ Finance
Use Case: Monthly Revenue and Expense Reporting
• Aggregate revenue by month
• Analyze profit margins
• Flag unusual transactions
SQL Task:
SELECT MONTH(date), SUM(revenue - expense) AS profit
FROM finance_data
GROUP BY MONTH(date);
3️⃣ HR Analytics
Use Case: Employee Attrition Analysis
• Track tenure, exits, departments
• Calculate average retention
• Segment by age, role, or location
SQL Task:
SELECT department, COUNT(*)
FROM employees
WHERE exit_date IS NOT NULL
GROUP BY department;
4️⃣ E-commerce
Use Case: Customer Order Behavior
• Find most ordered products
• Time between repeat orders
• Cart abandonment patterns
SQL Task:
SELECT customer_id, COUNT(order_id)
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 5;
5️⃣ Healthcare
Use Case: Patient Visit Frequency
• Find frequent visitors
• Analyze doctor performance
• Calculate average stay duration
SQL Task:
SELECT patient_id, COUNT(*) AS visits
FROM appointments
GROUP BY patient_id;
6️⃣ Marketing
Use Case: Campaign Performance by Channel
• Track leads, clicks, conversions
• Compare cost-per-lead by platform
SQL Task:
SELECT channel, SUM(conversions)/SUM(clicks) AS conv_rate
FROM campaign_data
GROUP BY channel;
🧪 Practice Task:
Pick a dataset (orders, users, sales)
→ Write 3 queries: summary, trend, filter
→ Visualize the output in Excel or Power BI
💬 Tap ❤️ for more!
❤4
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✅ Useful Platform to Practice SQL Programming 🧠🖥️
Learning SQL is just the first step — practice is what builds real skill. Here are the best platforms for hands-on SQL:
1️⃣ LeetCode – For Interview-Oriented SQL Practice
• Focus: Real interview-style problems
• Levels: Easy to Hard
• Schema + Sample Data Provided
• Great for: Data Analyst, Data Engineer, FAANG roles
✔ Tip: Start with Easy → filter by “Database” tag
✔ Popular Section: Database → Top 50 SQL Questions
Example Problem: “Find duplicate emails in a user table” → Practice filtering, GROUP BY, HAVING
2️⃣ HackerRank – Structured & Beginner-Friendly
• Focus: Step-by-step SQL track
• Has certification tests (SQL Basic, Intermediate)
• Problem sets by topic: SELECT, JOINs, Aggregations, etc.
✔ Tip: Follow the full SQL track
✔ Bonus: Company-specific challenges
Try: “Revising Aggregations – The Count Function” → Build confidence with small wins
3️⃣ Mode Analytics – Real-World SQL in Business Context
• Focus: Business intelligence + SQL
• Uses real-world datasets (e.g., e-commerce, finance)
• Has an in-browser SQL editor with live data
✔ Best for: Practicing dashboard-level queries
✔ Tip: Try the SQL case studies & tutorials
4️⃣ StrataScratch – Interview Questions from Real Companies
• 500+ problems from companies like Uber, Netflix, Google
• Split by company, difficulty, and topic
✔ Best for: Intermediate to advanced level
✔ Tip: Try “Hard” questions after doing 30–50 easy/medium
5️⃣ DataLemur – Short, Practical SQL Problems
• Crisp and to the point
• Good UI, fast learning
• Real interview-style logic
✔ Use when: You want fast, smart SQL drills
📌 How to Practice Effectively:
• Spend 20–30 mins/day
• Focus on JOINs, GROUP BY, HAVING, Subqueries
• Analyze problem → write → debug → re-write
• After solving, explain your logic out loud
🧪 Practice Task:
Try solving 5 SQL questions from LeetCode or HackerRank this week. Start with SELECT, WHERE, and GROUP BY.
💬 Tap ❤️ for more!
Learning SQL is just the first step — practice is what builds real skill. Here are the best platforms for hands-on SQL:
1️⃣ LeetCode – For Interview-Oriented SQL Practice
• Focus: Real interview-style problems
• Levels: Easy to Hard
• Schema + Sample Data Provided
• Great for: Data Analyst, Data Engineer, FAANG roles
✔ Tip: Start with Easy → filter by “Database” tag
✔ Popular Section: Database → Top 50 SQL Questions
Example Problem: “Find duplicate emails in a user table” → Practice filtering, GROUP BY, HAVING
2️⃣ HackerRank – Structured & Beginner-Friendly
• Focus: Step-by-step SQL track
• Has certification tests (SQL Basic, Intermediate)
• Problem sets by topic: SELECT, JOINs, Aggregations, etc.
✔ Tip: Follow the full SQL track
✔ Bonus: Company-specific challenges
Try: “Revising Aggregations – The Count Function” → Build confidence with small wins
3️⃣ Mode Analytics – Real-World SQL in Business Context
• Focus: Business intelligence + SQL
• Uses real-world datasets (e.g., e-commerce, finance)
• Has an in-browser SQL editor with live data
✔ Best for: Practicing dashboard-level queries
✔ Tip: Try the SQL case studies & tutorials
4️⃣ StrataScratch – Interview Questions from Real Companies
• 500+ problems from companies like Uber, Netflix, Google
• Split by company, difficulty, and topic
✔ Best for: Intermediate to advanced level
✔ Tip: Try “Hard” questions after doing 30–50 easy/medium
5️⃣ DataLemur – Short, Practical SQL Problems
• Crisp and to the point
• Good UI, fast learning
• Real interview-style logic
✔ Use when: You want fast, smart SQL drills
📌 How to Practice Effectively:
• Spend 20–30 mins/day
• Focus on JOINs, GROUP BY, HAVING, Subqueries
• Analyze problem → write → debug → re-write
• After solving, explain your logic out loud
🧪 Practice Task:
Try solving 5 SQL questions from LeetCode or HackerRank this week. Start with SELECT, WHERE, and GROUP BY.
💬 Tap ❤️ for more!
❤7
✅ Data Analytics Roadmap for Freshers in 2025 🚀📊
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
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✅ How to Build a Job-Ready Data Analytics Portfolio 💼📊
1️⃣ Pick Solid Datasets
• Public: Kaggle, UCI ML Repo, data.gov
• Business-like: e-commerce, churn, marketing spend, HR attrition
• Size: 5k–200k rows, relatively clean
2️⃣ Create 3 Signature Projects
• SQL: Customer Cohort & Retention (joins, window functions)
• BI: Executive Sales Dashboard (Power BI/Tableau, drill-through, DAX/calculated fields)
• Python: Marketing ROI & Attribution (pandas, seaborn, A/B test basics)
3️⃣ Tell a Story, Not Just Charts
• Problem → Approach → Insight → Action
• Add one business recommendation per insight
4️⃣ Document Like a Pro
• README: problem, data source, methods, results, next steps
• Screenshots or GIFs of dashboards
• Repo structure: /data, /notebooks, /sql, /reports
5️⃣ Show Measurable Impact
• “Reduced reporting time by 70% with automated Power BI pipeline”
• “Identified 12% churn segment with a retention playbook”
6️⃣ Make It Easy to Review
• Share live dashboards (Publish to Web), short Loom/YouTube walkthrough
• Include SQL snippets
• Pin top 3 projects on GitHub and LinkedIn Featured
7️⃣ Iterate With Feedback
• Post drafts on LinkedIn, ask “What would you improve?”
• Apply suggestions, track updates in a CHANGELOG
🎯 Goal: 3 projects, 3 stories, 3 measurable outcomes.
💬 Double Tap ❤️ For More!
1️⃣ Pick Solid Datasets
• Public: Kaggle, UCI ML Repo, data.gov
• Business-like: e-commerce, churn, marketing spend, HR attrition
• Size: 5k–200k rows, relatively clean
2️⃣ Create 3 Signature Projects
• SQL: Customer Cohort & Retention (joins, window functions)
• BI: Executive Sales Dashboard (Power BI/Tableau, drill-through, DAX/calculated fields)
• Python: Marketing ROI & Attribution (pandas, seaborn, A/B test basics)
3️⃣ Tell a Story, Not Just Charts
• Problem → Approach → Insight → Action
• Add one business recommendation per insight
4️⃣ Document Like a Pro
• README: problem, data source, methods, results, next steps
• Screenshots or GIFs of dashboards
• Repo structure: /data, /notebooks, /sql, /reports
5️⃣ Show Measurable Impact
• “Reduced reporting time by 70% with automated Power BI pipeline”
• “Identified 12% churn segment with a retention playbook”
6️⃣ Make It Easy to Review
• Share live dashboards (Publish to Web), short Loom/YouTube walkthrough
• Include SQL snippets
• Pin top 3 projects on GitHub and LinkedIn Featured
7️⃣ Iterate With Feedback
• Post drafts on LinkedIn, ask “What would you improve?”
• Apply suggestions, track updates in a CHANGELOG
🎯 Goal: 3 projects, 3 stories, 3 measurable outcomes.
💬 Double Tap ❤️ For More!
❤3
Core SQL Interview Questions. With answers
1 What is SQL
• SQL stands for Structured Query Language
• You use it to read and manage data in relational databases
• Used in MySQL, PostgreSQL, SQL Server, Oracle
2 What is an RDBMS
• Relational Database Management System
• Stores data in tables with rows and columns
• Uses keys to link tables
• Example. Customer table linked to Orders table using customer_id
3 What is a table
• Structured storage for data
• Rows are records
• Columns are attributes
• Example. One row equals one customer
4 What is a primary key
• Uniquely identifies each row
• Cannot be NULL
• No duplicate values
• Example. user_id in users table
5 What is a foreign key
• Links one table to another
• Refers to a primary key in another table
• Allows duplicate values
• Example. user_id in orders table
6 Difference between primary key and foreign key
• Primary key ensures uniqueness
• Foreign key ensures relationship
• One table can have one primary key
• One table can have multiple foreign keys
7 What is NULL
• Represents missing or unknown value
• Not equal to zero or empty string
• Use IS NULL or IS NOT NULL to check
8 What are constraints
• Rules applied on columns
• Maintain data quality
• Common constraints
– NOT NULL
– UNIQUE
– PRIMARY KEY
– FOREIGN KEY
– CHECK
9 What are data types
• Define type of data stored
• Common types
– INT for numbers
– VARCHAR for text
– DATE for dates
– FLOAT or DECIMAL for decimals
10 Interview tip you must remember
• Always explain with a small example
• Speak logic before syntax
• Keep answers short and direct
Double Tap ❤️ For More
1 What is SQL
• SQL stands for Structured Query Language
• You use it to read and manage data in relational databases
• Used in MySQL, PostgreSQL, SQL Server, Oracle
2 What is an RDBMS
• Relational Database Management System
• Stores data in tables with rows and columns
• Uses keys to link tables
• Example. Customer table linked to Orders table using customer_id
3 What is a table
• Structured storage for data
• Rows are records
• Columns are attributes
• Example. One row equals one customer
4 What is a primary key
• Uniquely identifies each row
• Cannot be NULL
• No duplicate values
• Example. user_id in users table
5 What is a foreign key
• Links one table to another
• Refers to a primary key in another table
• Allows duplicate values
• Example. user_id in orders table
6 Difference between primary key and foreign key
• Primary key ensures uniqueness
• Foreign key ensures relationship
• One table can have one primary key
• One table can have multiple foreign keys
7 What is NULL
• Represents missing or unknown value
• Not equal to zero or empty string
• Use IS NULL or IS NOT NULL to check
8 What are constraints
• Rules applied on columns
• Maintain data quality
• Common constraints
– NOT NULL
– UNIQUE
– PRIMARY KEY
– FOREIGN KEY
– CHECK
9 What are data types
• Define type of data stored
• Common types
– INT for numbers
– VARCHAR for text
– DATE for dates
– FLOAT or DECIMAL for decimals
10 Interview tip you must remember
• Always explain with a small example
• Speak logic before syntax
• Keep answers short and direct
Double Tap ❤️ For More
❤11
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When preparing for an SQL project-based interview, the focus typically shifts from theoretical knowledge to practical application. Here are some SQL project-based interview questions that could help assess your problem-solving skills and experience:
1. Database Design and Schema
- Question: Describe a database schema you have designed in a past project. What were the key entities, and how did you establish relationships between them?
- Follow-Up: How did you handle normalization? Did you denormalize any tables for performance reasons?
2. Data Modeling
- Question: How would you model a database for an e-commerce application? What tables would you include, and how would they relate to each other?
- Follow-Up: How would you design the schema to handle scenarios like discount codes, product reviews, and inventory management?
3. Query Optimization
- Question: Can you discuss a time when you optimized an SQL query? What was the original query, and what changes did you make to improve its performance?
- Follow-Up: What tools or techniques did you use to identify and resolve the performance issues?
4. ETL Processes
- Question: Describe an ETL (Extract, Transform, Load) process you have implemented. How did you handle data extraction, transformation, and loading?
- Follow-Up: How did you ensure data quality and consistency during the ETL process?
5. Handling Large Datasets
- Question: In a project where you dealt with large datasets, how did you manage performance and storage issues?
- Follow-Up: What indexing strategies or partitioning techniques did you use?
6. Joins and Subqueries
- Question: Provide an example of a complex query you wrote involving multiple joins and subqueries. What was the business problem you were solving?
- Follow-Up: How did you ensure that the query performed efficiently?
7. Stored Procedures and Functions
- Question: Have you created stored procedures or functions in any of your projects? Can you describe one and explain why you chose to encapsulate the logic in a stored procedure?
- Follow-Up: How did you handle error handling and logging within the stored procedure?
8. Data Integrity and Constraints
- Question: How did you enforce data integrity in your SQL projects? Can you give examples of constraints (e.g., primary keys, foreign keys, unique constraints) you implemented?
- Follow-Up: How did you handle situations where constraints needed to be temporarily disabled or modified?
9. Version Control and Collaboration
- Question: How did you manage database version control in your projects? What tools or practices did you use to ensure collaboration with other developers?
- Follow-Up: How did you handle conflicts or issues arising from multiple developers working on the same database?
10. Data Migration
- Question: Describe a data migration project you worked on. How did you ensure that the migration was successful, and what steps did you take to handle data inconsistencies or errors?
- Follow-Up: How did you test the migration process before moving to the production environment?
11. Security and Permissions
- Question: In your SQL projects, how did you manage database security?
- Follow-Up: How did you handle encryption or sensitive data within the database?
12. Handling Unstructured Data
- Question: Have you worked with unstructured or semi-structured data in an SQL environment?
- Follow-Up: What challenges did you face, and how did you overcome them?
13. Real-Time Data Processing
- Question: Can you describe a project where you handled real-time data processing using SQL? What were the key challenges, and how did you address them?
- Follow-Up: How did you ensure the performance and reliability of the real-time data processing system?
Be prepared to discuss specific examples from your past work and explain your thought process in detail.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. Database Design and Schema
- Question: Describe a database schema you have designed in a past project. What were the key entities, and how did you establish relationships between them?
- Follow-Up: How did you handle normalization? Did you denormalize any tables for performance reasons?
2. Data Modeling
- Question: How would you model a database for an e-commerce application? What tables would you include, and how would they relate to each other?
- Follow-Up: How would you design the schema to handle scenarios like discount codes, product reviews, and inventory management?
3. Query Optimization
- Question: Can you discuss a time when you optimized an SQL query? What was the original query, and what changes did you make to improve its performance?
- Follow-Up: What tools or techniques did you use to identify and resolve the performance issues?
4. ETL Processes
- Question: Describe an ETL (Extract, Transform, Load) process you have implemented. How did you handle data extraction, transformation, and loading?
- Follow-Up: How did you ensure data quality and consistency during the ETL process?
5. Handling Large Datasets
- Question: In a project where you dealt with large datasets, how did you manage performance and storage issues?
- Follow-Up: What indexing strategies or partitioning techniques did you use?
6. Joins and Subqueries
- Question: Provide an example of a complex query you wrote involving multiple joins and subqueries. What was the business problem you were solving?
- Follow-Up: How did you ensure that the query performed efficiently?
7. Stored Procedures and Functions
- Question: Have you created stored procedures or functions in any of your projects? Can you describe one and explain why you chose to encapsulate the logic in a stored procedure?
- Follow-Up: How did you handle error handling and logging within the stored procedure?
8. Data Integrity and Constraints
- Question: How did you enforce data integrity in your SQL projects? Can you give examples of constraints (e.g., primary keys, foreign keys, unique constraints) you implemented?
- Follow-Up: How did you handle situations where constraints needed to be temporarily disabled or modified?
9. Version Control and Collaboration
- Question: How did you manage database version control in your projects? What tools or practices did you use to ensure collaboration with other developers?
- Follow-Up: How did you handle conflicts or issues arising from multiple developers working on the same database?
10. Data Migration
- Question: Describe a data migration project you worked on. How did you ensure that the migration was successful, and what steps did you take to handle data inconsistencies or errors?
- Follow-Up: How did you test the migration process before moving to the production environment?
11. Security and Permissions
- Question: In your SQL projects, how did you manage database security?
- Follow-Up: How did you handle encryption or sensitive data within the database?
12. Handling Unstructured Data
- Question: Have you worked with unstructured or semi-structured data in an SQL environment?
- Follow-Up: What challenges did you face, and how did you overcome them?
13. Real-Time Data Processing
- Question: Can you describe a project where you handled real-time data processing using SQL? What were the key challenges, and how did you address them?
- Follow-Up: How did you ensure the performance and reliability of the real-time data processing system?
Be prepared to discuss specific examples from your past work and explain your thought process in detail.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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✅ Basic SQL Queries Interview Questions With Answers 🖥️
1. What does SELECT do
• SELECT fetches data from a table
• You choose columns you want to see
Example: SELECT name, salary FROM employees;
2. What does FROM do
• FROM tells SQL where data lives
• It specifies the table name
Example: SELECT * FROM customers;
3. What is WHERE clause
• WHERE filters rows
• It runs before aggregation
Example: SELECT * FROM orders WHERE status = 'Delivered';
4. Difference between WHERE and HAVING
• WHERE filters rows before GROUP BY
• HAVING filters groups after aggregation
Example: WHERE filters orders, HAVING filters total_sales
5. How do you sort data
• Use ORDER BY
• Default order is ASC
Example: SELECT * FROM employees ORDER BY salary DESC;
6. How do you sort by multiple columns
• SQL sorts left to right
Example: SELECT * FROM students ORDER BY class ASC, marks DESC;
7. What is LIMIT
• LIMIT restricts number of rows returned
• Useful for top N queries
Example: SELECT * FROM products LIMIT 5;
8. What is OFFSET
• OFFSET skips rows
• Used with LIMIT for pagination
Example: SELECT * FROM products LIMIT 5 OFFSET 10;
9. How do you filter on multiple conditions
• Use AND, OR
Example: SELECT * FROM users WHERE city = 'Delhi' AND age > 25;
10. Difference between AND and OR
• AND needs all conditions true
• OR needs one condition true
Quick interview advice
• Always say execution order: FROM → WHERE → SELECT → ORDER BY → LIMIT
• Write clean examples
• Speak logic first, syntax next¹
Double Tap ❤️ For More
1. What does SELECT do
• SELECT fetches data from a table
• You choose columns you want to see
Example: SELECT name, salary FROM employees;
2. What does FROM do
• FROM tells SQL where data lives
• It specifies the table name
Example: SELECT * FROM customers;
3. What is WHERE clause
• WHERE filters rows
• It runs before aggregation
Example: SELECT * FROM orders WHERE status = 'Delivered';
4. Difference between WHERE and HAVING
• WHERE filters rows before GROUP BY
• HAVING filters groups after aggregation
Example: WHERE filters orders, HAVING filters total_sales
5. How do you sort data
• Use ORDER BY
• Default order is ASC
Example: SELECT * FROM employees ORDER BY salary DESC;
6. How do you sort by multiple columns
• SQL sorts left to right
Example: SELECT * FROM students ORDER BY class ASC, marks DESC;
7. What is LIMIT
• LIMIT restricts number of rows returned
• Useful for top N queries
Example: SELECT * FROM products LIMIT 5;
8. What is OFFSET
• OFFSET skips rows
• Used with LIMIT for pagination
Example: SELECT * FROM products LIMIT 5 OFFSET 10;
9. How do you filter on multiple conditions
• Use AND, OR
Example: SELECT * FROM users WHERE city = 'Delhi' AND age > 25;
10. Difference between AND and OR
• AND needs all conditions true
• OR needs one condition true
Quick interview advice
• Always say execution order: FROM → WHERE → SELECT → ORDER BY → LIMIT
• Write clean examples
• Speak logic first, syntax next¹
Double Tap ❤️ For More
❤5
✅ SQL Joins Interview Questions With Answers 🖥️
1. What is a JOIN in SQL. Explain with an example.
• JOIN combines data from multiple tables
• Tables connect using a common column
• Usually primary key to foreign key
Example tables
Customers
customer_id, name
Orders
order_id, customer_id, amount
Query
SELECT c.name, o.amount
FROM customers c
INNER JOIN orders o
ON c.customer_id = o.customer_id;
Explanation
• SQL matches customer_id in both tables
• Output shows only related customer order data
2. What is INNER JOIN. When do you use it.
• INNER JOIN returns only matching rows
• Rows without match are removed
Example
Find customers who placed orders
Query
SELECT c.customer_id, c.name
FROM customers c
INNER JOIN orders o
ON c.customer_id = o.customer_id;
Logic
• Customers without orders are excluded
• Only matched records appear
3. What is LEFT JOIN. Explain with use case.
• LEFT JOIN returns all rows from left table
• Matching rows from right table
• Non matches show NULL
Example
Find all customers and their orders
Query
SELECT c.name, o.order_id
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id;
Logic
• Customers without orders still appear
• order_id becomes NULL
4. Difference between INNER JOIN and LEFT JOIN.
• INNER JOIN removes non matching rows
• LEFT JOIN keeps all left table rows
• LEFT JOIN shows NULL for missing matches
Interview tip
Explain using one missing record example
5. What is RIGHT JOIN.
• Returns all rows from right table
• Matching rows from left table
• Rarely used in real projects
Example
SELECT c.name, o.order_id
FROM customers c
RIGHT JOIN orders o
ON c.customer_id = o.customer_id;
6. What is FULL OUTER JOIN.
• Returns all rows from both tables
• Matches where possible
• Non matches show NULL
Example
SELECT c.name, o.order_id
FROM customers c
FULL OUTER JOIN orders o
ON c.customer_id = o.customer_id;
Use case
• Data reconciliation
• Comparing two datasets
7. How do you find records present in one table but not in another.
Find customers with no orders
Query
SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;
Logic
• LEFT JOIN keeps all customers
• WHERE filters non matched rows
8. Explain JOIN with WHERE clause. Common mistake.
• WHERE runs after JOIN
• Wrong WHERE condition breaks LEFT JOIN
Wrong
LEFT JOIN orders
WHERE orders.amount > 1000
Correct
LEFT JOIN orders
ON (link unavailable) = (link unavailable)
AND orders.amount > 1000
9. How do you join more than two tables.
• JOIN step by step
• Each JOIN needs condition
Example
SELECT c.name, o.order_id, p.product_name
FROM customers c
JOIN orders o
ON c.customer_id = o.customer_id
JOIN products p
ON o.product_id = p.product_id;
10. SQL execution order for JOIN queries.
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY
Interview advice
• Always explain logic first
• Draw table flow in words
• Then write query
Double Tap ♥️ For More
1. What is a JOIN in SQL. Explain with an example.
• JOIN combines data from multiple tables
• Tables connect using a common column
• Usually primary key to foreign key
Example tables
Customers
customer_id, name
Orders
order_id, customer_id, amount
Query
SELECT c.name, o.amount
FROM customers c
INNER JOIN orders o
ON c.customer_id = o.customer_id;
Explanation
• SQL matches customer_id in both tables
• Output shows only related customer order data
2. What is INNER JOIN. When do you use it.
• INNER JOIN returns only matching rows
• Rows without match are removed
Example
Find customers who placed orders
Query
SELECT c.customer_id, c.name
FROM customers c
INNER JOIN orders o
ON c.customer_id = o.customer_id;
Logic
• Customers without orders are excluded
• Only matched records appear
3. What is LEFT JOIN. Explain with use case.
• LEFT JOIN returns all rows from left table
• Matching rows from right table
• Non matches show NULL
Example
Find all customers and their orders
Query
SELECT c.name, o.order_id
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id;
Logic
• Customers without orders still appear
• order_id becomes NULL
4. Difference between INNER JOIN and LEFT JOIN.
• INNER JOIN removes non matching rows
• LEFT JOIN keeps all left table rows
• LEFT JOIN shows NULL for missing matches
Interview tip
Explain using one missing record example
5. What is RIGHT JOIN.
• Returns all rows from right table
• Matching rows from left table
• Rarely used in real projects
Example
SELECT c.name, o.order_id
FROM customers c
RIGHT JOIN orders o
ON c.customer_id = o.customer_id;
6. What is FULL OUTER JOIN.
• Returns all rows from both tables
• Matches where possible
• Non matches show NULL
Example
SELECT c.name, o.order_id
FROM customers c
FULL OUTER JOIN orders o
ON c.customer_id = o.customer_id;
Use case
• Data reconciliation
• Comparing two datasets
7. How do you find records present in one table but not in another.
Find customers with no orders
Query
SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id
WHERE o.order_id IS NULL;
Logic
• LEFT JOIN keeps all customers
• WHERE filters non matched rows
8. Explain JOIN with WHERE clause. Common mistake.
• WHERE runs after JOIN
• Wrong WHERE condition breaks LEFT JOIN
Wrong
LEFT JOIN orders
WHERE orders.amount > 1000
Correct
LEFT JOIN orders
ON (link unavailable) = (link unavailable)
AND orders.amount > 1000
9. How do you join more than two tables.
• JOIN step by step
• Each JOIN needs condition
Example
SELECT c.name, o.order_id, p.product_name
FROM customers c
JOIN orders o
ON c.customer_id = o.customer_id
JOIN products p
ON o.product_id = p.product_id;
10. SQL execution order for JOIN queries.
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY
Interview advice
• Always explain logic first
• Draw table flow in words
• Then write query
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✅ SQL GROUP BY and AGGREGATION Interview Questions 🎓
1. What is GROUP BY in SQL.
• GROUP BY groups rows with same values
• Used with aggregate functions
• One row per group in output
Example
Find total salary per department
Logic
• Rows grouped by department
• SUM runs on each group
2. Why do we use aggregate functions.
• To summarize data
• To calculate totals, averages, counts
Common functions
• COUNT
• SUM
• AVG
• MIN
• MAX
3. What happens if you use GROUP BY without aggregation.
• Output shows unique combinations of grouped columns
Example
Logic
• Acts like DISTINCT
4. Difference between WHERE and HAVING.
• WHERE filters rows
• HAVING filters groups
• WHERE runs before GROUP BY
• HAVING runs after GROUP BY
Example
Find departments with total salary above 5,00,000
5. Can you use WHERE with GROUP BY.
• Yes
• WHERE filters raw data before grouping
Example
Ignore inactive employees
6. Common GROUP BY interview error.
Why does this query fail
Answer
• Non aggregated column must be in GROUP BY
• name is missing
Correct query
7. What's the difference between COUNT(*) COUNT(column)?
• COUNT(*) counts all rows
• COUNT(column) skips NULL values
Example
8. Find total orders per customer.
Logic
• One row per customer
• COUNT runs per customer group
9. Find customers with more than 5 orders.
Logic
• GROUP first
• Filter groups using HAVING
10. Execution order for GROUP BY queries.
• FROM
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY
Interview advice
• Say execution order clearly
• Explain using one simple example
• Avoid mixing WHERE and HAVING logic
Double Tap ♥️ For More
1. What is GROUP BY in SQL.
• GROUP BY groups rows with same values
• Used with aggregate functions
• One row per group in output
Example
Find total salary per department
FROM employees
GROUP BY department;
Logic
• Rows grouped by department
• SUM runs on each group
2. Why do we use aggregate functions.
• To summarize data
• To calculate totals, averages, counts
Common functions
• COUNT
• SUM
• AVG
• MIN
• MAX
3. What happens if you use GROUP BY without aggregation.
• Output shows unique combinations of grouped columns
Example
FROM employees
GROUP BY department;
Logic
• Acts like DISTINCT
4. Difference between WHERE and HAVING.
• WHERE filters rows
• HAVING filters groups
• WHERE runs before GROUP BY
• HAVING runs after GROUP BY
Example
Find departments with total salary above 5,00,000
FROM employees
GROUP BY department
HAVING SUM(salary) > 500000;
5. Can you use WHERE with GROUP BY.
• Yes
• WHERE filters raw data before grouping
Example
Ignore inactive employees
FROM employees
WHERE status = 'Active'
GROUP BY department;
6. Common GROUP BY interview error.
Why does this query fail
FROM employees
GROUP BY department;
Answer
• Non aggregated column must be in GROUP BY
• name is missing
Correct query
FROM employees
GROUP BY department;
7. What's the difference between COUNT(*) COUNT(column)?
• COUNT(*) counts all rows
• COUNT(column) skips NULL values
Example
SELECT COUNT(delivery_date) FROM orders;
8. Find total orders per customer.
FROM orders
GROUP BY customer_id;
Logic
• One row per customer
• COUNT runs per customer group
9. Find customers with more than 5 orders.
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 5;
Logic
• GROUP first
• Filter groups using HAVING
10. Execution order for GROUP BY queries.
• FROM
• WHERE
• GROUP BY
• HAVING
• SELECT
• ORDER BY
Interview advice
• Say execution order clearly
• Explain using one simple example
• Avoid mixing WHERE and HAVING logic
Double Tap ♥️ For More
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