Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

📊 Essential SQL Concepts Every Data Analyst Must Know 🚀 SQL is the most important skill for Data Analysts. Almost every analytics job requires working with databases to extract, filter, analyze, and summarize data. Understanding the following SQL concepts will help you write efficient queries and solve real business problems with data. 1️⃣ SELECT Statement (Data Retrieval) What it is: Retrieves data from a table.

SELECT name, salary
FROM employees;

Use cases: Retrieving specific columns, viewing datasets, extracting required information. 2️⃣ WHERE Clause (Filtering Data) What it is: Filters rows based on specific conditions.

SELECT *
FROM orders
WHERE order_amount > 500;

Common conditions: =, >, <, >=, <=, BETWEEN, IN, LIKE 3️⃣ ORDER BY (Sorting Data) What it is: Sorts query results in ascending or descending order.

SELECT name, salary
FROM employees
ORDER BY salary DESC;

Sorting options: ASC (default), DESC 4️⃣ GROUP BY (Aggregation) What it is: Groups rows with same values into summary rows.

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

Use cases: Sales per region, customers per country, orders per product category. 5️⃣ Aggregate Functions What they do: Perform calculations on multiple rows.

SELECT AVG(salary)
FROM employees;

Common functions: COUNT(), SUM(), AVG(), MIN(), MAX() 6️⃣ HAVING Clause What it is: Filters grouped data after aggregation.

SELECT department, COUNT(*)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

Key difference: WHERE filters rows before grouping, HAVING filters groups after aggregation. 7️⃣ SQL JOINS (Combining Tables) What they do: Combine tables. -- INNER JOIN

SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;

-- LEFT JOIN

SELECT customers.customer_name, orders.order_id
FROM customers
LEFT JOIN orders
ON customers.customer_id = orders.customer_id;

Common types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN 8️⃣ Subqueries What it is: Query inside another query.

SELECT name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

Use cases: Comparing values, filtering based on aggregated results. 9️⃣ Common Table Expressions (CTE) What it is: Temporary result set used inside a query.

WITH high_salary AS (
  SELECT name, salary
  FROM employees
  WHERE salary > 70000
)
SELECT *
FROM high_salary;

Benefits: Cleaner queries, easier debugging, better readability. 🔟 Window Functions What they do: Perform calculations across rows related to current row.

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;

Common functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD() Why SQL is Critical for Data Analysts • Extract data from databases • Analyze large datasets efficiently • Generate reports and dashboards • Support business decision-making SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v Double Tap ♥️ For More

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🚀 SQL Project Series #5 E-Commerce Sales Analysis – Advanced Business Analytics In this part, we'll solve real-world business problems that Data Analysts encounter while working with customer, sales, and product data. 31. Calculate Customer Lifetime Value (CLV) SELECT o.customer_id, SUM(oi.quantity * oi.unit_price) AS customer_lifetime_value FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY o.customer_id ORDER BY customer_lifetime_value DESC; 32. Calculate Repeat Purchase Rate WITH customer_orders AS ( SELECT customer_id, COUNT(*) AS total_orders FROM orders GROUP BY customer_id ) SELECT ROUND( 100.0 * COUNT(CASE WHEN total_orders > 1 THEN 1 END) / COUNT(*), 2 ) AS repeat_purchase_rate FROM customer_orders; 33. Find New vs Returning Customers WITH first_order AS ( SELECT customer_id, MIN(order_date) AS first_order_date FROM orders GROUP BY customer_id ) SELECT CASE WHEN o.order_date = f.first_order_date THEN 'New Customer' ELSE 'Returning Customer' END AS customer_type, COUNT(*) AS total_orders FROM orders o JOIN first_order f ON o.customer_id = f.customer_id GROUP BY customer_type; 34. Find Customer Retention by Month WITH monthly_orders AS ( SELECT DISTINCT customer_id, DATE_TRUNC('month', order_date) AS order_month FROM orders ) SELECT order_month, COUNT(DISTINCT customer_id) AS active_customers FROM monthly_orders GROUP BY order_month ORDER BY order_month; 35. Find Customers Who Purchased from Multiple Categories SELECT o.customer_id, COUNT(DISTINCT p.category) AS categories_purchased FROM orders o JOIN order_items oi ON o.order_id = oi.order_id JOIN products p ON oi.product_id = p.product_id GROUP BY o.customer_id HAVING COUNT(DISTINCT p.category) > 1; 36. Find the Most Frequently Purchased Product Pair SELECT oi1.product_id AS product₁, oi2.product_id AS product₂, COUNT(*) AS purchase_count FROM order_items oi1 JOIN order_items oi2 ON oi1.order_id = oi2.order_id AND oi1.product_id < oi2.product_id GROUP BY oi1.product_id, oi2.product_id ORDER BY purchase_count DESC LIMIT 10; 37. Calculate Average Days Between Orders WITH customer_orders AS ( SELECT customer_id, order_date, LAG(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date ) AS previous_order FROM orders ) SELECT customer_id, ROUND( AVG(order_date - previous_order), 2 ) AS avg_days_between_orders FROM customer_orders WHERE previous_order IS NOT NULL GROUP BY customer_id; 38. Find the Fastest Growing Product Category WITH monthly_category_sales AS ( SELECT DATE_TRUNC('month', o.order_date) AS month, p.category, SUM(oi.quantity * oi.unit_price) AS revenue FROM orders o JOIN order_items oi ON o.order_id = oi.order_id JOIN products p ON oi.product_id = p.product_id GROUP BY month, p.category ) SELECT month, category, revenue, revenue - LAG(revenue) OVER ( PARTITION BY category ORDER BY month ) AS revenue_growth FROM monthly_category_sales; 39. Identify Customers at Risk of Churn SELECT customer_id, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING MAX(order_date) < CURRENT_DATE - INTERVAL '90 days'; 40. Perform RFM Analysis SELECT customer_id, CURRENT_DATE - MAX(order_date) AS recency, COUNT(order_id) AS frequency, SUM(oi.quantity * oi.unit_price) AS monetary FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY customer_id ORDER BY monetary DESC; 💡 Double Tap ❤️ For More

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29. Find Monthly Revenue Growth WITH monthly_sales AS (     SELECT         DATE_TRUNC('month', o.order_date) AS month,         SUM(oi.quantity * oi.unit_price) AS revenue     FROM orders o     JOIN order_items oi ON o.order_id = oi.order_id     GROUP BY DATE_TRUNC('month', o.order_date) ) SELECT     month,     revenue,     LAG(revenue) OVER (ORDER BY month) AS previous_month_revenue,     ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month)) / LAG(revenue) OVER (ORDER BY month), 2) AS growth_percentage FROM monthly_sales; 30. Find the Highest Value Order for Each Customer WITH order_values AS (     SELECT         o.customer_id,         o.order_id,         SUM(oi.quantity * oi.unit_price) AS order_value     FROM orders o     JOIN order_items oi ON o.order_id = oi.order_id     GROUP BY o.customer_id, o.order_id ) SELECT * FROM (     SELECT *,            ROW_NUMBER() OVER (                PARTITION BY customer_id ORDER BY order_value DESC            ) AS rn     FROM order_values ) t WHERE rn = 1; Window Functions Covered  • Ranking: ROW_NUMBER(), RANK(), DENSE_RANK()  • Navigation: LAG(), LEAD()  • Aggregates: SUM() OVER()  • Analytics: Running Totals, Revenue Contribution, Month-over-Month Growth  💡 Double Tap ❤️ For More

SQL Project Series #4 E-Commerce Sales Analysis – Advanced SQL with Window Functions 🚀 Window functions are widely used by Data Analysts to calculate rankings, running totals, moving averages, and customer insights without losing row-level details. Business Questions 21. Rank Customers by Total Revenue WITH customer_revenue AS ( SELECT o.customer_id, SUM(oi.quantity * oi.unit_price) AS revenue FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY o.customer_id ) SELECT customer_id, revenue, DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank FROM customer_revenue; 22. Find the Top Selling Product in Each Category WITH product_sales AS ( SELECT p.category, p.product_name, SUM(oi.quantity) AS total_sold FROM products p JOIN order_items oi ON p.product_id = oi.product_id GROUP BY p.category, p.product_name ) SELECT * FROM ( SELECT *, ROW_NUMBER() OVER ( PARTITION BY category ORDER BY total_sold DESC ) AS rn FROM product_sales ) t WHERE rn = 1; 23. Calculate Running Revenue by Order Date WITH daily_sales AS ( SELECT o.order_date, SUM(oi.quantity * oi.unit_price) AS daily_revenue FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY o.order_date ) SELECT order_date, daily_revenue, SUM(daily_revenue) OVER (ORDER BY order_date) AS running_revenue FROM daily_sales; 24. Find the Previous Order Date for Each Customer SELECT customer_id, order_id, order_date, LAG(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date ) AS previous_order_date FROM orders; 25. Find the Next Order Date for Each Customer SELECT customer_id, order_id, order_date, LEAD(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date ) AS next_order_date FROM orders; 26. Calculate Days Between Consecutive Orders SELECT customer_id, order_date, order_date - LAG(order_date) OVER ( PARTITION BY customer_id ORDER BY order_date ) AS days_between_orders FROM orders; Note: For Postgres use order_date - LAG(order_date) OVER(...). For MySQL use DATEDIFF(order_date, LAG(order_date) OVER(...)) 27. Find the Top 3 Customers by Revenue WITH customer_revenue AS ( SELECT o.customer_id, SUM(oi.quantity * oi.unit_price) AS revenue FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY o.customer_id ) SELECT * FROM ( SELECT *, DENSE_RANK() OVER (ORDER BY revenue DESC) AS rnk FROM customer_revenue ) t WHERE rnk <= 3; 28. Find Each Product's Contribution to Total Revenue WITH product_revenue AS ( SELECT p.product_name, SUM(oi.quantity * oi.unit_price) AS revenue FROM products p JOIN order_items oi ON p.product_id = oi.product_id GROUP BY p.product_name ) SELECT product_name, revenue, ROUND(100.0 * revenue / SUM(revenue) OVER (), 2) AS revenue_percentage FROM product_revenue ORDER BY revenue DESC;

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SQL Project Series #3 E-Commerce Sales Analysis – Intermediate SQL Business Questions Let's solve more real-world business problems using SQL. Business Questions 11. Find Repeat Customers SELECT customer_id, COUNT(order_id) AS total_orders FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 1; 12. Find Customers Who Never Placed an Order SELECT c.customer_id, c.customer_name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.order_id IS NULL; 13. Find Inactive Customers (No Orders in the Last 90 Days) SELECT c.customer_id, c.customer_name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.customer_name HAVING MAX(o.order_date) < CURRENT_DATE - INTERVAL '90 days' OR MAX(o.order_date) IS NULL; 14. Find the Best-Selling Product Category SELECT p.category, SUM(oi.quantity) AS units_sold FROM products p JOIN order_items oi ON p.product_id = oi.product_id GROUP BY p.category ORDER BY units_sold DESC LIMIT 1; 15. Find the Highest Revenue Product SELECT p.product_name, SUM(oi.quantity * oi.unit_price) AS revenue FROM products p JOIN order_items oi ON p.product_id = oi.product_id GROUP BY p.product_name ORDER BY revenue DESC LIMIT 1; 16. Find the Lowest Revenue Product SELECT p.product_name, SUM(oi.quantity * oi.unit_price) AS revenue FROM products p JOIN order_items oi ON p.product_id = oi.product_id GROUP BY p.product_name ORDER BY revenue LIMIT 1; 17. Calculate Average Products per Order SELECT ROUND(AVG(product_count), 2) AS avg_products_per_order FROM ( SELECT order_id, SUM(quantity) AS product_count FROM order_items GROUP BY order_id ) t; 18. Find Orders Worth More Than 10,000 SELECT order_id, SUM(quantity * unit_price) AS order_value FROM order_items GROUP BY order_id HAVING SUM(quantity * unit_price) > 10000; 19. Find Customers with the Highest Average Order Value SELECT customer_id, ROUND(AVG(order_value), 2) AS avg_order_value FROM ( SELECT o.customer_id, o.order_id, SUM(oi.quantity * oi.unit_price) AS order_value FROM orders o JOIN order_items oi ON o.order_id = oi.order_id GROUP BY o.customer_id, o.order_id ) t GROUP BY customer_id ORDER BY avg_order_value DESC; 20. Find the Top 3 Cities by Revenue SELECT c.city, SUM(oi.quantity * oi.unit_price) AS revenue FROM customers c JOIN orders o ON c.customer_id = o.customer_id JOIN order_items oi ON o.order_id = oi.order_id GROUP BY c.city ORDER BY revenue DESC LIMIT 3; SQL Concepts Practiced • LEFT JOIN • HAVING • Aggregate Functions • Nested Queries • GROUP BY • Business KPI Analysis • Customer Segmentation • Revenue Analysis 💡 Double Tap ❤️ For More

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🚀 SQL Project Series #2 E-Commerce Sales Analysis – SQL Business Questions Now that our database is ready, let's solve real-world business problems using SQL. 📊 Business Questions 1. Calculate Total Revenue

SELECT SUM(quantity * unit_price) AS total_revenue
FROM order_items;

2. Count Total Orders

SELECT COUNT(*) AS total_orders
FROM orders;

3. Count Total Customers

SELECT COUNT(*) AS total_customers
FROM customers;

4. Count Total Products

SELECT COUNT(*) AS total_products
FROM products;

5. Calculate Average Order Value (AOV)

SELECT
    ROUND(
        SUM(quantity * unit_price) /
        COUNT(DISTINCT order_id),
        2
    ) AS average_order_value
FROM order_items;

6. Find Top 5 Selling Products

SELECT
    p.product_name,
    SUM(oi.quantity) AS total_quantity
FROM order_items oi
JOIN products p ON oi.product_id = p.product_id
GROUP BY p.product_name
ORDER BY total_quantity DESC
LIMIT 5;

7. Find Revenue by Product Category

SELECT
    p.category,
    SUM(oi.quantity * oi.unit_price) AS revenue
FROM order_items oi
JOIN products p ON oi.product_id = p.product_id
GROUP BY p.category
ORDER BY revenue DESC;

8. Find Top 5 Customers by Revenue

SELECT
    c.customer_name,
    SUM(oi.quantity * oi.unit_price) AS revenue
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
JOIN order_items oi ON o.order_id = oi.order_id
GROUP BY c.customer_name
ORDER BY revenue DESC
LIMIT 5;

9. Calculate Monthly Revenue

SELECT
    DATE_TRUNC('month', o.order_date) AS month,
    SUM(oi.quantity * oi.unit_price) AS revenue
FROM orders o
JOIN order_items oi ON o.order_id = oi.order_id
GROUP BY DATE_TRUNC('month', o.order_date)
ORDER BY month;

10. Find Revenue by City

SELECT
    c.city,
    SUM(oi.quantity * oi.unit_price) AS revenue
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
JOIN order_items oi ON o.order_id = oi.order_id
GROUP BY c.city
ORDER BY revenue DESC;

🎯 SQL Concepts Practiced: Aggregate Functions, GROUP BY, ORDER BY, INNER JOIN, LIMIT, Date Functions, Business KPI Calculations 💡 Double Tap ❤️ For More!

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🚀 SQL Project Series #1 E-Commerce Sales Analysis Project 🛒 Build a real-world SQL project from scratch and learn the SQL skills required for Data Analyst interviews. 🎯 Business Objectives ✅ Analyze total sales and revenue ✅ Identify top-selling products ✅ Find the best-performing product categories ✅ Calculate monthly sales trends ✅ Identify repeat customers ✅ Find inactive customers ✅ Calculate Average Order Value (AOV) ✅ Calculate Customer Lifetime Value (CLV) ✅ Analyze customer purchasing behavior 📂 Step 1: Create Database CREATE DATABASE ecommerce_db; USE ecommerce_db; 📂 Step 2: Create Customers Table CREATE TABLE customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(100), gender VARCHAR(10), city VARCHAR(50), signup_date DATE ); 📂 Step 3: Create Products Table CREATE TABLE products ( product_id INT PRIMARY KEY, product_name VARCHAR(100), category VARCHAR(50), price DECIMAL(10,2) ); 📂 Step 4: Create Orders Table CREATE TABLE orders ( order_id INT PRIMARY KEY, customer_id INT, order_date DATE, order_status VARCHAR(30), FOREIGN KEY (customer_id) REFERENCES customers(customer_id) ); 📂 Step 5: Create Order_Items Table CREATE TABLE order_items ( order_item_id INT PRIMARY KEY, order_id INT, product_id INT, quantity INT, unit_price DECIMAL(10,2), FOREIGN KEY (order_id) REFERENCES orders(order_id), FOREIGN KEY (product_id) REFERENCES products(product_id) ); 📂 Step 6: Insert Sample Customers INSERT INTO customers VALUES (1,'Rahul','Male','Mumbai','2025-01-10'), (2,'Priya','Female','Delhi','2025-01-15'), (3,'Amit','Male','Pune','2025-02-01'), (4,'Sneha','Female','Bangalore','2025-02-10'), (5,'Rohan','Male','Hyderabad','2025-03-05'); 📂 Step 7: Insert Sample Products INSERT INTO products VALUES (101,'Laptop','Electronics',65000), (102,'Headphones','Electronics',2500), (103,'Office Chair','Furniture',7000), (104,'Keyboard','Electronics',1800), (105,'Water Bottle','Home',600); 📂 Step 8: Insert Sample Orders INSERT INTO orders VALUES (1001,1,'2025-03-01','Delivered'), (1002,2,'2025-03-03','Delivered'), (1003,1,'2025-03-10','Delivered'), (1004,3,'2025-03-15','Cancelled'), (1005,4,'2025-03-20','Delivered'); 📂 Step 9: Insert Sample Order Items INSERT INTO order_items VALUES (1,1001,101,1,65000), (2,1001,102,2,2500), (3,1002,103,1,7000), (4,1003,104,1,1800), (5,1004,105,3,600), (6,1005,101,1,65000); 🧠 SQL Concepts You'll Practice ✔ DDL Commands ✔ DML Commands ✔ Primary & Foreign Keys ✔ Joins ✔ Aggregate Functions ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ Subqueries ✔ CTEs ✔ Window Functions ✔ Date Functions 📊 Business KPIs You Can Build 📈 Total Revenue 📈 Total Orders 📈 Total Customers 📈 Average Order Value (AOV) 📈 Revenue by Product Category 📈 Monthly Sales Trend 📈 Daily Sales Trend 📈 Top 10 Selling Products 📈 Top 10 Customers by Revenue 📈 Revenue by City 📈 Revenue by Gender 📈 Customer Lifetime Value (CLV) 📈 Repeat Purchase Rate 📈 Customer Retention Rate 📈 Customer Churn Rate 📈 Average Products per Order 📈 Order Cancellation Rate 📈 Delivered vs Cancelled Orders 📈 Best Selling Category 📈 Worst Selling Category 📈 Most Expensive Product Sold 📈 Highest Revenue Month 📈 Customer Acquisition by Month 📈 New vs Returning Customers 📈 Product-wise Revenue 📈 Category-wise Revenue Contribution 🎯 Double Tap ❤️ For Part-2

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🚀 Top 11 SQL Project Ideas to Build a Strong Data Analytics Portfolio Building projects is one of the fastest ways to improve your SQL skills and stand out in interviews. Here are 11 real-world project ideas: 1️⃣ E-Commerce Sales Analysis Analyze sales trends Top-selling products Customer segmentation Revenue by category Repeat customer analysis 2️⃣ Banking Transaction Analysis Detect fraudulent transactions Monthly account activity Customer spending patterns Balance trends High-value transactions 3️⃣ Food Delivery Analytics Delivery time analysis Restaurant performance Peak ordering hours Customer retention Delivery partner efficiency 4️⃣ HR Analytics Dashboard Employee attrition Salary analysis Department-wise performance Hiring trends Attendance insights 5️⃣ Hospital Management Analysis Patient admissions Doctor utilization Readmission rate Bed occupancy Treatment costs 6️⃣ Netflix Movie & TV Show Analysis Most popular genres Content by country Ratings analysis Release trends Duration analysis 7️⃣ IPL Cricket Data Analysis Top batsmen Best bowlers Team performance Venue analysis Winning trends 8️⃣ Retail Inventory Management Stock availability Inventory turnover Slow-moving products Supplier performance Stock-out analysis 9️⃣ Ride-Sharing Analytics Peak ride hours Driver earnings Customer retention Trip cancellation rate City-wise demand 🔟 Finance & Expense Tracker Monthly expenses Budget vs actual Savings analysis Category-wise spending Cash flow trends 1️⃣1️⃣ Social Media Analytics User engagement Daily Active Users DAU Monthly Active Users MAU Content performance User retention 🔥 Double Tap ❤️ For More

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