Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
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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;
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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
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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
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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;
SELECT COUNT(*) AS total_orders
FROM orders;
SELECT COUNT(*) AS total_customers
FROM customers;
SELECT COUNT(*) AS total_products
FROM products;
SELECT
ROUND(
SUM(quantity * unit_price) /
COUNT(DISTINCT order_id),
2
) AS average_order_value
FROM order_items;
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;
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;
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;
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;
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;
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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
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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
You already have the skills and expertise in Data Analytics tools like SQL, Power BI, Tableau, and Python. 𝐍𝐨𝐰, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐟𝐢𝐧𝐝 𝐚 𝐣𝐨𝐛?
1. Tailor your LinkedIn profile to highlight your Data Analyst skills and experience.
2. Make a list of companies that hire Data Analysts and follow them on LinkedIn to stay updated on job openings. (Ex- McKinsey & Company, BCG, Bain & Company, Google, Amazon, Microsoft, IBM, Goldman Sachs, JPMorgan Chase, Walmart, Target)
3. Follow HRs from your target companies on LinkedIn and reach out to them for job openings or whenever they post about job openings, send your resume to them within 2-3 hours via LinkedIn or email if available.
4. Connect with Managers or Senior Managers in Data Analyst roles at your target companies on LinkedIn and ask if they are hiring for their team or would be willing to refer you for any relevant Data analyst role.
5. Apply for jobs on LinkedIn, Naukri, and directly on the company's website.
🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀
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✅ Company-specific Handbook
✅ Interview Process & Preparation Roadmap
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DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)
👉 Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
👉SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 – Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
👉Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
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