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

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

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Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data

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📈 Telegram 频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources 的分析概览

频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 39 697 名订阅者,在 教育 类别中位列第 4 634,并在 印度 地区排名第 9 720 位。

📊 受众指标与增长动态

自 невідомо 创建以来,项目保持高速增长,吸引了 39 697 名订阅者。

根据 05 十月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -26,过去 24 小时变化为 5,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 1.65%。内容发布后 24 小时内通常能获得 0.65% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 655 次浏览,首日通常累积 257 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 3。
  • 主题关注点: 内容集中在 analytic, dataset, visualization, sql, learning 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
“Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data”

凭借高频更新(最新数据采集于 06 十月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

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📊 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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🎓 𝗛𝗔𝗥𝗩𝗔𝗥𝗗 𝗨𝗡𝗜𝗩𝗘𝗥𝗦𝗜𝗧𝗬 𝗙𝗥𝗘𝗘 𝗢𝗡𝗟𝗜𝗡𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 😍 Dreaming of learning from one of the world’s m
🎓 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!
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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
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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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𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀! ​ Looking to learn practi
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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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🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊 Want to start a caree
🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊 Want to start a career in Data Analytics? Explore these 7 free Microsoft-backed learning resources covering Power BI, Excel, SQL and data fundamentals 🔗 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇 https://pdlink.in/3Tm2D3Z 💡 Ideal for students, freshers and professionals who want to build practical data skills.
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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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𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀 Join this expert-led masterclass and discover how to become industry-rea
𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀 Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles. 📅 Date: 24 September 2026 ⏰ Time: 7:00 PM–9:00 PM IST 🌐 Mode: Online 🎓 Certificate: Available to all attendees Eligibility :- Graduates Passing In 2025 or earlier 🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇 https://pdlink.in/4xAMeGW ⚡ Register now and take your first step towards a successful career in AI!
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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 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲 Explore these certification courses
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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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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.
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🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀 Explore these beginner-friendly courses and strengthen your r
🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀 Explore these beginner-friendly courses and strengthen your resume! 🎯 Perfect for Students, Freshers and Working Professionals 💻 Learn Online at Your Own Pace 📜 Earn Certificates After Successful Completion 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:- https://pdlink.in/45KgqDR 🔥 Don’t just collect certificates—build skills that employers value. Share this with your friends!
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𝗜𝗻𝗳𝗼𝘀𝘆𝘀 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 & 𝗔𝗻𝘀𝘄𝗲𝗿𝘀😍 ​ ✅ Real Interview Experiences ✅
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