Machine Learning & Artificial Intelligence | Data Science Free Courses

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🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF) JOINS help you combine data from two or more tables based on a related column (usually a primary key and a foreign key). 1. INNER JOIN Returns only matching rows between two tables. SELECT customers.name, orders.order_id FROM customers INNER JOIN orders ON customers.id = orders.customer_id; This returns only those customers who have placed at least one order. 2. LEFT JOIN (or LEFT OUTER JOIN) Returns all rows from the left table, and matched rows from the right table. If no match, you'll see NULLs. SELECT customers.name, orders.order_id FROM customers LEFT JOIN orders ON customers.id = orders.customer_id; This shows all customers, including those who haven’t placed any orders. 3. RIGHT JOIN (or RIGHT OUTER JOIN) Returns all rows from the right table, and matching rows from the left. SELECT customers.name, orders.order_id FROM customers RIGHT JOIN orders ON customers.id = orders.customer_id; You’ll see all orders — even if there’s no corresponding customer info. 4. FULL JOIN (or FULL OUTER JOIN) Returns all rows from both tables. If there's no match, it returns NULLs. Note: MySQL doesn't support FULL JOIN directly; use UNION of LEFT and RIGHT joins instead. 5. SELF JOIN You join a table with itself. Great for hierarchical relationships. SELECT e.name AS employee, m.name AS manager FROM employees e JOIN employees m ON e.manager_id = m.id; This shows each employee along with their manager's name. Pro Tip: Be careful with NULLs and always define clear join conditions to avoid cartesian products. Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume 📌1. Social Media Analytics: (https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset) 🚀2. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 📌3. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics- attrition-dataset) 🚀4. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 📌5. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 🚀6. Inventory Management: (https://www.kaggle.com/datasets? search=inventory+management) 📌 7.Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson- marketing-customer-value-data) 🚀8. Financial Data Analysis: (https://www.kaggle.com/awaiskalia/banking-database) 📌9. Supply Chain Management: (https://www.kaggle.com/shashwatwork/procurement-analytics) 🚀10. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself. Join for more: https://t.me/DataPortfolio Hope this piece of information helps you

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