Post #2530

Now, let's move to the next topic of data analytics roadmap: SQL Basics for Data Analytics What SQL does - Pull data from databases - Filter large datasets - Combine tables - Summarize metrics Core clauses - SELECT: Choose columns Example: SELECT name, sales FROM orders; - FROM: Source table Example: FROM orders; - WHERE: Filter rows Example: WHERE sales > 5000; - ORDER BY: Sort results Example: ORDER BY sales DESC; - LIMIT: Restrict rows Example: LIMIT 10; Filtering operators - =, <>, >, <, >=, <= - BETWEEN for ranges - IN for lists - LIKE for patterns Example: WHERE region IN ('East','West'); Logical conditions - AND - OR - NOT Aggregations - GROUP BY: Group rows Example: GROUP BY product; - Aggregate functions: COUNT, SUM, AVG, MIN, MAX - HAVING: Filter after aggregation Example: HAVING SUM(sales) > 100000; JOINS - INNER JOIN: Matching rows only - LEFT JOIN: All left rows, matching right - RIGHT JOIN: All right rows, matching left - FULL JOIN: All rows from both tables Example: SELECT o.order_id, c.customer_name FROM orders o INNER JOIN customers c ON o.customer_id = c.customer_id; NULL handling - IS NULL - IS NOT NULL - COALESCE(column, 0) Subqueries Query inside a query Example: SELECT * FROM orders WHERE sales > (SELECT AVG(sales) FROM orders); Window functions - ROW_NUMBER: Unique row number - RANK: Ranking with gaps - PARTITION BY: Reset calculation per group Example: ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) Common mistakes - Forgetting GROUP BY columns - Using WHERE instead of HAVING - Wrong join condition - Ignoring NULLs Daily practice - Write 5 SELECT queries - Use 1 JOIN - Use 1 GROUP BY - Handle NULL values SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v Double Tap ♥️ For More