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Day 14 #SQLWithIDC INNER JOIN seeks alignment LEFT JOIN preserves perspective RIGHT JOIN changes it A NULL in a LEFT JOIN isn’t absence — it’s a story untold, a connection yet to be made Data reminds us: inclusion reveals as much as matching ever will @indiandataclub @dpdzero
𝗗𝗮𝘆 𝟭𝟴 #SQLWithIDC🚀 Today I explored UNION & UNION ALL — combining multiple queries into one dataset! ✨ 💡Quick Tips: • UNION = no duplicates✅ • UNION ALL = keeps all✅ • Match columns & types • Order only after final result Combine smartly, analyze faster! 🙌
Day 10 of #SQLWithIDC 🚀— CASE statements 💡 Key takeaways: • Use CASE to categorize or create conditional metrics • Always include ELSE (avoid NULLs) • Works in SELECT, ORDER BY, and GROUP BY • First match wins — order matters!
𝗗𝗮𝘆 12 #21DaysSQLChallenge 🚀 Handling NULLs & comparing categories ✅ IS NULL / IS NOT NULL ✅ COALESCE for fallback ✅ COUNT(*) vs COUNT(column) ✅ CTE + CASE for clean summaries ⚠️ Don’t treat NULL as a value Stepwise queries = clear & scalable SQL! 🙌 #SQLWithIDC
Day 10/21 days sql challenge #SQLwithIDC SELECT SERVICE,COUNT(*) AS total_patients_admitted,AVG(SATISFACTION) AS avg_satisfactionCASE AVG(SATISFACTION) >= 85 'Excellent' AVG(SATISFACTION) >= 75 'Good' AVG(SATISFACTION) >= 65 'Fair' ELSE 'Needs Improvement'END AS pc FROM patients
day -18/21 Days SQl challenge #SQLwithIDC Getting back to the Grind of SQL with @indiandataclub and @dpdzero
Completed 7 days of the 21 Days SQL Challenge. Staying consistent has helped me strengthen my foundation and approach problems with clarity. Looking forward to learning more and progressing further in the upcoming days. Tagging @dpdzero and @indiandataclub #SQLWithIDC
Day 18 #SQLWithIDC 🔶UNION removes duplicates, slower but unique results. 🔷UNION ALL keeps all rows, faster performance. 🔶Same columns + compatible types. Use ORDER BY last. Pro tip: Use UNION ALL when duplicates aren't an issue! @indiandataclub @dpdzero
Day 21 #SQLWithIDC CTEs transform tangled logic into elegant, reusable steps, turning complexity into insight. From simple stats to multi-step analysis, CTEs are a game-changer for query organization. @indiandataclub @dpdzero
Day 15 #SQLWithIDC🚀 Today’s focus: joining 3+ tables — the real power move in SQL! 💡 Key Learnings: 🔗 Join tables left → right 🎯 Mix INNER + LEFT wisely 🧩 Use DISTINCT / GROUP BY to remove duplicates ⚠️ Avoid missing join conditions & WHERE filters that break LEFT JOINs
Day 14/21 dayssqlchallenge #SQLwithIDC Excited to share that as part of the #SQLwithIDC challenge, I completed over 400 lines of SQL within just 14 days. Grateful to be part of this learning journey! @indiandataclub @dpdzero
🗓️ Day 21 of #21DaysSQLChallenge by @indiandataclub! Today's topic: CTEs🎯 Using the WITH clause, you can create a temporary named result set & build your query step by step! Key takeaways💡 • Break complex queries into simple steps • Reuse logic • Easy to debug #SQLWithIDC
Day 15 #SQLWithIDC I explored the art of joining tables, untangling complex relationships,and turning raw data into meaningful insights.This journey isn’t just about queriesit’s about seeing patterns, solving problems, and mastering the language of data @indiandataclub @dpdzero
𝗗𝗮𝘆 𝟭𝟳 𝗼𝗳 #SQLWithIDC 🚀 Today’s focus: Subqueries in SELECT & FROM 💡 • SELECT → calculate per row • FROM → organize complex logic • Always alias derived tables • Correlated subqueries = slower ⚠ 🎯Takeaway: Subqueries make SQL cleaner and smarter! 🔥
Day 20 #SQLWithIDC Window functions like SUM() OVER and AVG() OVER unlock running totals, moving averages, and cumulative stats no GROUP BY needed. Track trends, smooth data, and compare values in a single query. Power up your analytics! @indiandataclub @dpdzero
Day 10 ✅ of #21DaysSQLChallenge by @IndianDataClub x @DPDzero Topic: CASE WHEN & Conditional Logic Today I learned to make SQL smarter — adding if-else logic inside queries 💡 Categorized satisfaction, grouped patients, & built performance reports with CASE! #SQLWithIDC
Day 9 of #21DaysSQLChallenge 🚀 Today’s focus: Date Functions ⏰ 💡 Key takeaways: • DATEDIFF() to calculate durations • YEAR(), MONTH(), DAY() to extract parts • Use ISO format (YYYY-MM-DD) for consistency • Avoid functions in WHERE on large tables #SQLWithIDC
Day 1/21 ✅ — #SQLWithIDC Starting my SQL journey with SELECT, DISTINCT & FROM Learning how to ask data questions with SQL. Query: SELECT DISTINCT service_name FROM services_weekly; #SQL #DataAnalytics #21DaysOfSQLChallenge
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