#sqlwithidc 検索結果
Day 12 #SQLWithIDC In SQL, NULL is not a value it’s the silence between the notes. It’s the unknown, the missing, the unrecorded. IS NULL, IS NOT NULL, COALESCE() these are not just keywords, they’re the tools to give meaning to absence. @indiandataclub @dpdzero
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🚀 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 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 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 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 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 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
Capstone project done!🕵️♀️ Final task of the #21DaysSQLChallenge: solve a CEO murder case using only SQL. From keycard logs to alibis to suspicious calls, every clue came from a query. Huge thanks to @IndianDataClub & @DPDzero for an amazing journey. #SQLWithIDC #SQL
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 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 1 of the IDC 21 Days SQL Challenge! Started my journey with basics: SELECT, filtering, exploring tables & finding unique values with DISTINCT. Day 1 challenge: List all unique hospital services. Big thanks to IDC & @dpdzero for the supportive community!🙌 #SQLWithIDC
Day 13 #SQLWithIDC In SQL and life, INNER JOIN reminds us: True insight emerges where paths intersect. It’s not about gathering all data— But about finding meaningful matches that matter. Real connection, real value. @indiandataclub @dpdzero
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
Day 15/21 SQL Challenge I have some commitments tomorrow, so I’m submitting the challenge a day early. I don’t want to break my streak — losing it would hurt more than a breakup! 😄 #SQLwithIDC with @indiandataclub and @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
I did it! 21 Days of SQL. 21 Days of learning. 21 Days of consistency. Thanks @DPDzero and @IndianDataClub for making the journey so engaging. #SQLWithIDC #SQL #TechCommunity
Day 12 #SQLWithIDC In SQL, NULL is not a value it’s the silence between the notes. It’s the unknown, the missing, the unrecorded. IS NULL, IS NOT NULL, COALESCE() these are not just keywords, they’re the tools to give meaning to absence. @indiandataclub @dpdzero
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 13 #SQLWithIDC In SQL and life, INNER JOIN reminds us: True insight emerges where paths intersect. It’s not about gathering all data— But about finding meaningful matches that matter. Real connection, real value. @indiandataclub @dpdzero
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 1 of the IDC 21 Days SQL Challenge! Started my journey with basics: SELECT, filtering, exploring tables & finding unique values with DISTINCT. Day 1 challenge: List all unique hospital services. Big thanks to IDC & @dpdzero for the supportive community!🙌 #SQLWithIDC
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 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 | #21DaysSQLChallenge with @indiandataclub & @dpdzero Learned conditional logic with CASE WHEN: 🔹 Categorised satisfaction levels 🔹 Grouped staff roles 💡 Challenge: Classify services as Excellent, Good, Fair, or Needs Improvement #SQLWithIDC #SQL #DataAnalytics
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
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 -18/21 Days SQl challenge #SQLwithIDC Getting back to the Grind of SQL with @indiandataclub and @dpdzero
Day 11 #SQLWithIDC 🔶DISTINCT removes duplicates, showing unique rows based on all selected columns. 🔷Use it to clean data and get true unique values. 🔶Remember, for aggregates use GROUP BY, and handle duplicates early for better performance. @indiandataclub @dpdzero
Day 12 ✅ of #21DaysSQLChallenge by @IndianDataClub x @DPDzero Dived into NULL handling. Today’s challenge: compare weeks “With Event” vs “No Event” and analyze satisfaction + morale. Key learning: NULLs change everything—handle them smartly! #SQLWithIDC
Day 10 of the #21DaysSQLChallenge with @indiandataclub! Today’s topic: CASE statements — SQL’s if-else logic.💻 ✅Perfect for adding decisions inside your queries ✅Super handy for creating categories, custom sorting, and conditional summaries!✨ #SQLWithIDC #SQL #Data
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
Something went wrong.
Something went wrong.
United States Trends
- 1. Texas Tech 11K posts
- 2. Purdue 8,067 posts
- 3. The Jupiter 484K posts
- 4. Liverpool 69.3K posts
- 5. Konate 11.7K posts
- 6. #big12championship N/A
- 7. Boozer 3,331 posts
- 8. Jon Pardi N/A
- 9. FINALLY DID IT 680K posts
- 10. Ferrin N/A
- 11. Jeremy Fears 1,040 posts
- 12. The Rock 47.3K posts
- 13. Caleb Foster N/A
- 14. Europe 429K posts
- 15. Mackey 2,651 posts
- 16. Izzo 1,243 posts
- 17. The EU 524K posts
- 18. Gakpo 21.1K posts
- 19. Slot 63K posts
- 20. Iowa State 16.6K posts