#sqlwithidc resultados da pesquisa
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 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 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 -18/21 Days SQl challenge #SQLwithIDC Getting back to the Grind of SQL with @indiandataclub and @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 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
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 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 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
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 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 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 09/21dayssqlchallenge #SQLwithIDC SELECTservice,COUNT(patient_id) AS patient_count, AVG(DATEDIFF(departure_date, arrival_date)) AS avg_length_of_stay FROM PATIENTS GROUP BY service HAVING AVG(DATEDIFF(departure_date, arrival_date)) > 7 ORDER BY avg_length_of_stay DESC;
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 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 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
#SQLWithIDC #portfolioshowcase #portfolio #presentation #businesspresentation #storytelling #DataAnalytics #21DayChallenge #learnsql @indiandataclub @dpdzero
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
The perfect end to the 21-day journey. SQL isn’t just about queries — it’s problem-solving, storytelling & logic. Proud to have completed this challenge! 🚀 @indiandataclub @dpdzero #SQLWithIDC #SQLMystery #21DaysSQLChallenge #DataAnalytics
🕵️♂️ Capstone Completed! | SQL Murder Mystery- “Who Killed the CEO?” Wrapped up the 21-Day SQL Challenge by @indiandataclub with a final investigation project where I solved a murder case using SQL, not fingerprints. @dpdzero #21DaysSQLChallenge #SQLWithIDC
🌟 21 Days. 21 Chances to Grow. I just earned my 21-Day SQL Challenge Badge with #SQLWithIDC! 🚀 It’s not just a badge — it’s proof of showing up every day, learning, practicing, and building consistency. 💫 💭 Lesson: Progress > Perfection. Keep going, every day counts!
🕵️♀️ SQL Murder Mystery — Solved! ➡️All clues point to David Kumar - the only one in the CEO office during the crime, a false alibi, a suspicious call at 8:55 PM, and matching fingerprints. Sponsored by @DPDzero | Organized by @IndianDataClub #SQLWithIDC #21DaysSQLChallenge
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
CapStone:I uncovered the killer behind TechNova Inc.’s CEO murder. 🔍 Killer Identified: David Kumar 📊 Skills Used: JOINs, time filtering, CTEs, pattern tracing SQL isn’t just queries — it’s detective work. Linkedin:linkedin.com/feed/update/ur… #SQLWithIDC @indiandataclub @dpdzero
✅Day 21 of #SQLWithIDC Challenge Complted! sponsored by @dpdzero & organized by @indiandataclub. 💡Today’s concept: CTEs — the best way to simplify complex SQL into clean, readable steps. 🎯 Challenge: Build a hospital performance dashboard using 3 CTEs + final performance.
🗓️ 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 21 — #21DaysSQLChallenge (Final Day!) 🚀 Wrapped up the challenge by building a full performance dashboard 1️⃣ Patient metrics 2️⃣ Service metrics 3️⃣ Staff metrics 21 days done — SQL muscles upgraded 💪 @dpdzero @indiandataclub #SQLWithIDC #SQL #DataAnalytics #Day21
Day 21. Final day of the 21 Days of SQL Challenge. I built a dashboard with CTEs combining service metrics, staff metrics, and patient demographics, then computed a weighted performance score. @indiandataclub @dpdzero #SQL #SQLWithIDC
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 21/21 – 𝐒𝐐𝐋 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞🚀 🔸Learned CTEs today — clean, readable queries with step-by-step logic. 🔸Perfect for breaking down complex reports easily. 🔥 🔸CTEs > nested subqueries any day! ⚡ #SQLWithIDC #CTE #IndianDataClub #DPDzero
✅ Completed Day 20 - Window Functions Sponsored by @dpdzero & Organized by @indiandataclub. - Worked with running totals, moving averages, and trend analysis using SUM() OVER & AVG() OVER. - Analyzed weeks 10–20 for admissions + satisfaction trends. 🔍 #SQLWithIDC
Today’s Learning : SQL window functions. SUM() OVER for running totals, AVG() OVER for moving averages, and ROWS BETWEEN to control the window. Helps analyze trends without losing rows. #sqlwithidc #sqllearning #indiandataclub
🗓️ Day 20 of #21DaysSQLChallenge by @indiandataclub! Topic: Aggregate Window Functions! Key idea: calculate totals without losing rows Frame controls: UNBOUNDED PRECEDING = from start N PRECEDING = last N rows CURRENT ROW = this row N FOLLOWING = next N rows #SQLWithIDC #SQL
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 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 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 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 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 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 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 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 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 & @dpdzero 🔹 Topic: NULL Handling Learned how to work with missing data using IS NULL, IS NOT NULL, and COALESCE() to replace NULLs and ensure accurate filtering and calculations. #SQLWithIDC #LearnSQL #IDCChallenge
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 -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 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 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
Something went wrong.
Something went wrong.
United States Trends
- 1. Texas Tech 25.9K posts
- 2. Messi 174K posts
- 3. Ty Simpson 1,727 posts
- 4. #SECChampionship 2,286 posts
- 5. Inter Miami 48.7K posts
- 6. Ryan Williams 1,138 posts
- 7. #MLSCupFinal 1,291 posts
- 8. Dawgs 8,174 posts
- 9. Harry Ford 1,281 posts
- 10. Slot 120K posts
- 11. Big 12 2,829 posts
- 12. Gunner 5,845 posts
- 13. Busquets 13.3K posts
- 14. NDSU 1,305 posts
- 15. Mariners 3,184 posts
- 16. Jordi Alba 8,444 posts
- 17. Liverpool 121K posts
- 18. Ferrer 3,113 posts
- 19. Illinois State 7,994 posts
- 20. #RollTide 1,945 posts