#21daysofsql search results

🚀 Day 12 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on handling NULL values 🧩 Learned IS NULL, IS NOT NULL, and COALESCE to clean & manage missing data. 🎯 Challenge: Compare weeks with vs without events → avg satisfaction & staff morale #SQL #DataAnalytics #Day12

🚀 Day 10 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored CASE statements for conditional logic Perfect for categorizing data & creating dynamic columns! 🎯Challenge: Build a service report with avg satisfaction → tag as Excellent, Good, Fair, or Needs Improvement #Data

🚀 Day 9 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored date functions: extraction, date math & filtering Useful for length-of-stay, month/year grouping, & timelines. 🎯 Challenge: Avg stay (days) per service → show only >7 days + patient count (DESC). #SQL #Data #day9

7-Day SQL Learning Streak! Proud to stay consistent in the #21DaysOfSQL challenge by @IndianDataClub x @DPDzero 🔥 Every single day is helping me sharpen my data mindset and SQL fundamentals. On to Day 8 🚀 #SQLWithIDC #DataAnalytics #LearningJourney #Consistency

🚀 Day 8 | #21DaysOfSQL by @IndianDataClub x @DPDzero Worked with string functions: UPPER, LOWER, LENGTH, CONCAT, SUBSTRING ✨ Great for cleaning + formatting text data. 🎯 Challenge: Build patient summary (name formatting, age group, length > 10). #SQL #DataAnalytics #Day8

🚀 Day 7 | #21DaysOfSQL by @IndianDataClub x @DPDzero Covered HAVING — used to filter results after GROUP BY 🔍 Perfect for conditions on aggregates like COUNT & AVG. 🎯 Challenge: Find services with >100 refusals & avg satisfaction < 80. #SQL #DataAnalytics #Day7

🚀 Day 6 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored GROUP BY to summarize data by category 📊 Great for counts, averages & service-level insights. 🎯 Challenge: For each service → total admitted, refused & admission rate (sorted DESC). #SQL #DataAnalytics #Day6

🚀 Day 5 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on aggregates: COUNT, SUM, AVG, MIN, MAX 📊 Great for totals, averages & min–max insights. 🎯 Challenge: Find total admitted, total refused & avg satisfaction (rounded). #SQL #DataAnalytics #Day5

🚀 Day 4 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored LIMIT + OFFSET for pagination 📄 🔹 LIMIT → restrict rows 🔹 OFFSET → skip rows 🔹 Better with ORDER BY 🎯 Challenge: Get 3rd–7th highest satisfaction scores (patient_id, name, service, satisfaction). #SQL #Day4

🚀 Day 3 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on ORDER BY for sorting 📊 🔹 Sort by age (DESC) 🔹 Weekly data by week ASC + requests DESC 🔹 Staff sorted A→Z 🎯 Challenge: Top 5 weeks with highest patient refusals 🔍 #SQL #DataAnalytics #Day3

🚀 Day 2 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on mastering the WHERE clause 🔍 🧠 Learned filtering with =, !=, AND, OR, IN, BETWEEN, LIKE 🎯Challenge: Find patients in Surgery with satisfaction < 70 🏥 🔗 Repo: github.com/gagan8605/IDC_… #SQL #DataAnalytics #Day2

🚀 Day 1 | #21DaysOfSQL by @IndianDataClub x @DPDzero Learned the power of SELECT — the first step in talking to data! 🧠 SELECT * | Columns | LIMIT | DISTINCT 🎯 Challenge: List unique hospital services 🏥 📊 Repo Link | 🔗 github.com/gagan8605/IDC_… #SQL #DataAnalytics #Day1

Day 9 of #21daysofsql Solved 2 questions One was moderate and second was difficult level (2024:01:17)

Day 8 of #21daysofsql In 8th section solved 5 questions easy and moderate level (16:01:2024)

🚀 Day 10 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored CASE statements for conditional logic Perfect for categorizing data & creating dynamic columns! 🎯Challenge: Build a service report with avg satisfaction → tag as Excellent, Good, Fair, or Needs Improvement #Data

🚀 Day 2 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on mastering the WHERE clause 🔍 🧠 Learned filtering with =, !=, AND, OR, IN, BETWEEN, LIKE 🎯Challenge: Find patients in Surgery with satisfaction < 70 🏥 🔗 Repo: github.com/gagan8605/IDC_… #SQL #DataAnalytics #Day2

7-Day SQL Learning Streak! Proud to stay consistent in the #21DaysOfSQL challenge by @IndianDataClub x @DPDzero 🔥 Every single day is helping me sharpen my data mindset and SQL fundamentals. On to Day 8 🚀 #SQLWithIDC #DataAnalytics #LearningJourney #Consistency

🚀 Day 7 | #21DaysOfSQL by @IndianDataClub x @DPDzero Covered HAVING — used to filter results after GROUP BY 🔍 Perfect for conditions on aggregates like COUNT & AVG. 🎯 Challenge: Find services with >100 refusals & avg satisfaction < 80. #SQL #DataAnalytics #Day7

🚀 Day 12 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on handling NULL values 🧩 Learned IS NULL, IS NOT NULL, and COALESCE to clean & manage missing data. 🎯 Challenge: Compare weeks with vs without events → avg satisfaction & staff morale #SQL #DataAnalytics #Day12

🚀 Day 8 | #21DaysOfSQL by @IndianDataClub x @DPDzero Worked with string functions: UPPER, LOWER, LENGTH, CONCAT, SUBSTRING ✨ Great for cleaning + formatting text data. 🎯 Challenge: Build patient summary (name formatting, age group, length > 10). #SQL #DataAnalytics #Day8

🚀 Day 4 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored LIMIT + OFFSET for pagination 📄 🔹 LIMIT → restrict rows 🔹 OFFSET → skip rows 🔹 Better with ORDER BY 🎯 Challenge: Get 3rd–7th highest satisfaction scores (patient_id, name, service, satisfaction). #SQL #Day4

🚀 Day 1 | #21DaysOfSQL by @IndianDataClub x @DPDzero Learned the power of SELECT — the first step in talking to data! 🧠 SELECT * | Columns | LIMIT | DISTINCT 🎯 Challenge: List unique hospital services 🏥 📊 Repo Link | 🔗 github.com/gagan8605/IDC_… #SQL #DataAnalytics #Day1

🚀 Day 5 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on aggregates: COUNT, SUM, AVG, MIN, MAX 📊 Great for totals, averages & min–max insights. 🎯 Challenge: Find total admitted, total refused & avg satisfaction (rounded). #SQL #DataAnalytics #Day5

🚀 Day 3 | #21DaysOfSQL by @IndianDataClub x @DPDzero Focused on ORDER BY for sorting 📊 🔹 Sort by age (DESC) 🔹 Weekly data by week ASC + requests DESC 🔹 Staff sorted A→Z 🎯 Challenge: Top 5 weeks with highest patient refusals 🔍 #SQL #DataAnalytics #Day3

🚀 Day 6 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored GROUP BY to summarize data by category 📊 Great for counts, averages & service-level insights. 🎯 Challenge: For each service → total admitted, refused & admission rate (sorted DESC). #SQL #DataAnalytics #Day6

🚀 Day 9 | #21DaysOfSQL by @IndianDataClub x @DPDzero Explored date functions: extraction, date math & filtering Useful for length-of-stay, month/year grouping, & timelines. 🎯 Challenge: Avg stay (days) per service → show only >7 days + patient count (DESC). #SQL #Data #day9