SQL Challenge: Churn Risk Customers

❄️ LeetCode Daily Challenge 📅 Day 19 of 50 Days SQL Challenge Continuing my SQL journey with another Hard-level SQL problem focused on session analytics and behavioral anomaly detection. 📌 Problem: Find Zombie Sessions 🔗 Problem Link: https://lnkd.in/gDEr9VEq 💡 Problem Summary: We need to identify zombie sessions — sessions where users appear active but show abnormal behavior patterns. A session qualifies as a zombie session if it satisfies ALL conditions: ✔ Session duration > 30 minutes ✔ At least 5 scroll events ✔ Click-to-scroll ratio < 0.20 ✔ No purchases during the session Finally, return the result ordered by: scroll_count (DESC), session_id (ASC) The idea was to aggregate event-level data into session-level metrics, then filter sessions based on behavioral thresholds. Another Hard SQL challenge completed — consistency continues 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #Database #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

❄️ LeetCode Daily Challenge 📅 Day 29 of 50 Days SQL Challenge Today’s challenge was a perfect blend of time-based filtering, aggregation, and business logic — exactly what we deal with in real-world data scenarios. 📌 Problem: Find Golden Hour Customers 🔗Problem Link: https: https://lnkd.in/gPyRUtSG 💡 Problem Breakdown: Identify golden hour customers who: ✔ Placed at least 3 orders ✔ ≥ 60% of orders during peak hours (11:00-14:00 or 18:00-21:00) ✔ Have an average rating ≥ 4.0 29 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 Drop your approach below 👇 #LeetCode #SQL #DataEngineering #Analytics #CustomerInsights #SQLPractice #WindowFunctions #LearningInPublic #50DaysChallenge #DataAnalytics

❄️ LeetCode Daily Challenge 📅 Day 28 of 50 Days SQL Challenge Continuing my SQL consistency journey with a Medium-level SQL problem focused on user behavior analysis and aggregation. 📌 Problem: Find Emotionally Consistent Users 🔗 Problem Link: https://lnkd.in/gwAXyQGJ 💡 Problem Summary: Identify users who are emotionally consistent based on their reactions: ✔ At least 5 reactions ✔ At least 60% of reactions are the same type ✔ Return dominant reaction + ratio 28 days of consistent SQL practice completed ✅ Consistency is turning into clarity 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #UserBehavior #DataAnalysis #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

❄️ LeetCode Daily Challenge 📅 Day 40 of 50 Days SQL Challenge Today’s challenge focused on subscription funnel analysis — a common real-world business analytics use case 📈 📌 Problem: Analyze Subscription Conversion 🔗Problem Link: https://lnkd.in/gSEZWxJb 💡 Problem Breakdown: Identify users who converted from free trial to paid subscription: ✔ Find users who moved from free_trial to paid ✔ Calculate average daily activity duration during free trial ✔ Calculate average daily activity duration during paid period ✔ Round averages to 2 decimal places 40 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 💬 How would you approach this? Single query with conditional aggregation or separate CTEs? Drop your thoughts below 👇 #LeetCode #SQL #DataEngineering #AzureDataEngineer #ProductAnalytics #SQLPractice #LearningInPublic #50DaysChallenge #DataAnalytics

🚀 Day 25/50 of My #LeetCode SQL Challenge Today I solved “Product Sales Analysis III” 💡 This problem was about identifying the first year each product was sold and analyzing the corresponding sales data. 🔍 Key Learnings: How to identify the earliest occurrence of data within groups Strengthening concepts around data grouping and filtering Understanding how to extract meaningful insights from historical records 📊 Real-world relevance: This type of analysis is useful for: Tracking product launch performance Analyzing early sales trends Making data-driven business decisions ✨ Takeaway: Finding the first occurrence in data is a powerful technique that appears frequently in analytics and interviews. Consistency matters — one problem a day keeps improvement on track 💪 #Day25 #LeetCode #SQL #DataAnalytics #LearningInPublic #100DaysOfCode

❄️ LeetCode Daily Challenge 📅 Day 21 of 50 Days SQL Challenge Continuing my SQL consistency journey with a Medium-level SQL problem focused on joins and conditional aggregation. 📌 Problem : Market Analysis I 🔗 Problem Link : https://lnkd.in/gK6uAEEJ 💡 Problem Summary : For each user, we need to find: ✔ Their join date ✔ Number of orders they made as a buyer in 2019 Important: Include users with 0 orders Count only orders from 2019 21 days of SQL practice done ✅ Consistency is building real confidence now 💪 Let’s grow one query at a time 🚀 #LeetCode #SQL #DataEngineering #Analytics #Database #WindowFunctions #DailyPractice #LearningInPublic #50DaysChallenge

💡 Challenge: Write an SQL query to identify numbers that appear 3 or more times consecutively. 🔥 Let’s make it interactive: Drop your SQL solution in the comments 👇 Try solving using Window Functions 🧠 Bonus: Can you solve it without using LAG() or LEAD()? 💬 Why this matters? This type of problem tests your understanding of: Sequential data patterns Window functions Real-world scenarios like user activity tracking 📈 🔗 If you're learning SQL & Data Analytics, I regularly share problems like this! #SQL #DataAnalytics #LeetCode #LearningInPublic #TechChallenge #DataScience #CareerGrowth

Day 10/50 – #SQLChallenge 🚀 Solved “Managers with at Least 5 Direct Reports” problem on LeetCode. ✅ Approach: Used subquery with GROUP BY and HAVING ✅ Key Concept: Aggregating data to filter groups based on count 💡 Advanced Insight: The HAVING clause is applied after grouping, making it ideal for filtering aggregated results (like COUNT). This is different from WHERE, which filters rows before grouping. Understanding this distinction is key when working with real-world data. 🔍 Takeaway: Combining subqueries with aggregation helps solve hierarchical data problems like identifying managers and their reporting structure. 10 days of consistency — building strong fundamentals 💪 #SQL #LeetCode #Database #CodingChallenge #ProblemSolving #LearningInPublic #DeveloperJourney

❄️ LeetCode Daily Challenge 📅 Day 38 of 50 Days SQL Challenge Today’s problem focused on event sequencing — identifying recovery patterns from time-series data. 📌 Problem: Find COVID Recovery Patients 🔗Problem Link: https://lnkd.in/gcRgygqH 💡 Problem Breakdown: Identify patients who have recovered: ✔ Must have at least one Positive test ✔ Followed by a Negative test on a later date ✔ Ignore inconclusive results ✔ Calculate recovery time = first negative after first positive 38 days of consistent SQL practice completed ✅ Daily practice is turning concepts into intuition 💪 Let’s grow one query at a time 🚀 💬 How would you approach this? Using window functions or aggregation + joins? Drop your thoughts below 👇 #LeetCode #SQL #DataEngineering #Analytics #HealthcareAnalytics #SQLPractice #LearningInPublic #50DaysChallenge #AzureDataEngineer

Day 1 of posting about Data Analytics. I spent some time today diving back into Common Table Expressions (CTEs) and I’m reminded of how much they transform the way we handle complex data. While subqueries get the job done, CTEs bring a level of readability and structure that is hard to beat. By using the WITH clause, you can break down intricate logic into "virtual" tables that make your scripts much easier to debug and maintain. I created a simple customer orders database & created a CTE for high value orders. #DataAnalytics #Buildinginpublic #SQL #Techcommunity #Datascience