Ayushi kumari’s Post

🚀 Day 49 | LeetCode Learning Journal Today I solved Rank Scores using SQL. This problem introduced me to ranking functions and how to handle duplicate values efficiently in databases! 🔑 Key Points: • Used DENSE_RANK() for ranking • Applied ORDER BY to rank scores in descending order • Understood difference between RANK() and DENSE_RANK() • Ensured no gaps in ranking 🌱 What I Learned: • How ranking functions work in SQL • Handling duplicates while assigning ranks • Writing cleaner queries using window functions • Importance of choosing the right ranking method #LeetCode #100DaysOfCode #DSA #CodingJourney #SQL #Database #Day49 🚀

🚀 Day 9/30 of My LeetCode Journey (SQL Focus) Another day, another step towards mastering SQL! 📊💻 🔹 **SQL Problem of the Day** 👉 *Delete Duplicate Emails* Given a `Person` table, write a query to delete all duplicate emails, keeping only the record with the smallest `id`. 💡 *Key Concept:* Identifying duplicates using self-join / subquery and removing extra records using `DELETE`. Learning not just how to fetch data, but also how to clean and manage it efficiently 🔥 Day 9 done ✅ #LeetCode #30DaysChallenge #SQL #CodingJourney #Consistency #DataCleaning #ProblemSolving #Learning

Day 15 of my DSA journey — SQL Practice Today I focused on SQL and continued working on the SQL 50 study plan on LeetCode. What I covered: • SELECT queries and filtering • JOIN operations (INNER JOIN, LEFT JOIN) • Handling NULL values • GROUP BY with aggregate functions • Writing queries for real-world scenarios Progress: • Completed 23 / 50 SQL problems Key takeaway: SQL is all about understanding the data first, then applying the right operations like filtering, joining, and grouping. Thinking in terms of tables and relationships makes queries much easier to write. #DSA #SQL #LeetCode #Database #CodingJourney #Learning

🚀 Day 45 | LeetCode Learning Journal Today I solved LeetCode 182 – Duplicate Emails using SQL aggregation. This problem helped me understand how to identify repeated data in a table efficiently! 🔑 Key Points: • Used a single Person table • Applied GROUP BY to group emails • Used COUNT() to count occurrences • Filtered duplicate emails using HAVING COUNT(*) > 1 🌱 What I Learned: • How to find duplicate records in SQL • Difference between WHERE and HAVING • Importance of aggregation functions • Writing clean and optimized SQL queries #LeetCode #100DaysOfCode #DSA #SQL #CodingJourney #Day45 🚀

Day 43 | LeetCode Learning Journal 🚀 Today I solved Customers Who Never Order using SQL joins and NULL filtering. This problem helped me understand how to find missing data across related tables! 🔑 Key Points: • Given Customers and Orders tables • Needed to find customers who never placed any order • Used LEFT JOIN to combine tables • Applied NULL check to filter non-ordering customers 🌱 What I Learned: • LEFT JOIN helps identify missing relationships • Importance of checking NULL values in SQL • Real-world use case: finding inactive users/customers • Improved understanding of JOIN + filtering logic #LeetCode #100DaysOfCode #DSA #SQL #CodingJourney #Day43 🚀

🚀 Day 17/30 of My LeetCode Journey (SQL Focus) Another day, another step forward in mastering SQL 📊🔥 🔹 **SQL Problem of the Day** 👉 *Actors and Directors Who Cooperated At Least Three Times* Given an `ActorDirector` table, find all pairs of (actor_id, director_id) who have worked together at least three times. 💡 *Key Concept:* GROUP BY on multiple columns + HAVING COUNT() ≥ 3. Learning how to aggregate data across multiple columns is a powerful skill 💡 Day 17 done ✅ #LeetCode #30DaysChallenge #SQL #CodingJourney #Consistency #ProblemSolving #DataAnalytics #Learning

🚀 Day 51 | LeetCode Learning Journal Today I solved Rising Temperature using SQL. This problem helped me understand how to compare data across rows and work with dates effectively! 🔑 Key Points: • Used SELF JOIN to compare current and previous day • Applied DATEDIFF() to ensure consecutive dates • Checked temperature increase condition • Explored window function (LAG) as an alternative 🌱 What I Learned: • How to compare rows within the same table • Working with date functions in SQL • Importance of handling time-based data • Writing efficient queries using joins and window functions #LeetCode #100DaysOfCode #DSA #CodingJourney #SQL #Database #Day51 🚀

🚀 Day 19/30 of my SQL Problem Solving Challenge 💻 Problem Statement: Find total distance traveled by each user and sort them by highest distance. If distances are equal, sort by name. 🧠 Approach: Used LEFT JOIN to include all users, SUM() with GROUP BY to calculate total distance, and ORDER BY for sorting. Also handled NULL values using COALESCE. ✨ Key Learning: Break the problem into steps, join data, group it, apply aggregation, then sort. Also learned a new function COALESCE, which replaces NULL values by returning the first non-null value (used it here to convert NULL distance into 0). #SQL #30DaysOfSQL #MYSQL #CodingJourney #SDE #ProblemSolving #Streak #DailyLearning

Day 42 | LeetCode Learning Journal 🚀 Today I solved Combine Two Tables using SQL JOIN operations. This problem helped me understand how to combine data from multiple tables efficiently! 🔑 Key Points: • Given two tables: Person and Address • Needed to display firstName, lastName, city, and state • Used LEFT JOIN to combine both tables • Ensured all persons are included, even without address 🌱 What I Learned: • LEFT JOIN includes all records from the left table • Handling NULL values when data is missing • Importance of table relationships using keys • Strengthened basics of SQL joins and real-world data handling #LeetCode #100DaysOfCode #DSA #SQL #CodingJourney #Day42 🚀

🚀 Day 16/30 – SQL Challenge 🔹 Problem: Find the first year each product was sold along with its details. 🔹 Approach: Used MIN(year) with a JOIN to get the correct records. Also explored using ROW_NUMBER(). 🔹 Learning: Avoid tuple IN queries and prefer JOINs or window functions for better results. #SQL #LeetCode #Day16 #CodingJourney #ProblemSolving #SDE