Solving Crimes with SQL Queries and MySQL PostgreSQL

🚀 Practicing SQL queries to strengthen my data analysis skills! From basic SELECT statements to applying filters using WHERE, conditions (=, !=, >) and working with multiple columns — every step is helping me understand data better and write efficient queries. Consistency is the key 🔑 — small daily practice leads to big improvements! 💡 Always learning, always growing. #SQL #DataAnalytics #LearningJourney #DataAnalyst #Database #TechSkills #Coding #MySQL #CareerGrowth #PracticeMakesPerfect

Most SQL beginners write queries like this: SELECT * FROM (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) t WHERE t.total > 1000; It works. But it's hard to read. Here's the same query using a CTE: WITH user_revenue AS (  SELECT user_id, SUM(revenue) AS total  FROM orders  GROUP BY 1 ) SELECT * FROM user_revenue WHERE total > 1000; Same result. Way easier to read. So what IS a CTE? Think of it like giving your subquery a name and moving it to the top. That's it. Why? → Your query reads top to bottom like a story → Each step has a clear, meaningful name → You can chain multiple CTEs together → Debugging becomes much easier Bonus: Recursive CTEs let you walk through hierarchical data — like org charts or folder trees — in pure SQL. If nested subqueries are giving you headaches, try CTEs. You won't go back. #SQL #DataAnalytics #DataEngineering #LearningInPublic

🚀 Day 26 – SQL Learning Journey Today’s focus was on one of the most powerful concepts in SQL: Subqueries & Correlated Subqueries 🔍 🔹 Subqueries (Inner Queries) - A query written inside another query - Used to break complex problems into simpler steps - Can be used in "SELECT", "WHERE", or "FROM" clauses - Example use: finding customers with above-average spending 🔹 Correlated Subqueries - A subquery that depends on the outer query - Executes row-by-row, making it more dynamic - Useful for comparisons within groups (like per customer, per store, etc.) 💡 Key Learnings: ✔ Simplified complex filtering logic ✔ Learned when to use subqueries vs joins ✔ Understood performance impact of correlated queries ✔ Practiced real-world scenarios like ranking, filtering, and segmentation 📊 Realizations: Subqueries are great for clarity, but correlated subqueries require careful use due to performance considerations. Consistency is the key 🔥 — one step closer to mastering SQL! #SQL #DataAnalytics #LearningJourney #Subqueries #CorrelatedSubqueries #Day26

#Day_30 of learning SQL in 60 days Topic I covered: What I Practiced Today in MySQL: INNER JOIN Today I spent time strengthening my understanding of INNER JOIN in MySQL by solving multiple practice questions. INNER JOIN helps combine data from two tables by returning only the matching records based on a common column. What I worked on: ✔️ Practiced 10+ INNER JOIN queries ✔️ Combined data from multiple tables using common keys ✔️ Understood how non-matching records are excluded Syntax: SELECT COLUMN_NAME(S) FROM TABLE1 INNER JOIN TABLE2 ON TABLE1.COMMON_COLUMN=TABLE2.COMMON_COLUMN; 📂 I’ve also documented all the practice questions and solutions as part of my learning process. Key Insight: INNER JOIN returns only the intersection of both tables, which makes it essential for accurate data analysis. Consistent practice is helping me build confidence in SQL and understand real-world data relationships better. #SQL #MySQL #InnerJoin #LearningByDoing #DataAnalytics #TechJourney

SQL Progress: Aggregations & Joins! What I learned and practiced today: 1. Aggregate Functions: It’s becoming easier to decide when to use GROUP BY and how to handle multiple aggregations in one query. 2. Data Precision: Handling decimal calculations and rounding is becoming second nature now. It’s all about making the final output clean and professional. I love to learn from you! If you have any tips or a better way to solve these, please type them in the comments! قليل مستمر خير من كثير منقطع #SQL #DataEngineering #PostgreSQL #LeetCode #100DaysOfCode #LearningInPublic #ProblemSolving #DataAnalytics

🚀 Day 26 – SQL Learning Journey Today’s focus was on one of the most powerful concepts in SQL: Subqueries & Correlated Subqueries 🔍 🔹 Subqueries (Inner Queries) - A query written inside another query - Used to break complex problems into simpler steps - Can be used in "SELECT", "WHERE", or "FROM" clauses - Example use: finding customers with above-average spending 🔹 Correlated Subqueries - A subquery that depends on the outer query - Executes row-by-row, making it more dynamic - Useful for comparisons within groups (like per customer, per store, etc.) 💡 Key Learnings: ✔ Simplified complex filtering logic ✔ Learned when to use subqueries vs joins ✔ Understood performance impact of correlated queries ✔ Practiced real-world scenarios like ranking, filtering, and segmentation 📊 Realizations: Subqueries are great for clarity, but correlated subqueries require careful use due to performance considerations. Consistency is the key 🔥 — one step closer to mastering SQL! #SQL #DataAnalytics #LearningJourney #Subqueries #CorrelatedSubqueries #Day26 generate a image and take a example of dataset

🚀 Just Published My New Blog on MySQL! As part of my Data Science journey, I recently worked on a blog explaining MySQL commands using flowcharts — and it really helped me understand concepts more clearly. Instead of memorizing queries, I focused on understanding the flow and purpose behind each SQL command category: 🔹 DDL – Structure 🔹 DML – Data operations 🔹 DCL – Access control 🔹 TCL – Transactions 🔹 DQL – Queries 📊 I also included a simple flowchart and real examples to make it beginner-friendly. Big thanks to my mentor Koduri Srihari and trainer Manohar Chary .V for their guidance and support throughout this learning process 🙌 Grateful to Innomatics Research Labs for providing such structured learning opportunities. 🔗 Read the full blog here: https://lnkd.in/gXVnDhYN Would love to hear your feedback! #MySQL #SQL #DataScience #LearningJourney #BeginnerFriendly #Database #Growth

Advanced SQL ✅ HackerRank doesn't play easy — and neither do I. Window functions, pivots, NULLs — bring it on. 💪 #SQL #HackerRank #DataAnalytics #KeepBuilding

Many people struggle with SQL not because it is difficult, but because they focus on writing queries without understanding how data is structured and connected. You might learn syntax, joins, and functions, but when queries grow complex, things start breaking. Not because SQL is hard, but because the underlying logic is unclear. That is the gap Master SQL with MySQL by Mani Ratnam is designed to address. In this course, you will learn to: • Understand how data is organized and how different tables relate • Build queries step by step instead of memorizing syntax • Work with joins, subqueries, and optimize queries using real datasets With 60+ lessons and hands-on practice, the focus is on thinking in SQL and applying it in real scenarios. Explore the course here:  https://lnkd.in/gNah-JtY #SQL #MySQL #DataAnalytics #LearnSQL #Upskilling

Mastering SQL one query at a time 🚀 Recently practiced using a Common Table Expression (CTE) to calculate total runs scored by each player and present the results in descending order. CTEs make queries more readable, modular, and easier to debug—especially when working with aggregations and complex logic. Small steps like this consistently build stronger data skills 💡 #SQL #LearningSQL #DataAnalytics #DataScience #MySQL #SQLPractice #CTE #Database #Analytics #TechLearning #CodingJourney #DataEngineering