Rohan Singh’s Post

Learning SQL in 2026? Start here! SQL is like asking questions to a big box of data You type a query, and it gives you the answer. Here's how you can learn step by step: 1 Basics - Learn how to pick data using SELECT, WHERE, ORDER BY, GROUP BY. 2 Filtering - Search only what you need with conditions like IN, BETWEEN. 3 Joins Combine data from different tables (INNER, LEFT, RIGHT, FULL). 4 Window Functions - Do smart tricks like ranking, finding next/previous values. 5 Dates - Work with time: today, yesterday, months, years. 6 Advanced Stuff - Play with stats, ML tools, and more. 7 CTES & Subqueries - Break big problems into small easy queries. 8 Speed Tips - Use indexes, don't SELECT *, write clean queries. How to practice: Start with small queries. Use sample data. Try joins and date functions. Solve real-world problems. Do daily practice. ← SQL is not hard. It's like learning a new language. The more you practice, the better you get! Save this if you're learning SQL or share it with your friends #SQL #Dataanalyst #Dataanalysis

Learning SQL in 2025? Start here! SQL is like asking questions to a big box of data 📊 You type a query, and it gives you the answer. Here’s how you can learn step by step: 1️⃣ Basics – Learn how to pick data using SELECT, WHERE, ORDER BY, GROUP BY. 2️⃣ Filtering – Search only what you need with conditions like IN, BETWEEN. 3️⃣ Joins – Combine data from different tables (INNER, LEFT, RIGHT, FULL). 4️⃣ Window Functions – Do smart tricks like ranking, finding next/previous values. 5️⃣ Dates – Work with time: today, yesterday, months, years. 6️⃣ Advanced Stuff – Play with stats, ML tools, and more. 7️⃣ CTEs & Subqueries – Break big problems into small easy queries. 8️⃣ Speed Tips – Use indexes, don’t SELECT *, write clean queries. ✅ How to practice: – Start with small queries. – Use sample data. – Try joins and date functions. – Solve real-world problems. – Do daily practice. 👉 SQL is not hard. It’s like learning a new language. The more you practice, the better you get! ♻️ Save this if you’re learning SQL or share it with a friend who is. Note: Reposting for new-audience #dataanalyst #sql #datascience #dataengineer

Learning SQL in 2026? Start here! SQL is like asking questions to a big box of data 📊 You type a query, and it gives you the answer. Here’s how you can learn step by step: 1️⃣ Basics – Learn how to pick data using SELECT, WHERE, ORDER BY, GROUP BY. 2️⃣ Filtering – Search only what you need with conditions like IN, BETWEEN. 3️⃣ Joins – Combine data from different tables (INNER, LEFT, RIGHT, FULL). 4️⃣ Window Functions – Do smart tricks like ranking, finding next/previous values. 5️⃣ Dates – Work with time: today, yesterday, months, years. 6️⃣ Advanced Stuff – Play with stats, ML tools, and more. 7️⃣ CTEs & Subqueries – Break big problems into small easy queries. 8️⃣ Speed Tips – Use indexes, don’t SELECT *, write clean queries. ✅ How to practice: – Start with small queries. – Use sample data. – Try joins and date functions. – Solve real-world problems. – Do daily practice. 👉 SQL is not hard. It’s like learning a new language. The more you practice, the better you get! ♻️ Save this if you’re learning SQL or share it with a friend who is. Note: Reposting for new-audience Gif credit : sai kumar #dataanalyst #sql #datascience #dataengineer

SQL simplified. 📂✨ Whether it's DQL for data fetching or DML for updates, keeping the fundamentals organized is key. Saving this one for my next technical interview prep! 📝 Which part of SQL do you find the trickiest? For me, it’s nested subqueries! 😅👇 #SQL #Coding #DataAnalytics #TechTips #JavaFullstack

🚨 90% of SQL learners get this wrong — can you solve it? While exploring SQL problems by Ankit Bansal, I came across a deceptively simple question that really tests your understanding of data patterns, not just syntax. 👉 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: You’re given a table that stores daily task results (success / fail). Your challenge is to: ➡️ Identify continuous streaks of the same state ➡️ Merge them into a single row with start date and end date Sounds easy… until you try it 😄 👉 𝐓𝐚𝐛𝐥𝐞 𝐒𝐞𝐭𝐮𝐩: create table tasks ( date_value date, state varchar(10) ); insert into tasks values ('2019-01-01','success'), ('2019-01-02','success'), ('2019-01-03','success'), ('2019-01-04','fail'), ('2019-01-05','fail'), ('2019-01-06','success'); 👉 𝐄𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐎𝐮𝐭𝐩𝐮𝐭: start_date | end_date | state -----------|------------|-------- 2019-01-01 | 2019-01-03 | success 2019-01-04 | 2019-01-05 | fail 2019-01-06 | 2019-01-06 | success 🧠 𝐓𝐡𝐢𝐬 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐬 𝐚 𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐞𝐱𝐚𝐦𝐩𝐥𝐞 𝐨𝐟: Pattern recognition in data Real-world analytics scenarios (streaks, sessions, trends) Thinking beyond basic GROUP BY 💬 Drop your approach in the comments — curious to see different ways to solve this! And if you’ve solved it before, how did you think about it? Shoutout to Ankit Bansal for consistently sharing high-quality SQL problems 🙌 #SQL #DataAnalytics #DataEngineering #InterviewPrep #LearnSQL

“SQL is not hard. It’s just asking questions to data.” That shift in thinking changed everything for me. When I stopped seeing SQL as a “coding language” and started seeing it as a way to *communicate with data*, things became much clearer. Instead of memorizing syntax, I focused on questions like: * Which customers have ordered in the last 7 days? * What products are you selling the most? * Where are we losing revenue? And SQL simply became the tool to get those answers. The real skill isn’t writing complex queries. It’s knowing **what to ask**. If you can think logically and break down problems, you can learn SQL. Start small: SELECT FILTER GROUP And build from there. Don’t overcomplicate it. Data is always talking. SQL just helps you listen. #SQL #DataAnalytics #Learning #BeginnerFriendly #CareerGrowth

Most people think SQL is just about writing queries. But the truth is… It’s about 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝗻 𝗱𝗮𝘁𝗮. When I started learning SQL, I was just memorizing syntax. SELECT this… WHERE that… GROUP BY something. It didn’t work. Everything changed when I understood why each concept exists... not just how to write it. This cheat sheet covers the core building blocks every data analyst actually uses:   • Filtering data (WHERE) • Structuring insights (GROUP BY, HAVING)   • Combining data (JOINS)   • Analyzing patterns (WINDOW FUNCTIONS)   • Handling real-world messy data (NULLs, CASE, CTEs) If you master these, you don’t just write queries… You start solving real business problems. Save this post if you’re learning SQL... you’ll come back to it again and again. Which concept do you struggle with the most? #SQL #DataAnalytics #DataAnalyst #LearnSQL #DataScience #Analytics #TechSkills #CareerGrowth

Day 8 of learning SQL 🚀 Today I focused on working with string functions in SQL and understanding how to clean and transform text data. It may look simple, but I realized how powerful string operations are in real-world data analysis. Topics I covered: ✔ CONCAT – combining multiple columns into one ✔ LOWER / UPPER – standardizing text format ✔ LENGTH – measuring text data ✔ TRIM – removing unnecessary spaces ✔ LEFT / RIGHT / SUBSTRING – extracting specific parts of text ✔ REPLACE – modifying values inside strings Example I practiced: SELECT CONCAT(LOWER(first_name), '.', LOWER(last_name), '@gmail.com') AS email FROM employees; Key learning today 💡 Data is often messy, and cleaning it is a crucial step Small transformations can make data more usable and consistent String functions are essential for real-world datasets Every day I’m getting more comfortable thinking in SQL and solving problems step by step. Goal: Become job-ready in SQL & Data Analysis 💪 #SQL #DataAnalytics #LearningInPublic #100DaysOfCode #Consistency

SQL is one of those skills where the basics can take you far—but mastering the right functions is what truly sets you apart. Writing efficient queries isn’t about complexity; it’s about knowing what to use and when. Functions like COALESCE, CASE, and window functions such as ROW_NUMBER and RANK are incredibly powerful and widely used in real-world scenarios. Over time, I’ve realized that strong SQL skills are not about memorizing syntax—they’re about thinking in terms of data transformation: • How do you handle null values? • How do you rank or deduplicate records? • How do you turn raw data into meaningful insights? The more you practice these concepts in real-world situations, the more natural SQL becomes. At the end of the day, SQL isn’t just a query language—it’s the foundation of how we work with data. 📌 Save this post for later 🔁 Repost if you found this helpful 🔔 Follow Gautam Kumar for more insights on Data Science and Analytics Credit: Respective Owner #SQL #DataAnalytics #DataScience #SQLTips #DataEngineering #BusinessIntelligence #Analytics #LearnSQL #DataTransformation #TechCareers

🚀 Back to My Data Science Journey! A few months ago, I started learning SQL. Now I’m continuing with more clarity and consistency 💪 📊 SQL Flow – Understanding the Order While revising, I understood the practical flow of SQL: 1️⃣ DDL → Create structure (CREATE, ALTER) 2️⃣ DML → Insert/modify data (INSERT, UPDATE) 3️⃣ DQL → Retrieve data (SELECT) 4️⃣ TCL → Save changes (COMMIT) 💡 Learning: SQL is not random—it follows a logical flow. This time, I’m focusing more on strong fundamentals and practice 🚀 #SQL #DataAnalytics #LearningJourney #Restart