SQL Cheatsheet for Data Analysts

Still using basic GROUP BY for everything? You’re leaving SQL power on the table 👀 Window functions are what separate beginners from real data analysts/data engineers. Master these essentials: ✅ ROW_NUMBER() → assign unique row IDs ✅ RANK() → rank rows with ties ✅ LAG() → compare with previous rows ✅ NTILE() → split data into buckets ✅ SUM() OVER() → running totals These functions show up in: 📌 SQL interviews 📌 Data analyst tasks 📌 Business reporting 📌 Real-world dashboards Learn them once → use them forever. Save this before your next SQL interview 💻🔥 #SQL #DataAnalytics #DataEngineer #SQLInterview #LearnSQL #DataScience #BusinessAnalytics #TechCareers #Programming #DataAnalyst #SQLTips

Back with another round! 📊 Some of you might remember my last revision series, it helped me a lot. So I am doing it again, but this time with slightly advanced topics. Starting with SQL, where I will be covering more than just the basics. Think joins, subqueries, window functions and how to actually use them in real scenarios. What is different this time? - Going deeper into concepts I skipped before - Sharing practical examples from actual projects - Focusing on interview prep and real world applications I am doing this because the more I learn about data analytics, the more I realize how much there is to know. And honestly, teaching others is the best way to learn yourself. If you're also preparing for data roles or want to brush up your SQL skills, follow along! And if you have been working with data for a while, drop your favorite SQL tip below, I'd love to learn from you. Here we go again! 💪 #DataAnalytics #SQL #LearningTogether #DataJobs

Day 14/30 🚀 Today I learned something powerful in SQL — 👉 Correlated Subqueries At first, it felt confusing… But then it clicked 💡 Instead of calculating once, it runs for every row — making it perfect for real-world comparisons. 📌 Example: Find employees earning more than their department average This concept is a game-changer for: ✔ Data Analyst interviews ✔ Writing smarter SQL queries ✔ Understanding row-level logic ⚡ Bonus learning: You can optimize it using JOINs for better performance! #SQL #DataAnalytics #LearnSQL #DataAnalyst #LinkedInLearning

🚀 Struggling with SQL in interviews? 😓 SQL Cheat Sheet Every Data Analyst Should Know! If you're starting your journey in Data Analytics or brushing up your skills, mastering SQL is a must 💡 I created this quick cheat sheet to help you revise the most important concepts: ✔️ Basics (SELECT, WHERE, LIMIT) ✔️ Conditions (AND, IN, LIKE) ✔️ Aggregations (COUNT, SUM, AVG) ✔️ Joins (INNER, LEFT, RIGHT) ✔️ Advanced (ROW_NUMBER, CASE WHEN, CTE) 📊 Whether you're preparing for interviews or working on real-world projects, this can be your go-to quick reference! Consistency + Practice = SQL Mastery 🔥 #SQL #DataAnalytics #DataScience #Learning #CareerGrowth #TechSkills #DataEngineer #LearningStage

Question 1: How do you select all columns from a table named 'employees'?" Level: Basic. Roles: Data analyst or any junior role. Interviewer's Goal: Verifying that you know the absolute basics of SQL querying. Comment: OK, as SQL questions go, this is the very basics. In a driving test, this is the equivalent of ‘Are you able to get in the car and sit facing the right way?’ If you don’t know this, you do really need to go and study from the very beginning. Any ‘intro to SQL’ on YouTube will do the trick. Answer: SELECT * FROM employees; For additional bonus points, you could mention that using SELECT * isn't best practice in production code. It’s better for maintenance and performance to explicitly list the actual columns needed. The above is the first of 33 common SQL questions you might be asked in a technical interview for a SQL-based role. There are 32 more like these - although to be fair, the rest should be a bit more challenging than this! There's a link in the comments to get your free copy today. #sql #learnsql #sqlbook #freebook #sqlinterview

Day 10 of Learning SQL for Data Analytics 🚀 Most people skip LEFT JOIN… and regret it in interviews. Today I learned one of the most used JOINs in real-world projects 👇 What is LEFT JOIN? ✅ All rows from the LEFT table — always ➕ Matching rows from the RIGHT table ❌ No match? → RIGHT side becomes NULL Real example: 4 employees → only 2 have valid dept_id LEFT JOIN still returns all 4 rows John & Priya get NULL because no department matched Key difference from INNER JOIN: 🔵 INNER JOIN → drops unmatched rows 🟣 LEFT JOIN → keeps ALL left rows, NULLs the rest When to use it? → All customers + their orders (even if no order placed) → All employees + department info (even unassigned) → Finding missing data: WHERE right.col IS NULL 💡 Insight: LEFT JOIN never loses rows from the left table. NULL is not an error — it's information. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ Day 7 ✅ Day 8 ✅ Day 9 ✅ Day 10 ✅ #SQL #Day10 #DataAnalytics #LearningInPublic #DataAnalyst #SQLJoins #LeftJoin #CareerGrowth

Level Up Your SQL Skills with Real-Time Practice! I’m excited to share a resource I’ve put together: 📘 Real-Time SQL Questions to Practice (Beginner → Advanced) Whether you're starting your journey as a Data Analyst or preparing for interviews, this document is designed to help you: ✅ Strengthen core SQL concepts ✅ Practice real-world scenarios ✅ Improve problem-solving skills ✅ Gain confidence for interviews 💡 Consistent practice is what transforms knowledge into expertise — and SQL is no exception. If you're serious about mastering SQL, this guide can be a great addition to your learning path. Let me know your thoughts or your favorite SQL problem in the comments #SQL #DataAnalytics #DataAnalyst #LearnSQL #SQLPractice #InterviewPreparation #DataEngineering #Analytics #CareerGrowth #TechLearning Magudeswaran | Ajay Babu | Kaviya | Manikanta | Srinivasareddy | Sreethar M B | Suresh | Maureen Direro | Krishnakanth | Gopi Krishna | Satya Sekhar | RAMA | Santosh J. | Mahesh | Sabyasachi | Sainatha | Veeresh | Shafque | Anirban

🚀 My SQL Learning Journey So Far Over the past few weeks, I’ve been consistently working on strengthening my SQL skills as part of my transition into the Data Analytics field. Here’s what I’ve covered: ✔ SQL Basics & Data Handling ✔ Joins & Real-world Relationships ✔ Subqueries & Correlated Queries ✔ Window Functions (RANK, LAG, LEAD, NTILE) ✔ CTE (WITH Clause) ✔ Views & Index Basics What I’m proud of: 🔹 Solving real-world business problems using SQL 🔹 Understanding not just “how” but “why” behind queries 🔹 Building strong problem-solving confidence 📊 Current Level: Intermediate (moving towards Advanced) Next step: Applying these skills in real projects and preparing for interviews. Would love to connect with professionals in the data field and learn from your experiences! #SQL #DataAnalytics #CareerTransition #LearningInPublic #OpenToOpportunities

𝙈𝙤𝙨𝙩 𝙥𝙚𝙤𝙥𝙡𝙚 𝙜𝙚𝙩 𝙘𝙤𝙣𝙛𝙪𝙨𝙚𝙙 𝙗𝙮 𝙎𝙌𝙇 𝙅𝙊𝙄𝙉𝙨. 𝗜𝗡𝗡𝗘𝗥? 𝗟𝗘𝗙𝗧? 𝗥𝗜𝗚𝗛𝗧? 𝗙𝗨𝗟𝗟? 𝗖𝗿𝗼𝘀𝘀? 𝗪𝗵𝗮𝘁 𝗲𝘃𝗲𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? I used to feel the same way. But this beginner-friendly PDF makes it crystal clear. Here’s what’s inside ✅ Simple breakdowns of each JOIN ✅ Clear examples to solidify your understanding If you're a → Data Analyst → Data Engineer → Or just someone brushing up on SQL... This is worth saving. I’ve attached the PDF for you here. #SQL #DataEngineering #DevopsLearning #LearningTogether #CareerGrowth

You voted. 44% wanted Advanced SQL. So this week — 3 Advanced SQL deep dives, 3 Interview Case Studies, 1 Deep Learning. Following the poll exactly. Day 1: Window Functions. Most data analysts can write GROUP BY. Few can write a window function fluently. That gap is what separates "junior" from "ready for senior" in SQL interviews. The 5 window functions you actually need: → ROW_NUMBER() — assigns 1, 2, 3 within a group (no ties) → RANK() — handles ties with gaps (1, 2, 2, 4) → DENSE_RANK() — handles ties without gaps (1, 2, 2, 3) → LAG() / LEAD() — compare with previous/next row (period-over-period) → SUM() OVER (PARTITION BY ... ORDER BY ... ROWS UNBOUNDED PRECEDING) — running totals The real interview question isn't "what does RANK do." It's "rank customers by total spending within each city, but break ties by signup date." That tests: 1. Did you partition correctly? 2. Did you order correctly? 3. Did you pick the right ranking function? Free notebook covers all 5 with side-by-side examples on a real database: https://lnkd.in/g2hzWJyi Day 1 of 7. Tomorrow: the SQL interview question I've seen at every Tier-1 company. #SQL #WindowFunctions #DataAnalyst #InterviewPrep #AdvancedSQL #DataAnalytics #FreeResources #SQLInterview