SQL Interview Questions for Data Analysts

SQL Real Scenario Questions Every Data Analyst Should Know 💡 I’ve compiled 10 important SQL questions with answers that are commonly asked in interviews and real projects. If you're preparing for Data Analyst roles, this will help you strengthen your fundamentals. Save this for later and keep practicing 🚀 #SQL #DataAnalytics #InterviewPrep #DataAnalyst #Learning #CareerGrowth

🚀 Advanced SQL Challenge (Data Analyst Level) If you can solve this cleanly, you're interview-ready 👇 💡 Scenario: You have an "orders" table: - order_id - customer_id - order_date - amount 👉 Task: Write a query to find customers whose latest order amount is higher than their average order amount. ⚠️ Constraints: - Only consider customers with at least 3 orders - Handle duplicate order_date correctly (latest = max date, but ties possible) - Output: - customer_id - latest_order_amount - avg_order_amount --- 🔥 Bonus (real challenge): - Solve using window functions only (no subqueries) - Then rewrite using CTEs - Optimize for large datasets (millions of rows) --- 💭 Why this matters: This tests: - Window functions - Aggregation logic - Real-world thinking (not just syntax) Most candidates fail here — not because SQL is hard, but because thinking in data is. 👇 Drop your query. I’ll review the best ones. #SQL #DataAnalytics #AdvancedSQL #DataAnalyst #InterviewPrep #Analytics #LearnSQL

🚀 Day 12 – Data Analyst Journey Today I focused on advanced SQL querying techniques, especially combining data from multiple tables and performing analytical operations. 🗄️ SQL Concepts Covered: 🔗 SQL Joins: - INNER JOIN – returns matching records from both tables - LEFT JOIN – returns all records from left table + matched from right - RIGHT JOIN – returns all records from right table + matched from left 📊 Window Functions (Advanced SQL 🔥): Learned how to perform calculations across a set of rows without grouping them. 📌 Types of Window Functions: 1️⃣ Ranking Functions: - "ROW_NUMBER()" - "RANK()" - "DENSE_RANK()" 2️⃣ Aggregate Window Functions: - "SUM()" - "AVG()" - "COUNT()" - "MIN()" - "MAX()" 3️⃣ Value Functions: - "LAG()" - "LEAD()" - "FIRST_VALUE()" - "LAST_VALUE()" 4️⃣ Distribution Functions: - "NTILE()" 💡 Also understood how to use "OVER()" clause with "PARTITION BY" and "ORDER BY" to control window behavior. 💡 Today’s learning helped me analyze data at a deeper level without losing row-level details — a key skill for real-world data analysis. #SQL #DataAnalytics #WindowFunctions #LearningJourney #FutureDataAnalyst #PlacementPrep

Day 7 of my Data Analyst Journey Today I moved beyond basic queries and started working with a more realistic dataset- World Wide Importers. Instead of isolated queries, I focused on understanding how data actually connects across tables and how to extract meaningful insights. Practiced combining multiple tables using JOINs Applied filtering to get relevant business data Started thinking in terms of questions -> data -> insights One small realization today: Writing SQL is not just about syntax - it's about asking the right questions. Slowly building the habit of thinking like a data analyst. #DataAnalytics #SQL #LearningInPublic #CareerSwitch

🚀 SQL Cheat Sheet Every Data Analyst Should Know If you're in Data Analytics / BI / Data Engineering, SQL isn’t optional — it’s your core skill. Here’s what actually matters 👇 🔹 Filtering & Cleaning → WHERE, IN, BETWEEN, LIKE 🔹 Aggregation → GROUP BY, HAVING, SUM, COUNT, AVG 🔹 Sorting & Dedup → ORDER BY, DISTINCT, LIMIT 🔹 Joins (Must-Know 💯) → INNER, LEFT, RIGHT, FULL 🔹 Advanced (Game Changer 🚀) → CASE WHEN, ROW_NUMBER, CTE, SUBQUERY 💡 Pro Tip: Syntax is basic. Knowing when to use what is what sets you apart. 📌 Perfect for: • Data Analyst interviews • SQL coding rounds • Real-world dashboarding 💬 Comment “SQL” for advanced interview questions & real scenarios 🔁 Share with someone preparing for data roles #SQL #DataAnalytics #DataAnalyst #BusinessIntelligence #SQLQueries #LearnSQL #Analytics #InterviewPrep #DataEngineering #CareerGrowth #Upskill

You don't need a degree to become a data analyst. You need four things — and a structured path to build them. Here's what actually moves the needle. 👇 #EdgeFoundry #DataAnalytics #CareerChange #SQL #TechCareers

🚀 Want to make your data analysis 10x easier? Mastering a few powerful SQL functions can save hours of manual work and simplify complex logic. From summarizing data with GROUP BY to handling NULLs with COALESCE and analyzing trends using DATE_TRUNC — these are must-know tools for every aspiring data analyst. 💡 These functions are widely used in interviews, dashboards, and real-world projects—so practicing them daily can truly level up your skills. #SQL #DataAnalytics #DataAnalyst #Learning #CareerGrowth #TechSkills

Just solved the “Compressed Mean” SQL problem 💻📊 This problem was all about understanding how to calculate a weighted average (mean) using SQL. 👉 Key concept: Instead of a simple average, we calculate: SUM(value × frequency) / SUM(frequency) Here’s the approach I used: - Multiplied "item_count" with "order_occurrences" - Summed the results - Divided by total occurrences - Rounded to 1 decimal place This was a great refresher on aggregation + real-world data interpretation — something very useful for data analyst roles. If you're preparing for SQL interviews, problems like this are a must-practice! 🚀 #SQL #DataAnalytics #LearningInPublic #DataAnalyst #SQLPractice #DataLemur

SQL Cheatsheet for data analysts :- The infographic neatly organizes everything into six sections: 1. Basics :- ( select, rename, all columns ) 2. Filtering Data :- ( WHERE, AND, LIKE ) 3. Aggregations :- ( COUNT, SUM, GROUP BY, HAVING ) 4. Joins :- ( INNER, LEFT ) 5. Useful Functions :-( string, date, CASE ) 6. Data Analyst Essentials :- ( LIMIT, DISTINCT, window functions ) This kind of short revision sheet helps much more than reading long notes again and again. Comment SQL if you want more posts like this on interview prep, cheatsheets, and practical data engineering learning. #SQL #DataEngineering #DataAnalyst #InterviewPreparation #LearnSQL #Database #TechCareers #DataEngineer #CareerGrowth

Day 36 – Date Functions On my first day as a data analyst, my manager asked: "Can you show me all orders from the last 30 days?" I wrote: WHERE order_date = '2024-03-16' and manually counted back. Every day I had to update it. Every weekend it broke. Then I learned: WHERE order_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY) That one line is self-updating. Run it today, tomorrow, next year always shows last 30 days. Date functions are in 90% of real SQL queries. Here are the ones that matter most: Get today: CURDATE() → 2024-04-15. NOW() → includes time. Extract parts: YEAR(date), MONTH(date), DAY(date) → use for GROUP BY month Calculate gaps: DATEDIFF(date1, date2) → days between. TIMESTAMPDIFF(MONTH, d1, d2) → months/years Shift dates: DATE_ADD(date, INTERVAL 30 DAY) → 30 days later. DATE_SUB → go back Format output: DATE_FORMAT(date, '%Y-%m') → 2024-04. Use '%Y-%m' for monthly grouping (year first = sorts correctly!) Day 36 / 60 — SQL for Beginners. Follow for a new concept every day. 🚀 #SQL #LearnSQL #SQLforBeginners #DataAnalytics #TechCareer #DataScience