Akshay Rathod’s Post

If you know these 10 SQL queries… You’re already ahead of 80% of Data Analysts. But most people still struggle — not because SQL is hard, but because they don’t know what actually matters. So I created this 👇 A simple, practical SQL cheat sheet based on real-world usage. Here’s what you’ll find: 🔹 SELECT + WHERE → Filter data like a pro 🔹 ORDER BY + LIMIT → Get top insights instantly 🔹 GROUP BY + HAVING → Turn raw data into decisions 🔹 JOINs → Combine multiple datasets effectively 🔹 CASE WHEN → Add logic inside your queries 🔹 Window Functions → Advanced analytics (game changer) 🔹 CTEs → Write clean & scalable SQL And more… These are not just concepts — these are the exact query patterns I use in real projects. 📌 If you’re learning SQL: Don’t try to learn everything. Master these → and you’ll be job-ready faster. 💾 Save this post — you’ll need it again. 💬 Comment “SQL” if you want real-world practice questions. #SQL #DataAnalytics #BusinessIntelligence #DataScience #Learning #PowerBI #Python

🚀 SQL Data Analysis Cheat Sheet — My Quick Reference Guide As a beginner in data analysis, I created this simple SQL cheat sheet to keep all important concepts in one place 👇 🔹 Basic Queries (SELECT, ORDER BY) 🔹 Filtering Data (WHERE, IN, BETWEEN, LIKE) 🔹 Aggregate Functions (COUNT, SUM, AVG, MAX, MIN) 🔹 Joins (INNER, LEFT, RIGHT, FULL) 🔹 Grouping & Sorting (GROUP BY, ORDER BY) 🔹 Common Real-World Queries This helps me quickly revise concepts while practicing datasets and solving real problems. 💡 My Goal: Become a Data Analyst by building strong fundamentals step by step. If you're also learning SQL, save this post — it might help you too! 📌 Next Step: Practice queries on real datasets and improve problem-solving skills. #SQL #DataAnalysis #DataAnalytics #LearningJourney #Beginner #DataAnalyst #PowerBI #Python #CareerGrowth

The Foundation Never Lies 🏗️ The community verdict: SQL is still the ultimate "Day 1" skill. We asked, and you answered. If we had to start from zero today, nearly 50% of us would grab a SQL console before anything else. Why these results make perfect sense: ▪️ SQL (49%): It’s the universal language. Tools change, dashboards get redesigned, but the data always lives in a database. If you can’t talk to the data, you can’t analyze it. ▪️ Excel (27%): Still the fundamental pillar. Mastering Excel remains a cornerstone in understanding data analytics, providing the first real grasp of data structures and logic. ▪️ Power BI / Tableau (15%): Visuals are the "output," but the community knows that without the 80% work (SQL/Excel), the dashboard is just a pretty picture. ▪️ Python (8%): Essential for advanced automation and ML, but seen as a specialized "Step 2" rather than a "Step 1" for someone starting from scratch. The takeaway? Don't get distracted by the flashy tools. Master the foundation, and the rest will follow. Hit ❤️ if you agree that SQL + Excel are an important part of Data Analytics! #DataAnalytics #CareerPath #SQL #Excel #DataStrategy #PowerBI #LearningData #CommunityInsights #RomaniaTech

Four YouTube channels that taught me data analytics without a CS degree: -- Alex Freberg (Alex the Analyst): Best starting point for SQL. Covers real-world stuff, not just theory. https://lnkd.in/gcyxgWAA -- Data Professor: Strong on Python and data science fundamentals. Approachable for beginners. https://lnkd.in/gHCRz9CT -- Absent Data: Underrated. Covers how to approach technical SQL questions https://lnkd.in/geab_Yji -- Mo Chen: Excellent for Tableau and Power BI. https://lnkd.in/g9niE6TA The resources are not the bottleneck. Sitting down and actually doing the work is. I had to watch the videos, then open SQL and practice, then build something I could show. You do not need to spend money to break into data. You need to start. Which free resource has helped you the most? I am always adding to my list.

🧠 SQL is the language of data — and here’s your starter pack 📦 Whether you're analyzing customer behaviour, building dashboards, or cleaning messy datasets, these six commands are your daily essentials: 🔹 SELECT - Grab the data you need 🔹 WHERE - Filter out the noise 🔹 ORDER BY - Sort your results 🔹 GROUP BY - Summarize by category 🔹 JOIN - Connect the dots across tables 🔹 LIMIT - Keep it concise I created this visual cheat sheet to help fellow learners and aspiring analysts quickly reference the fundamentals. It’s simple, practical, and saves time when you're deep in query mode. 💬 If you're just starting out, master these first. If you're already using them, what’s your favourite SQL trick that makes your workflow smoother? Let’s make data work smarter 💡 #SQL #DataAnalytics #DataAnalyst #SQLTips #SQLCheatsheet #PowerBI #Excel #Python #BusinessIntelligence #DataScienceCommunity #TechTips #CareerGrowth #LearningSQL #Codebasics #DataDriven

Most Excel files don’t have a data problem. They have a shape problem. You’ve seen this: Part 01-Apr 02-Apr 03-Apr A123 100 95 88 B456 50 47 49 Looks clean. Feels structured. Completely unusable. Now try to answer simple questions: - Trend over time? - Compare SKUs? - Feed into Power BI / Python? - Join with other data? You either can’t… or you build a monster of helper columns. Here’s the fix: Turn this (wide matrix) into this (long list): Part Date Qty A123 01-Apr 100 A123 02-Apr 95 A123 03-Apr 88 B456 01-Apr 50 B456 02-Apr 47 B456 03-Apr 49 Why this matters: 1. Analysis becomes trivial Pivot, filter, group — everything just works. 2. Scales instantly Add 100 new dates? No redesign needed. 3. Works with real tools SQL, Python, Power BI all expect this format. The formula: =LET( sku, A2:A100, dates, C1:G1, data, C2:G100, nRows, ROWS(sku), nCols, COLUMNS(data), sku_out, INDEX(sku, ROUNDUP(SEQUENCE(nRows*nCols)/nCols,0)), date_out, INDEX(dates, MOD(SEQUENCE(nRows*nCols)-1,nCols)+1), qty_out, TOCOL(data), HSTACK(sku_out, date_out, qty_out) ) What it does (in plain English) - Repeats each SKU for every date - Cycles through all dates - Stacks all quantities into one column - Combines everything into a clean table Or the simple way: 👉 Power Query → Unpivot The real takeaway: 👉 Wide format = presentation 👉 Long format = analysis Most teams don’t need better dashboards. They need to stop storing data like a slide deck. Fix the structure → everything downstream gets easier.

🔍 SQL Joins - explained once and for all! If you've ever stared at a JOIN query and thought "wait, what's actually being returned here?" you're not alone. Here's a quick visual breakdown every data professional should bookmark 👇 ✅ INNER JOIN → Only matching rows from both tables ✅ FULL JOIN → Everything from both tables (matched + unmatched) ✅ FULL JOIN + WHERE NULL → Only the rows that DON'T match (outer exclusive) ✅ LEFT JOIN → All of A + matching rows from B ✅ LEFT JOIN + WHERE B.key IS NULL → Only rows in A with NO match in B ✅ RIGHT JOIN → All of B + matching rows from A ✅ RIGHT JOIN + WHERE B.key IS NULL → Only rows in B with NO match in A Mastering JOINs is the difference between writing queries that work... and writing queries that work efficiently. 💡 Save this post next time you're lost in a JOIN rabbit hole. 🐇 Which JOIN do you use the most in your day-to-day work? Drop it in the comments! 👇 #SQL #DataEngineering #DataScience #Analytics #Programming #TechTips #LearnSQL #DatabaseDesign #DataAnalytics #SoftwareEngineering #Coding #BigData #CareerGrowth #TechCommunity

A SQL query that calls itself! Sounds like a bug. It's actually a superpower: 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘𝘀! A recursive CTE is a Common Table Expression that selects from itself. Seems tricky, but it's actually not that difficult! Imagine you have this company hierarchy: Alice (CEO) ↳ Bob and Charlie report to Alice ↳ David and Eve report to Bob ↳ Frank and Grace report to Charlie You can build this entire corporate tree with SQL! Showing each employee and their level in the hierarchy. Behind the SQL scenes: 1️⃣ The base case gets the top of the hierarchy (Alice). 2️⃣ Then the CTE calls itself to find everyone reporting to her. 3️⃣ Each new “generation” of employees adds one more level. 4️⃣ The process continues until there are no more subordinates. That’s recursion! 🔄 The most powerful way to handle hierarchical data. 𝟭𝟬𝟬 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝟯𝟬𝟬 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗘𝘅𝗰𝗲𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 + 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝗦𝗵𝗲𝗲𝘁 𝟭𝟱𝟬 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 (𝗡𝘂𝗺𝗣𝘆 + 𝗣𝗮𝗻𝗱𝗮𝘀 + 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯) 𝟭𝟬𝟬 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗗𝗔𝗫 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗧𝗼𝗽 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 𝟭𝟬𝟬 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 𝗥𝗲𝘀𝘂𝗺𝗲 𝗚𝘂𝗶𝗱𝗲 + 𝟳𝟬𝟬 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗦𝗶𝘁𝗲𝘀 𝗚𝗲𝘁 𝗔𝗰𝗰𝗲𝘀𝘀 𝗛𝗲𝗿𝗲: https://lnkd.in/dyBfCTjK #datascience #data #dataanalysis #sql #python #pandas #excel #powerbi

🔍 WHERE vs HAVING in SQL — Don’t Confuse These Two! When working with SQL, filtering data is essential. But many learners (and even professionals) get confused between WHERE and HAVING. They might look similar… but they work at completely different stages of a query. 💡 WHERE Clause — Filter Before Grouping The WHERE clause is used to filter rows before applying any grouping. 👉 Works on raw data (individual rows) 👉 Cannot use aggregate functions like SUM(), COUNT(), AVG() Example: SQL SELECT * FROM sales WHERE amount > 500; Here, we’re filtering data at the row level before any grouping happens. 📊 HAVING Clause — Filter After Grouping The HAVING clause is used after the GROUP BY operation. 👉 Works on grouped data 👉 Used with aggregate functions Example: SQL SELECT customer_id, SUM(amount) AS total_sales FROM sales GROUP BY customer_id HAVING SUM(amount) > 1000; Here, we first group the data, then filter those groups based on aggregated values. ⚖️ Key Difference (Easy to Remember) 👉 WHERE → Filters Rows (Before Aggregation) 👉 HAVING → Filters Groups (After Aggregation) 🚀 Pro Tip If you ever try using an aggregate function in a WHERE clause and get an error… that’s your sign 👉 switch to HAVING. 🔥 Final Thought Understanding this small difference can make your SQL queries more accurate, efficient, and professional. #SQL #DataScience #DataAnalytics #MachineLearning #ArtificialIntelligence #DataEngineering #Analytics #Python

❌ You don’t need 100 tools to become a Data Analyst 👉 You need just ONE skill to start: SQL --- I see many beginners doing this mistake: Learning Python ❌ Learning Power BI ❌ Watching 50 tutorials ❌ But skipping SQL ❌ --- 💡 Reality check: SQL is used in almost EVERY data job If you know SQL, you can: ✔ Pull data from databases ✔ Answer business questions ✔ Impress in interviews ✔ Work with real company data --- 🚀 What I learned in SQL recently: 🔹 JOIN → Combine multiple tables 🔹 GROUP BY → Summarize data 🔹 WINDOW FUNCTIONS → Real analysis (RANK, LAG, ROW_NUMBER) --- 📊 I built a project where I: * Analyzed sales & customer data * Found top customers by city * Tracked sales trends over time --- ⚠️ Biggest lesson: SQL is not hard… 👉 Lack of practice makes it hard --- 📌 If you are starting: Focus on SQL → then move to tools --- 💬 Comment “SQL” and I’ll share beginner roadmap #SQL #DataAnalytics #DataAnalyst #LearningInPublic #CareerSwitch #TechSkills