Master SQL with Cheat Sheet and Practice

If SQL feels confusing, you’re probably learning it in fragments. Instead, think of SQL as a cycle 🔁 From filtering data → grouping → joining → ordering… everything is connected. Understanding this flow: • WHERE filters your data • GROUP BY summarizes it • JOINS connect tables • ORDER BY organizes results Once you see SQL as a system—not isolated commands—it becomes much easier to master. Stop memorizing. Start understanding the flow. That’s when SQL starts making sense 💡 #SQL #PowerBI #DataAnalytics #LearnSQL #Analytics #Codebasics #DataLearning

In syntax related to Group by and having Here's a quick example: SELECT department, AVG(salary) as avg_salary FROM employees GROUP BY department HAVING AVG(salary) > 50000; This query : - Groups employees by "department" - Calculates the average "salary" for each department - Returns only departments with an average salary > 50,000

📊 SQL Revision Day – Strengthening the Foundations Today, I focused on revising core SQL concepts by working on a dummy cellular company dataset. 🔍 Concepts I practiced: ✔️ SELECT & data filtering using WHERE (AND / OR, LIKE) ✔️ Sorting data using ORDER BY ✔️ Aggregating insights using GROUP BY ✔️ Combining multiple tables using INNER JOIN 💡 What stood out: Understanding how data connects across tables using JOINs really helps in seeing the bigger picture — especially when analyzing customers, plans, and subscriptions together. 📈 This hands-on revision is helping me move beyond theory and build practical confidence in SQL. #SQL #DataAnalytics #LearningJourney #DataSkills #BusinessAnalysis

🧩 SQL Concepts… All in One Sheet Not too advanced, not too basic — just a clean and structured summary of important SQL concepts 👇 When you're learning SQL, the hardest part isn’t always understanding… It’s remembering what to use and when. That’s where something like this helps: ✔️ Basic queries & filtering ✔️ Joins (with visual understanding) ✔️ Aggregations & GROUP BY ✔️ Window functions ✔️ Subqueries, CTEs & more #sql #cheatsheet #data #analytics #datascience

🧠 SQL Table Structure – Simplified If you're starting with SQL, understanding this is a game changer 👇 📌 Table → Organized data in rows & columns 📌 Column → Defines data type (Name, Email, etc.) 📌 Row → One complete record 🔑 Primary Key Unique ID for each row (no duplicates, no NULLs) 🔗 Foreign Key Connects tables → builds relationships between data 💡 Think of SQL like a well-structured system where relationships matter more than just data. Master this foundation, and everything else becomes easier. #SQL #Database #PowerBI #Codebasics #DataLearning #TechBasics

Stop scrolling and Save This. Because no one has time for a 4-hour tutorial when you just need the data now. Here’s the 80/20 of SQL: 👁️ SELECT & WHERE → See only what you need 📊 GROUP BY → Summarize the mess 🔗 JOINs → Stop copy-pasting between Excel sheets 🧮 Aggregates → Count, Average, Sum (Done) 🛑 Golden Rule: Avoid SELECT * in production. Your DBA will thank you. What’s the one SQL command you use the most? #SQL #DataAnalytics #TechTips #CareerGrowth

3-Day Rolling Sum (ROWS BETWEEN) — Easy Trick 💡 In SQL, a 3-day rolling sum means adding values of the current row + previous 2 rows. 👉 Trick to remember: 🔹 “2 before + current = 3-day total” ✔ 2 PRECEDING → go 2 rows back ✔ CURRENT ROW → include today ✔ Total = 3 rows (3 days) 🚀 Used for trends, moving averages, and time-based analysis. #SQL #WindowFunctions #DataAnalytics #InterviewPrep #Learning #TechTips

Here's an even more concise version ✅ 🔺 SQL Quick Reference ▪️ 1. Basics SELECT: Picks columns. FROM: Specifies the table. WHERE: Filters rows (LIKE, IN, BETWEEN, AND, OR). ORDER BY: Sorts results (ASC, DESC). AS (Alias): Renames columns or tables. ▪️ 2. Summarizing Functions: COUNT (), SUM(), AVG(, MIN(, MAX. GROUP BY: Groups rows for aggregate functions. HAVING: Filters after grouping. ▪️ 3. Combining INNER JOIN: Matching rows from both tables. LEFT JOIN: All from left, matches from right. RIGHT JOIN: All from right, matches from left. FULL JOIN: All rows from both tables. 🔺 Follow Saurabh Rai for more... #SQL #Data #Dataanalyst #Learning #Joins

Day 35/90 — Week 5 complete. Phase 2 has officially started and this week was big. Here is the full cheat sheet — save it. CASE WHEN: → SQL's if/else — classify rows into labelled segments → Use with GROUP BY to count each segment in one query Subquery in WHERE: → WHERE amount > (SELECT AVG(amount) FROM orders) → Filters dynamically — recalculates every time Subquery in FROM: → Wrap a query in FROM to create a virtual table → Always alias it — FROM (...) AS my_table Correlated subquery: → References the outer query's columns → Runs once per row — powerful but can be slow on large data Master query using all of this week: Filter above-average orders → classify each city by tier → count per tier All in a single SQL statement. Week 6 starts Monday — CTEs. The cleaner, more readable alternative to nested subqueries. Follow so you don't miss it. Tag someone who is learning SQL and needs this cheat sheet. #SQL #CaseWhen #Subquery #DataAnalytics #LearnSQL #Week5 #SQLCheatSheet #DataAnalyst

Hello everyone 🤗 In the fourth episode of our SQL series, we go beyond simply retrieving data and focus on preparing it for analysis. 🧩 Window Functions (ROW_NUMBER) 🧩 LEAD & LAG 🧩 Date/Time Functions 🧩 COALESCE 🧩 String Manipulation If you want to quickly shape your data before building a model, these SQL commands will be your best friends. Swipe to explore sample queries! What’s your favorite SQL function for cleaning data? Let’s chat in the comments👾👾. And, if you want to read the previous 3 slides, here are the links: 🔗https://shorturl.at/Er4AQ - Language of Data: SQL 🔗https://shorturl.at/UvPTu - 5 Basic Queries with SQL 🔗https://shorturl.at/XZEcv - 5 Basic Queries with SQL-2 Shared by: S. Bürde DÜLGER #SQL #DataScience #DataAnalytics #Database #DSMLHub #StudyNotes #BasicQueries #DataCleaning