🎬 Real Skills. Real Projects. Real Confidence. Our latest mockup reel gives you a behind- #ACTETechnologies #ACTEChennai #StudentSuccessStory #DataAnalyticsCourse #DataAnalyticsTraining #BestInstituteChennai #PythonForAnalytics #PowerBITraining #SQLForDataScience

🎬 Real Skills. Real Projects. Real Confidence. #ACTETechnologies #ACTEChennai #StudentSuccessStory #DataAnalyticsCourse #DataAnalyticsTraining #BestInstituteChennai #PythonForAnalytics #PowerBITraining #SQLForDataScience #JobOrientedTraining #CareerInAnalytics #UpskillNow

📊 Master SQL for Data Science – No Experience Needed! Build full data pipelines, analyze with SQL in Databricks & uncover insights that drive business impact. 🔗 groupify.ai/ai-courses/sql-b… #GroupifyAI #SQLforDataScience #Databricks #AICourse

3/ The dataset includes: Booking & arrival dates Number of guests & nights Distribution channels (e.g. OTA, Direct) Customer types (Transient, Corporate) Deposits, status (cancelled/checked out) Revenue & losses It’s detailed and rich. 📊 #SQLForDataScience

Today marked another fantastic step in our deep tech journey! Participants in the Data Science & Machine Learning track got a practical class following a nationwide live session. They dived into an interactive introduction to SQL the bedrock of data manipulation. We're not just teaching; we're building the future of tech in Nigeria, covering crucial areas like: Data Science and Machine Learning. Advanced Data Analysis and Visualisation Computer Vision and Natural Language Processing. This week's session was buzzing with real-time problem-solving and query building. Huge props to the learners for their active participation! #DataAnalytics #SQLForDataScience #BrainBuildersIT #3MTT #DataScienceNigeria #DeepLearning #SkillsForFuture @3MTTNigeria @DeepTech_Ready @dsn_ai_network

📜 SQL Cheat Sheet 🔍 Fetching Data (SELECT) SELECT * FROM table_name; SELECT column1, column2 FROM table_name; SELECT DISTINCT column FROM table_name; _____________________________________________________ 🎯 Filtering Data (WHERE) SELECT * FROM users WHERE age > 25; SELECT * FROM orders WHERE status = 'delivered'; _____________________________________________________ 🧩 Multiple Conditions (AND, OR, NOT) SELECT * FROM users WHERE age > 25 AND city = 'Delhi'; SELECT * FROM products WHERE price < 500 OR stock > 50; SELECT * FROM employees WHERE NOT department = 'HR'; _____________________________________________________ 🔢 Sorting Results (ORDER BY) SELECT * FROM employees ORDER BY salary DESC; SELECT * FROM products ORDER BY price ASC, name DESC; _____________________________________________________ 🎛 Limiting & Skipping (LIMIT, OFFSET) SELECT * FROM products LIMIT 5; SELECT * FROM orders LIMIT 10 OFFSET 5; _________________________________________________________ 🏗 Grouping Data (GROUP BY, HAVING) SELECT department, COUNT(*) FROM employees GROUP BY department; SELECT category, AVG(price) FROM products GROUP BY category HAVING AVG(price) > 100; _________________________________________________________ 🏆 Aggregations (COUNT, SUM, AVG, MIN, MAX) SELECT COUNT(*) FROM users; SELECT SUM(price) FROM orders; SELECT AVG(salary) FROM employees; SELECT MIN(age), MAX(age) FROM users; _____________________________________________________ 🔄 Joining Tables (JOIN) 👫 Inner Join SELECT users.name, orders.amount FROM users INNER JOIN orders ON users.id = orders.user_id; _____________________________________________________ 📌 Left Join SELECT users.name, orders.amount FROM users LEFT JOIN orders ON users.id = orders.user_id; _________________________________________________________ 📌 Right Join SELECT users.name, orders.amount FROM users RIGHT JOIN orders ON users.id = orders.user_id; _____________________________________________________ 🔗 Full Join SELECT users.name, orders.amount FROM users FULL JOIN orders ON users.id = orders.user_id; _____________________________________________________ 🏗 Creating & Modifying Tables 🆕 Creating a Table CREATE TABLE users ( id INT PRIMARY KEY, name VARCHAR(100), age INT, city VARCHAR(50) ); _____________________________________________________ 🔧 Altering a Table ALTER TABLE users ADD COLUMN email VARCHAR(100); ALTER TABLE users DROP COLUMN city; _____________________________________________________ 📝 Modifying Data ➕ Inserting Data INSERT INTO users (id, name, age) VALUES (1, 'Amit', 30); _____________________________________________________ 🛠 Updating Data UPDATE users SET age = 31 WHERE id = 1; _____________________________________________________ ❌ Deleting Data DELETE FROM users WHERE id = 1; DELETE FROM users; -- Delete all rows _____________________________________________________ 🏛 Indexes & Keys 🔑 Primary Key CREATE TABLE users ( id INT PRIMARY KEY, name VARCHAR(100) ); _____________________________________________________ 🔑 Foreign Key CREATE TABLE orders ( id INT PRIMARY KEY, user_id INT, FOREIGN KEY (user_id) REFERENCES users(id) ); _____________________________________________________ 🚀 Creating Index CREATE INDEX idx_name ON users(name); _____________________________________________________ 📜 Subqueries & Unions 🔄 Subquery SELECT name FROM users WHERE id IN (SELECT user_id FROM orders); _____________________________________________________ 🔗 Union (Combine Results) SELECT name FROM customers UNION SELECT name FROM suppliers; SELECT name FROM customers UNION ALL SELECT name FROM suppliers; -- Includes duplicates _____________________________________________________ 🔥 Transactions BEGIN TRANSACTION; UPDATE accounts SET balance = balance - 500 WHERE id = 1; UPDATE accounts SET balance = balance 500 WHERE id = 2; COMMIT; -- Save changes ROLLBACK; -- Undo changes #SQL #SQLTutorial #LearnSQL #SQLQueries #SQLCheatSheet #Database #DataScience #Programming #Tech #Code #SQLForBeginners #SQLBasics #SQLTraining #LearnToCode #CodingForBeginners #AdvancedSQL #SQLPerformance #SQLOptimization #SQLBestPractices #SQLDatabase #MySQL #PostgreSQL #SQLServer #OracleSQL #NoSQL #DataAnalytics #SQLForDataScience #BusinessIntelligence #BigData #DataVisualization

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 4 𝗦𝗶𝗺𝗽𝗹𝗲 𝗦𝘁𝗲𝗽𝘀! SQL is a must-have skill for any data scientist, analyst, or engineer. If you’re struggling to master it, don’t worry—I’ve broken it down into 4 easy steps to help you go from beginner to pro. 🎯 Ready to start? 🔗lnkd.in/dkexGamM 🔔 Don’t forget to 𝗦𝗛𝗔𝗥𝗘 𝘁𝗵𝗶𝘀 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗵𝗼 𝗺𝗶𝗴𝗵𝘁 𝗻𝗲𝗲𝗱 𝘁𝗵𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀. #SQLforDataScience #DataSkills #LearnSQL #DataAnalytics

🚀 Ready to level up your SQL game? Dive into advanced SQL techniques tailored for data science and uncover ways to tackle complex data challenges! From powerful window functions to optimization strategies, this guide covers it all in a practical, hands-on approach. Whether you’re a seasoned analyst or just starting, these techniques will transform how you approach data. Don't miss out—get ready to elevate your skills and gain insights that will set you apart! #SQLforDataScience #AdvancedSQL linkedin.com/pulse/advanced-…

🚀 Unlock the Power of SQL for Data Science! If you’re looking to master joins, subqueries, and indexing—the building blocks of efficient data handling—this is your guide! Learn how these skills can transform the way you work with data, boosting speed and clarity in your queries. Whether you're just starting or brushing up, this article breaks it down with hands-on examples and clear explanations. Ready to level up? Dive in now and see how these techniques can sharpen your data game! #DataScienceEssentials #SQLforDataScience linkedin.com/pulse/joins-sub…

"Master SQL skills for data science! Learn how to manipulate, query, and manage databases efficiently to uncover valuable insights and boost your data-driven decision-making. #SQLForDataScience #DataScienceSkills #LearnSQL #SQLQueries #DataAnalytics #SQLTraining #hkrtrainings

I just completed the course "Data Manipulation in SQL"! datacamp.com/completed/state… via @DataCamp #datafam #sql #sqlfordatascience

#SQL #webinar #sqlforbeginners #sqlfordatascience For those who were unable to attend the session I made a series of videos for the young aspirants so they get quick grip on it. lnkd.in/ds4gnXqz

Real-world data analysis requires proficiency in databases and SQL. Check out these free resources: W3Schools (SQL) #SQLBasics Mode Analytics (SQL) #SQLForDataScience

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Python Basics Tutorial Our SQL Journey Pt 13 What How When and Why Do I ... youtu.be/nerr2YeXvPQ via @YouTube #Python #SQL #SQLFORDATASCIENCE

Day 16 #100daysofcode #30daysofdata #sqlfordatascience (@DataCamp - Learnt about "self-join"(joining a table to itself) basic calculations using "self-join" - Learnt the use of the "CASE" clause together with the use of the "WHEN", "THEN", "ELSE" AND "END" clauses.

#sqlfordatascience - Learnt the use of the "HAVING" keyword (this is closely related to the "WHERE" keyword. The distinction, however, is grounded in the fact that the "HAVING" keyword is used with "AGGREGATE FUNCTIONS"- "AVG", "SUM"...- whilst the "WHERE" keyword is not).

Day 15 #100daysofcode #30daysofdata #sqlfordatascience(@DataCamp) - Learnt the "sorting of single columns" - Learnt the "sorting of multiple columns" - Learnt the use of the "GROUP BY" clause - Introduction to the basics of the "JOIN" keyword to link multiple TABLES contd...