SQL Skills for Data Roles

Master the core SQL commands that drive 80% of tasks. This post focuses on practical, real-world applications of SQL for maximum impact. Fundamental SQL Commands 1. 𝗦𝗘𝗟𝗘𝗖𝗧: Retrieving specific data        𝚂𝙴𝙻𝙴𝙲𝚃 𝚏𝚒𝚛𝚜𝚝_𝚗𝚊𝚖𝚎, 𝚕𝚊𝚜𝚝_𝚗𝚊𝚖𝚎, 𝚎𝚖𝚊𝚒𝚕 𝙵𝚁𝙾𝙼 𝚌𝚞𝚜𝚝𝚘𝚖𝚎𝚛𝚜;    2. 𝗪𝗛𝗘𝗥𝗘: Filtering results        𝚆𝙷𝙴𝚁𝙴 𝚙𝚞𝚛𝚌𝚑𝚊𝚜𝚎_𝚍𝚊𝚝𝚎 >= '𝟸𝟶𝟸𝟹-𝟶𝟷-𝟶𝟷' 𝙰𝙽𝙳 𝚝𝚘𝚝𝚊𝚕_𝚜𝚙𝚎𝚗𝚝 > 𝟷𝟶𝟶𝟶;    3. 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬: Aggregating data        𝚂𝙴𝙻𝙴𝙲𝚃 𝚙𝚛𝚘𝚍𝚞𝚌𝚝_𝚌𝚊𝚝𝚎𝚐𝚘𝚛𝚢, 𝚂𝚄𝙼(𝚜𝚊𝚕𝚎𝚜_𝚊𝚖𝚘𝚞𝚗𝚝) 𝙰𝚂 𝚝𝚘𝚝𝚊𝚕_𝚜𝚊𝚕𝚎𝚜    𝙵𝚁𝙾𝙼 𝚜𝚊𝚕𝚎𝚜    𝙶𝚁𝙾𝚄𝙿 𝙱𝚈 𝚙𝚛𝚘𝚍𝚞𝚌𝚝_𝚌𝚊𝚝𝚎𝚐𝚘𝚛𝚢;    4. 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬: Sorting data        𝚂𝙴𝙻𝙴𝙲𝚃 𝚙𝚛𝚘𝚍𝚞𝚌𝚝_𝚗𝚊𝚖𝚎, 𝚜𝚝𝚘𝚌𝚔_𝚚𝚞𝚊𝚗𝚝𝚒𝚝𝚢    𝙵𝚁𝙾𝙼 𝚒𝚗𝚟𝚎𝚗𝚝𝚘𝚛𝚢    𝙾𝚁𝙳𝙴𝚁 𝙱𝚈 𝚜𝚝𝚘𝚌𝚔_𝚚𝚞𝚊𝚗𝚝𝚒𝚝𝚢 𝙰𝚂𝙲;    5. 𝗝𝗢𝗜𝗡: Combining related data        𝚂𝙴𝙻𝙴𝙲𝚃 𝚘.𝚘𝚛𝚍𝚎𝚛_𝚒𝚍, 𝚌.𝚌𝚞𝚜𝚝𝚘𝚖𝚎𝚛_𝚗𝚊𝚖𝚎, 𝚘.𝚘𝚛𝚍𝚎𝚛_𝚍𝚊𝚝𝚎    𝙵𝚁𝙾𝙼 𝚘𝚛𝚍𝚎𝚛𝚜 𝚘    𝙸𝙽𝙽𝙴𝚁 𝙹𝙾𝙸𝙽 𝚌𝚞𝚜𝚝𝚘𝚖𝚎𝚛𝚜 𝚌 𝙾𝙽 𝚘.𝚌𝚞𝚜𝚝𝚘𝚖𝚎𝚛_𝚒𝚍 = 𝚌.𝚒𝚍;    Advanced SQL Techniques 1. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Nested queries for complex conditions        SELECT product_name, price    FROM products    WHERE price > (SELECT AVG(price) FROM products);    2. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀 (𝗖𝗧𝗘): Simplifying complex queries        WITH monthly_sales AS (    SELECT EXTRACT(MONTH FROM sale_date) AS month, SUM(amount) AS total    FROM sales    GROUP BY EXTRACT(MONTH FROM sale_date)    )    SELECT month, total    FROM monthly_sales    WHERE total > 100000;    3. 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Calculations across row sets        SELECT    department,    employee_name,    salary,    RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank    FROM employees;    4. 𝗖𝗔𝗦𝗘 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁𝘀: Conditional categorization        SELECT    customer_id,    CASE    WHEN lifetime_value > 10000 THEN 'VIP'    WHEN lifetime_value > 5000 THEN 'Premium'    ELSE 'Standard'    END AS customer_segment    FROM customer_data;    Optimization Tips - Use indexes on frequently filtered columns - Avoid SELECT * and only retrieve necessary columns - Use EXPLAIN ANALYZE to understand query execution plans Learning Strategy 1. Start with simple SELECT queries on a sample database 2. Progress to filtering and sorting data 3. Practice joins with multiple tables 4. Explore advanced techniques with real datasets 5. Participate in online SQL challenges and forums By mastering these SQL commands and techniques, you'll be well-equipped to handle a wide range of data analysis tasks efficiently. Regular practice with diverse datasets will solidify your skills. What's your favorite SQL trick for streamlining data ? Share your insights below!

90% of SQL interviews are built on these patterns. (If you know them, you're already ahead.) SQL interviews aren’t about syntax. They’re about problem-solving and spotting patterns. If you master these 5 patterns, you won’t just answer questions, you’ll impress with clarity and confidence. 1. 𝐉𝐨𝐢𝐧𝐬 & 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 ↳ Know how to connect multiple tables. ↳ Understand inner, outer, and self joins. ↳ Learn how filtering affects results post-join. 2. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐫𝐨𝐮𝐩 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ↳ Use GROUP BY to uncover trends. ↳ Add HAVING to filter aggregated results. ↳ Go deeper with nested aggregations. 3. 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 ↳ Rank rows with ROW_NUMBER, RANK, DENSE_RANK. ↳ Compare values using LAG, LEAD. ↳ Partition data for running totals and comparisons. 4. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 & 𝐂𝐓𝐄𝐬 ↳ Use subqueries to isolate logic. ↳ Break down complexity with CTEs. ↳ Write recursive queries for hierarchy problems. 5. 𝐐𝐮𝐞𝐫𝐲 𝐋𝐨𝐠𝐢𝐜 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 ↳ Control flow with CASE, COALESCE, NULLIF. ↳ Filter efficiently using WHERE, IN, EXISTS. ↳ Optimize performance with indexes and EXPLAIN. You don’t need to memorize everything. Just understand these patterns deeply. That’s how top candidates stand out. Check out the full breakdown on "𝐇𝐨𝐰 𝐭𝐨 𝐀𝐜𝐞 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬": https://lnkd.in/dVfhtz3V Remember, practice is the key!! I’ve attached a cheat sheet of the most common SQL functions to help you prep faster. ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 13,000+ readers here → https://lnkd.in/dUfe4Ac6

If you're preparing for a Data Analyst interview (especially if you have 0-2 years of experience), SQL is something you'll face in almost every round. Recently, after interacting with many freshers and junior analysts, I noticed that interviewers are now asking practical SQL questions that reflect day-to-day business scenarios. So here are some relevant SQL questions I've observed in recent interviews that will help you prepare better: Find Customers Who Purchased Exactly Two Different Products in a Single Month Tables: Orders (order_id, customer_id, product_id, order_date) Identify Customers Who Haven’t Made Any Purchase in the Last 6 Months Tables: Customers (customer_id, customer_name), Orders (order_id, customer_id, order_date) Calculate the Percentage of Orders Delivered Later Than Expected Tables: Orders (order_id, order_date, expected_delivery_date, actual_delivery_date) List the Top 3 Products per Category Based on Revenue Tables: Products (product_id, category), Sales (sale_id, product_id, amount) Calculate Each Customer’s Lifetime Spending (Customer Lifetime Value) Tables: Customers (customer_id), Orders (order_id, customer_id, order_date, amount) Find Employees Who Have Changed Departments More Than Twice Tables: Employee_Dept_History (employee_id, department, start_date, end_date) Identify Users Who Made Their First Purchase During a Promotional Campaign Tables: Users (user_id), Orders (order_id, user_id, order_date, promo_applied) Find Days Where Total Sales Decreased More Than 20% Compared to the Previous Day Tables: Daily_Sales (date, total_sales_amount) List Products That Have Never Been Out of Stock Tables: Products (product_id, name), Inventory (product_id, inventory_date, stock_available) Compare Average Order Values for New vs Returning Customers Tables: Orders (order_id, customer_id, order_amount, order_date) Interviewers aren’t just checking your technical accuracy, they're assessing your logical thinking. So, clearly explain your approach step-by-step while answering. Have you recently faced any interesting SQL interview questions? Share them in the comments—let’s discuss and grow together!

SQL is a lot more than “𝗦𝗘𝗟𝗘𝗖𝗧 * 𝗙𝗥𝗢𝗠” in the Real Projects! “Real SQL isn't written to impress. It's written to run every day at 2am without fail.” You get it right? With bootcamps and beginner courses, All you know is - SELECT, INSERT, UPDATE, DELETE Then you feel you’re ready to go. Whereas, it’s more about - ✅ 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 & 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: SQL isn't just SELECTing.  It's joining logs, cleaning messy real-world data with CASE WHEN this, JOIN that, handling NULLs, and writing robust WHERE conditions to filter and shape incoming data. ✅ 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗴𝗶𝗰: It’s the core logic!  Writing efficient GROUP BY clauses, using window functions to rank, rank, and transform data before it even gets to the warehouse. ✅ 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Building complex reports with nested WITH common table expressions (CTEs) to break down complex logic, using window functions to analyze trends. ✅ 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴: Structuring data with GROUP BY and PARTITION BY time, optimizing queries for reporting, maybe even using JOINing fact and dimension tables. ✅ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗟𝗼𝗴𝗶𝗰: Using WHERE clauses to filter and ensure data integrity, crafting JOIN statements to link different data sources, handling NULL values, using GROUP BY for aggregation. When you think you know SQL, think if you can really solve these performance hiccups - ❌ SELECT * on large tables ❌ No indexes on JOIN columns ❌ Unnecessary subqueries ❌ N+1 query patterns Think of SQL logic as the recipe in a kitchen—master the recipe, and you can cook up solutions with any tool in any kitchen. Willing to level up? Then take up your SQL Challenge This Week 👇 Find a real business problem and solve it with SQL: 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Who are your most valuable customers? 𝗦𝗮𝗹𝗲𝘀 𝘁𝗿𝗲𝗻𝗱𝘀: What patterns exist in your transaction data? 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: What inconsistencies exist in your datasets? 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Can you make a slow query 10x faster? Share your results! Post your most complex SQL query and explain what business problem you solved. Tag us(Pooja Jain & Ankita Gulati) - we love seeing real SQL in action.🔥 Image Credits: Brij kishore Pandey Need some resources for reference? Refer the comments section 👇 #data #engineering #reeltorealdata #python #sql #analytics

How do you solve a complex SQL problem ? This is how I do it. Complex SQL isn't about being clever. It's about being clear. Break problems into chunks. Use window functions smartly. Let's take an example > Find customers who: > Placed at least 2 orders in the last 6 months > Have an average gap between orders less than 30 days > And whose most recent order includes at least one returned item Here's what we can do: Step 1: Instead of jumping into a big query, I broke it down: - Get orders from the last 6 months - For each customer, calculate gaps between their orders - Compute average gap per customer - Filter those with avg gap < 30 - Check their latest order for a return Each of these needed different logic : some aggregations, some row-level. Step 2: Use CTEs to layer the logic This is important when you're working with complex SQL problems Step 3: Keep it readable CTEs made it easier to test each step. If something broke, I knew exactly where to look. Here's how it would look: If you’ve solved a beast of a query lately, would love to hear how you tackled it! 𝐍𝐞𝐞𝐝 𝐇𝐞𝐥𝐩 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮 𝐝𝐚𝐭𝐚 𝐜𝐚𝐫𝐞𝐞𝐫, 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 1 𝐭𝐨 1 𝐡𝐞𝐫𝐞 : https://lnkd.in/gH4DeYb4 ♻️ If you found this useful, repost it ! 👋 Follow me for more daily data content

One thing SQL taught me early in my career... Most performance issues aren’t because of “big data”… they’re because of small mistakes repeated everywhere. Let me share one concept many analysts overlook: The real power of SQL is how early you filter not how much you select. I’ve seen teams write beautiful queries, perfect joins, clean logic… but they push their filters to the bottom of the query. And then wonder why the query takes 20 seconds instead of 2. In reality: • Filtering before joining reduces workload • Reducing columns before joining reduces memory • Restricting the dataset early changes the entire execution plan A simple shift from “select everything → join everything → then filter” to “filter → reduce → join → select what matters” has saved hours of compute time in real projects. This is the difference between “knowing SQL syntax” and thinking like an engineer who respects the database. SQL isn’t just a language. It’s a negotiation with the database and the database rewards those who keep things lean. If your queries feel slow lately, ask yourself: “What can I remove before the join?” It’s a small habit with a massive impact. If you want structured SQL learning with real-world logic, I’ve shared practical learning kits here: https://lnkd.in/gasgBQ6k #DataAnalyst #SQL #Python #PowerBi #Interviews #Excel #DataJourney

SQL isn’t just a technical skill—it’s a superpower for PMs Whether it’s user engagement metrics, A/B test results, or sales trends, you might want to pull the numbers without relying on tech teams. With SQL, you can: ✅ Pull your own data without waiting. ✅ Spot trends and insights faster. ✅ Build stronger, data-driven cases for your ideas. But SQL can feel intimidating for non-tech PMs. Here’s a quick breakdown what you need to know in SQL: 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀 Start with the essentials—the queries you’ll use every day: - SELECT: Fetch specific columns (e.g., product names and prices). - WHERE: Filter data (e.g., products priced above $100). - ORDER BY: Sort results (e.g., most expensive products first). - LIMIT: Get a snapshot of data (e.g., top 5 products). These queries are your foundation. They’ll help you answer questions like: - What are our top-selling products? - How many users completed onboarding last week? 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 Once you’ve mastered the basics, level up with: - JOINs: Combine data from multiple tables (e.g., orders + customers). - GROUP BY: Summarize data (e.g., average price by category). - Subqueries: Break down complex problems into smaller steps. - HAVING: Filter grouped results (e.g., categories with >10 products). These queries let you tackle more complex questions: - What’s the average order value by region? - Which features are most used by power users? 𝗟𝗲𝘃𝗲𝗹 𝟯: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 Dive into: - Window Functions: Calculate running totals or rankings. - CTEs (Common Table Expressions): Simplify complex queries. - CASE Statements: Add conditional logic (e.g., categorize products by price range). - Recursive Queries: Handle hierarchical data (e.g., org charts). These queries help you uncover insights like: - What’s the cumulative revenue growth over time? - How do user engagement trends vary by cohort? SQL isn’t just about writing queries—it’s about empowering yourself to make better decisions. 👉 Want the Full Guide? I’ve put together a detailed resource with syntax, examples, and real-world use cases for each query, comment “SQL” below, and I’ll send it your way.

Preparing for a SQL interview? 🤓 Here's a checklist to ensure you're ready to ace it: 1🔶 Joins: Master the art of joining tables to extract meaningful insights. Understand different types of joins and when to use them. 2🔷 Group By: Dive deep into grouping data to analyze trends and patterns. Know how to aggregate information effectively using GROUP BY. 3🔶 Window Functions: Level up your skills with window functions. Learn how to perform calculations across a set of rows related to the current row. 4🔷 Core Database Concepts: Brush up on the fundamentals - understand indexes, transactions, normalization, and other essential concepts that form the backbone of databases. 5🔶 Schema Design (Facts and Dimensions): Explore the art of designing effective database schemas. Grasp the importance of organizing data into facts and dimensions for optimal performance. Remember, a strong foundation in these areas will not only help you crack the interview but also make you a more proficient SQL practitioner. Practice, understand the logic behind each concept, and don't hesitate to challenge yourself with real-world scenarios. Good luck! 🌟#sqldeveloper #databricks #linkedin #powerbi #dataanalysis #businessanalytics #ai #growth #learningandgrowing #dataengineering

Breaking Into 20-30 LPA Data Science Roles: Essential SQL Interview Questions from Leading Tech Firms Position: Data Scientist (2+ Years Experience) Key Skill: Mastering SQL is no longer optional—it’s what sets top candidates apart in interviews at companies like Amazon and Microsoft. Across every Data Science interview I’ve seen or conducted, strong SQL knowledge is a clear differentiator for advancing through the screening process. Preparing for a top-paying role? Here are some of the real-life SQL challenges you should be ready for: Common SQL Questions for Data Scientist Interviews (Amazon, Microsoft & Beyond): • Data Aggregation & Window Functions • Select the top 3 selling products for each category using SQL. • Demonstrate how to compute moving averages or running totals. • Advanced Joins & Subqueries • Query to find all users who have never made a purchase (using users/orders tables). • Identify customers who bought the same product more than once. • Data Cleaning & Transformation • Remove duplicate entries from a dataset. • How do you handle NULL values within aggregate functions? • Advanced Filtering • List orders placed within the last 30 days by region. • Retrieve employees with salaries exceeding the department average. • Handling Dates & Time • Write an SQL query for month-over-month sales growth. • Calculate days between two timestamp fields. • Optimization Best Practices • What steps would you take to speed up a slow query? Which indexes could help? • How do you use  EXPLAIN  to review and optimize SQL queries? • Business-Oriented Cases • Detect anomalies in transactional data. • Segment users based on their activity levels in the previous quarter. Topics to Prioritize: • Window functions ( ROW_NUMBER() ,  RANK() ,  LAG() ,  LEAD() ) • All types of joins (including self and outer joins) • Aggregations & grouping ( GROUP BY ,  HAVING ) • Subqueries and CTEs • Strategies for handling NULL values and data types If you want personalized tips or want to practice with mock SQL/data interviews, connect here! 🚀 Link: https://lnkd.in/gz44hDxm Save this post and share it with your network if you found it useful. Let’s help each other crack the next big interview! #DataScience #SQL #CareerGrowth #InterviewTips

If I were learning SQL in 2025, Here is exactly what I would do (+ resources) 👇 I have worked as a DS in 3 different companies. I have landed DS offers from 10 different companies. The number 1 skill I’ve used on the job & in interviews? It’s SQL. Yes, I’ve used SQL more than Python as a Data Scientist. So here's how to learn SQL from scratch. 𝟭. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Boring…. can’t we jump start into learning SQL? No! SQL = storing + extracting data from relational DB. So it’s really helpful to know relational databases. K͟e͟y͟ ͟c͟o͟n͟c͟e͟p͟t͟s͟ ↳ Rows vs. columns ↳ Tables vs. schemas vs. database ↳ Keys (primary, foreign & unique) ↳ Indexes ↳ Table relationships ↳ Data types: numeric, string, datetime, boolean Learn relational databases here: https://lnkd.in/gyt3q8AC 𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗯𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 We'll start with getting data out of a SINGLE table. F͟o͟u͟n͟d͟a͟t͟i͟o͟n͟s͟ ↳ SELECT ↳ FROM ↳ WHERE ↳ ORDER BY ↳ LIMIT ↳ AS C͟l͟e͟a͟n͟i͟n͟g͟ ͟d͟a͟t͟a͟ ↳ DISTINCT ↳ LIKE ↳ BETWEEN ↳ COALESCE ↳ CASE WHEN B͟a͟s͟i͟c͟ ͟a͟n͟a͟l͟y͟t͟i͟c͟s͟ ↳ GROUP BY ↳ HAVING ↳ COUNT ↳ SUM ↳ AVG ↳ MIN / MAX How to do analyses with SQL: https://lnkd.in/gvZjepWf 𝟯. 𝗟𝗲𝘃𝗲𝗹 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗦𝗤𝗟 𝘀𝗸𝗶𝗹𝗹𝘀 C͟o͟m͟b͟i͟n͟i͟n͟g͟ ͟t͟a͟b͟l͟e͟s͟ ↳ JOINs (INNER, LEFT, RIGHT, FULL) ↳ UNION and UNION ALL ↳ CTEs vs subqueries W͟i͟n͟d͟o͟w͟ ͟f͟u͟n͟c͟t͟i͟o͟n͟s͟ ↳ OVER ↳ PARTITION BY ↳ ORDER BY ↳ ROWS BETWEEN ↳ SUM, AVG, MIN, MAX with windows ↳ RANK, ROW_NUMBER, NTILE, LAG, LEAD Intermediate SQL: https://lnkd.in/gKM9WkyA Advanced SQL: https://lnkd.in/grhDPTdK 𝟰. 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 In the real-world we work with a lot of data at once. This is not a nice-to-have; it’s a must-have skill. Q͟u͟e͟r͟y͟ ͟o͟p͟t͟i͟m͟i͟z͟a͟t͟i͟o͟n͟ ͟t͟i͟p͟s͟ ↳ Avoid unnecessary data processing ↳ Reduce dataset size early ↳ Use indexes wisely ↳ Use EXPLAIN Get practice optimizing your queries: www.interviewmaster.ai 𝟱. 𝗔𝗽𝗽𝗹𝘆, 𝗯𝘂𝗶𝗹𝗱, 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 Build your own projects. But what projects should you build? Here are some ideas: ↳ Analyzing student’s mental health: https://lnkd.in/gZCUPpr5 ↳ What and where are the world’s oldest businesses: https://lnkd.in/gSWSdVt3 ↳ NYC public school test result scores: https://lnkd.in/g-SCsY5M 𝟲. 𝗣𝗿𝗲𝗽 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲𝘀 Learn how SQL is used in the real-world: https://lnkd.in/gZt6bp-F And, of course, practice for SQL interviews - LeetCode: https://lnkd.in/gpcyVPh9 - Interview Master: https://lnkd.in/gvs2u8Bm - StrataScratch: https://lnkd.in/g9D9jZ9A ——— Starting from scratch? Learn all your SQL fundamentals in one place: https://lnkd.in/gNXW297S