Ebin Sujin’s Post

Coming from Analytics background, I thought about sharing some excited things I’ve learned so far in my journey in Advanced Analytics. First stop would be with practical tips writing SQL queries that helped me grow. SQL window functions can feel overwhelming at first, but once you break them down, they’re incredibly powerful for analyzing data! Let’s look at the key components to make sense of them: Every window function is built with 3 essential parts: ·      Function + OVER() ·      PARTITION BY ·      ORDER BY Here’s how these parts work: - PARTITION BY divides your data into groups, similar to GROUP BY, but it only affects the window function. -      ORDER BY sorts the rows within each partition. -      Both of these fit inside the OVER() clause and affect only the window function, not the entire query. 1. Function + OVER(): The function you’re applying will run on each partition after the data has been grouped and sorted. Some common functions include: ROW_NUMBER(): Assigns a unique number to each row. RANK(): Ranks rows with possible gaps. DENSE_RANK(): Ranks without gaps. LAG() and LEAD(): Compare values across rows. You can also use aggregate functions like: SUM() AVG() COUNT() These work inside the window function just as they would in a regular query. 2. PARTITION BY: Use PARTITION BY to split the data into smaller groups. For example: PARTITION BY customer_id groups rows by each customer, applying the function to each group individually. 3. ORDER BY: Within each partition, you can arrange the rows using ORDER BY. For instance: ORDER BY transaction_date arranges each customer’s transactions in date order. #AdvanceAnalytics #SQL #WindowsFunction

🚀 Mastering SQL Essentials 🚀 In SQL, getting the fundamentals down can supercharge your data analysis! Here’s a quick dive into some key concepts: 1-Alias (AS) - Rename columns or tables to make queries more readable. 2-Grouping & Filtering (GROUP BY, HAVING) - Use GROUP BY to aggregate data based on a column and HAVING to filter groups, perfect for summarizing insights! 3-Ordering Results (ASC, DESC) - Organize data with ORDER BY to see results in ascending (ASC) or descending (DESC) order. 4-Joins (Inner, Left, Right, Full) - Combine tables to enrich data: •INNER JOIN - Matches only where both tables have data. •LEFT JOIN - Returns all from the left table, matching data on the right. •RIGHT JOIN - Returns all from the right, matching data on the left. •FULL JOIN - Brings everything from both tables, whether matched or not. 5-Functions (AVG, MAX, MIN, SUM, COUNT) - Basic math for analysis: •AVG() - Average values. •MAX(), MIN() - Find highest and lowest values. •SUM() - Total up numbers. •COUNT() - Count occurrences. 6-Filtering (WHERE, LIKE, IN, BETWEEN, ANY, EXIST, ALL) - Refine results: •LIKE - Finds patterns. •IN - Matches any value in a list. •BETWEEN - Set a range. •ANY & ALL - Compare with subqueries. •EXIST - Checks if subquery returns data. Master these and bring efficiency to your data journey! 🌟 #SQL #DataAnalysis #Analytics #BusinessIntelligence #RetailAnalytics #BasicSQL

🚀SQL Window Functions: Unlocking Advanced Data Analysis 🚀 As a data professional, I'm constantly amazed by the power of SQL, especially when it comes to window functions. These hidden gems allow us to perform complex calculations across a set of rows related to the current row, opening up a world of analytical possibilities. Key benefits of window functions: 1. 📊 Time-series analysis: Easily calculate running totals, moving averages, and year-over-year comparisons. 2. 🏆 Ranking: Implement sophisticated ranking systems within groups or across entire datasets. 3. 🔍 Lag and lead operations: Access data from previous or future rows without complex self-joins. 4. 📈 Percentiles and distributions: Calculate percentiles and distribution statistics efficiently. One of my favorite applications is using window functions for cohort analysis. By partitioning data by cohort and ordering by time, we can track user behavior and retention rates with just a few lines of code. While window functions may seem daunting at first, mastering them can significantly enhance your data analysis capabilities and query performance. Have you explored window functions in your SQL work? What's your favorite use case? Let's discuss in the comments! #SQL #DataAnalysis #WindowFunctions #DataScience #Amdari21DaysDataChallenge #Ayodeji_BelloeAmdariTODDC

"Mastering SQL Insert for Multiple Rows in Minutes!" In this video, we'll walk you through one of the most essential SQL operations: inserting data into a table. While many beginners know how to insert single values into a database, real-world applications often require you to insert multiple rows at once. Imagine you are working for a company that recently surveyed hundreds of participants. You need to store their data efficiently in your database, but doing this one row at a time would take forever. The good news? SQL has a solution for bulk inserting data in a single command! In this tutorial, I’ll show you the syntax and step-by-step process of inserting multiple values into a table in just one statement. Whether you're adding user data, product listings, or survey results, you'll save time and minimize errors by applying this technique. You’ll also learn how to structure your SQL `INSERT INTO` statement to handle any type of data, so you can manage both small and large datasets with ease. Learn more on our Youtube page https://lnkd.in/d-V3Xdgn Our Introduction to Data Analytics Practical Class with Advanced Excel, Power BI, and SQL is ongoing and we have limited slots for new enrollees. Claim a slot in the link https://bit.ly/4cBfxQ3 --- #SQLTutorial #DataManagement #SQLInsert #LearnSQL #DatabaseDevelopment #TechEducation #MultipleRowsInsert #DataAnalysis #SQLForBeginners #TechSkills #CodeWithMe #SoftviewAnalytics

Mastering SQL: Window Functions vs. GROUP BY and More! Today, I dived deep into SQL window functions and how they compare to GROUP BY. These concepts are incredibly powerful for advanced data analysis, and here’s what I learned: 1️⃣ Window Functions vs. GROUP BY Unlike GROUP BY, which collapses data into groups, window functions allow you to perform calculations across a set of rows without reducing the dataset. This flexibility makes window functions perfect for tasks like ranking, running totals, and moving averages. 2️⃣ Window Syntax Understanding the structure of window functions is essential. Functions like ROW_NUMBER(), RANK(), and SUM() over a defined window open up endless possibilities for dynamic analyses. 3️⃣ PARTITION BY Clause The PARTITION BY clause divides data into groups, allowing calculations to reset for each partition. For instance, calculating sales rankings within each region becomes seamless with this feature. 4️⃣ ORDER BY Clause Adding ORDER BY to window functions lets you define the sequence of rows within each partition. This is crucial for creating rankings, calculating cumulative sums, or identifying trends. 5️⃣ FRAME Clause The FRAME clause lets you define a specific subset of rows for your calculations—like a rolling window or cumulative frame—adding precision to your analysis. 6️⃣ 4 Rules of Window Functions I explored the rules that govern window functions, ensuring clarity and consistency in their application. Following these rules ensures accurate and predictable results in every query. Why This Matters Window functions are a game-changer for modern data analysis. They offer unparalleled flexibility for performing advanced calculations without altering the overall dataset, making them invaluable for dashboards, reports, and beyond. If you’ve mastered window functions or have interesting use cases to share, drop them in the comments—I’d love to learn from you! #SQLLearning #WindowFunctions #DataAnalytics #ContinuousLearning

Mastering SQL Set Operators: A Game-Changer for Data Analysis! Today, I focused on SQL set operators, a powerful set of tools for combining and comparing datasets. These operators make it easier to work with multiple tables and extract meaningful insights. Here’s what I learned: 1️⃣ UNION vs. UNION ALL • UNION: Combines results from two queries while removing duplicates. Perfect for when you need clean, distinct results. • UNION ALL: Combines all rows, including duplicates, making it faster and ideal for large datasets when duplicates don’t matter. 2️⃣ EXCEPT • A handy operator to find records present in one dataset but not in another. This is great for identifying unique entries or discrepancies between tables. 3️⃣ INTERSECT • Finds common records between two datasets. It’s incredibly useful when you want to identify overlaps or matches across tables. Why These Matter Set operators are essential for scenarios like merging reports, comparing datasets, and ensuring data integrity. By mastering these, I’m better equipped to handle real-world challenges like reconciling records and generating unified insights. SQL continues to amaze me with its versatility and power. If you’ve used set operators in your work, I’d love to hear how they’ve helped you solve problems—share your experiences in the comments! #SQLSetOperators #DataAnalytics #SQLLearning #ContinuousLearning

SQL Window Functions: SQL is powerful for querying data, but sometimes we need more complex insights beyond standard aggregations like SUM, AVG, or COUNT. That’s where Window Functions come in. What are Window Functions? Unlike standard SQL aggregations that return a single result, window functions operate across a defined subset of rows (called a "window") and return a calculated result for each row in that window. They enable complex calculations like running totals, moving averages, ranking, and more—without grouping data. Why Window Functions Matter: They allow us to analyze data while retaining row-level detail, which can make a huge difference in analytics and reporting. For instance: Calculate a cumulative sales total by month within each year. Create a moving average to smooth out trends in time series data. Rank products by sales within each category, so you can see top performers in context. Example: SELECT product_id, sales_date, SUM(sales_amount) OVER (PARTITION BY product_id ORDER BY sales_date) AS running_total FROM sales_data ORDER BY product_id, sales_date; Here, we’re calculating a running total for each product, keeping a running tally by sales date. We use PARTITION BY to define groups within your data. Combine window functions to get unique insights, like ranking within groups or creating weighted averages. Mastering window functions can turn basic queries into powerful analytical tools. They’re essential for anyone serious about data #SQL #DataAnalytics #WindowFunctions #DataEngineering #LearningSQL

Unlocking the Power of SQL Window Functions: Ranking Data I’ve been diving into SQL window functions and focusing on ranking data—an essential skill for data analysis and reporting. Here’s what I’ve been working on: 1. Ranking Transactions: Assigning ranks to transactions based on various criteria, such as transaction quantity or unit price, to identify top performers and trends. 2. Partitioning and Ordering: Using `RANK()`, `DENSE_RANK()`, and `ROW_NUMBER()` to partition data and order results, providing insightful views into performance metrics. 3. Analyzing Trends: Understanding how different data points rank within their categories and store locations, helping to drive strategic decisions. Why Ranking Data Matters: - Insightful Analysis: Helps in identifying top performers and anomalies within data sets. - Enhanced Reporting: Provides clarity and precision in reporting metrics and trends. - Data-Driven Decisions: Empowers businesses to make informed decisions based on rank-based insights. I’m excited to continue exploring more advanced SQL concepts and apply these skills to complex data challenges. If you're also working with SQL or data analytics, let’s connect and share insights! #SQL #DataAnalytics #WindowFunctions #RankingData #DataScience #LearningJourney

🚀 **Master Your SQL Skills with the LAG() Function!💡 Looking to take your data analysis to the next level? The SQL LAG() function is a powerful tool for anyone working with sequential data. It allows you to access data from previous rows in your result set without needing complex joins. 🙌 Why should you use LAG()? 🔄 Compare Current and Previous Rows: Whether you're tracking sales over time or analyzing stock prices, LAG() helps you compare current values with previous ones effortlessly. 📊 Trend Analysis: This function is perfect for identifying patterns and trends in your data. Spot growth, decline, or consistency over time by referencing earlier rows in your dataset. ⚡ Simplify Calculations: With LAG(), you can calculate differences between rows without the need for a self-join, making your queries cleaner and more efficient. 🔍 Detect Changes: Need to flag when a value changes from the previous row? LAG() has got you covered. It's an excellent tool for tracking variations in key metrics. How does it work? LAG() works as a window function, meaning it operates across a set of rows defined by a partition and ordered by a column. You can specify how many rows back you want to look, and even set a default value for cases where no previous row exists. LAG() is a must-have in your SQL toolbox if you're working with time-series data or performing comparative analysis across sequential rows. Whether you're in finance, retail, or tech, mastering this function will give you a new level of insight into your data. Got questions or want to learn more about SQL functions? Feel free to connect and drop a comment below! 👇 #SQL #DataAnalytics #BusinessIntelligence #DataScience #CareerInTech #SQLWindowFunctions #LAGFunction #TechSkills #SQLTips

🌟 SQL Queries in Action: Simplifying Data Analysis 🌟 Data drives decisions, and mastering SQL helps me dig deep into that data to uncover valuable insights. Here's a recent example of how SQL made a difference: 🔍 Scenario: [Briefly explain a business problem or task] 💡 Solution: I used SQL to query the database and extract [specific data]. ⚙️ Query Example: SELECT department, SUM(revenue) AS total_revenue FROM sales_data GROUP BY department ORDER BY total_revenue DESC; 📊 This helped the team quickly identify the top-performing departments, driving focused strategy decisions. SQL is a powerful tool in my toolkit that makes working with data seamless, efficient, and insightful. Excited to keep improving and growing my skills in database management! 💻✨ #SQL #DataAnalysis #DatabaseManagement #DataDriven #TechSkills #ProblemSolving #SQLQuery