Shaikh Mohammad Saaqib Aasim’s Post

I used to think SQL was complicated… But the real problem was simpler than I thought. I wasn’t filtering my data properly I used to run queries on full datasets which made analysis slower and confusing. Then I started using WHERE conditions effectively: • Focus only on relevant data • Reduce unnecessary rows • Get faster and clearer insights Example: SELECT * FROM sales WHERE revenue > 10000; This small change made my queries: ✔ Faster ✔ Cleaner ✔ Easier to understand Now I spend less time searching data and more time analyzing it 📊 Still learning SQL every day as I move into Data Analytics What’s one SQL tip that improved your workflow? #SQL #DataAnalytics #LearningJourney #Analytics

Week 6 of my Data Analytics Journey Last week, I got introduced to SQL, and I won’t lie, it was confusing at first 😅 I kept asking myself: What exactly am I creating? What am I selecting? But after going back to the class recordings and practicing, things started to make more sense. I learned how to: - Create a database and tables - Define columns and set a primary key - Use SELECT to choose the table I want to work with - Insert values into a table - Delete a row using the primary key. It’s still early, but I’m starting to understand how data is actually stored and managed. Slowly moving from confusion to clarity, one step at a time. @TechCrush.pro #RisewithTechCrush #Tech4Africans #LearningwithTechCrush #DataAnalytics #SQL #LearningJourney #Beginner #Growth #TechSkills

👉 SQL Series: GROUP BY – The Key to Data Aggregation ➡️ GROUP BY - Groups data based on a column. ◾️GROUP BY clause is used to group rows that have the same values in specified columns and is often used with aggregate functions like COUNT(), SUM(), AVG() etc. 📌 Always ensure that GROUP BY comes after the FROM clause. 🔹 Key Points: ❗️GROUP BY comes after WHERE clause and before ORDER BY ❗️It aggregates your data into meaningful summaries ❗️It combines rows with the same values ❗️It helps aggregate one column based on another column 📍 Every column in SELECT must either be: • Included in GROUP BY • Or used with an aggregate function Understanding GROUP BY is essential for analyzing and summarizing real-world datasets. ✅️ Aggregated Data ➡️ Better Insights ✅️ #SQL #DataAnalytics #LearnSQL #SelfLearning #CareerTransition

🚀 DAY 17/30 – SQL Value Functions (Game Changer 🔥) If you’re still writing complex self-joins to compare rows… 👉 It’s time to upgrade your SQL with Value Functions 💡 💥 Today’s Focus: ✔ LAG() → Previous row value ✔ LEAD() → Next row value ✔ FIRST_VALUE() → First value in group ✔ LAST_VALUE() → Last value in group 🧠 Why this matters? Because real-world data problems are not just about totals… 👉 They are about comparison, trends, and insights What you can do with this: ✔ Compare current vs previous sales 📈 ✔ Track growth/decline over time ✔ Identify trends in data ✔ Find gaps and anomalies ✔ Build powerful analytics without joins 💬 Big Insight: SQL is not just about retrieving data… 👉 It’s about understanding the story behind the data #SQL #DataAnalytics #WindowFunctions #LearningInPublic #DataAnalyst

🚀 Day 18 of My Data Analytics Journey Today’s focus was on grouping and aggregation in SQL—learning how to summarize data to extract meaningful insights. I worked with the GROUP BY clause alongside aggregate functions like SUM, COUNT, AVG, MIN, and MAX to analyze datasets more effectively. This helped me understand how to break down large volumes of data into smaller, meaningful summaries. I also practiced using HAVING to filter grouped data, which made it possible to focus only on relevant results after aggregation. This step showed me how powerful SQL can be when it comes to analyzing trends and patterns within datasets. What stood out to me is that aggregation transforms raw data into valuable information, making it easier to interpret and support decision-making. I’m becoming more confident in using SQL to not just retrieve data, but to truly analyze it. #DataAnalytics #SQL #DataAggregation #LearningJourney #Day18 #DataDriven

🚀 SQL Scenarios – Day 09 Continuing my journey of solving real-world SQL problems to strengthen my data analytics skills 💻📊 📌 Today’s Focus: ✔️ GROUP BY & Aggregation ✔️ Window Functions using PARTITION BY ✔️ Running Totals in SQL ✔️ Rolling Window Analysis 🔹 What I Practiced Today: 👉 Calculated total sales per salesperson using GROUP BY and SUM() 👉 Learned how Window Functions help retain row-level details while performing aggregations 👉 Created running totals using ORDER BY inside OVER() for trend analysis 👉 Built rolling sales calculations using ROWS BETWEEN for short-term performance tracking 💡 Key Takeaway: Window Functions are one of the most powerful SQL concepts for real-world analytics because they allow advanced calculations without losing detailed data visibility. Grateful to Ankit Bansal and Shashank Singh 🇮🇳 Singh for the valuable insights 🙌 #SQL #DataAnalytics #DataAnalyst #SQLPractice #WindowFunctions #LearningJourney #InterviewPreparation #Analytics #RunningTotal #SQLTips

🚀 Day 49 of SQL Series – SQL for Trend Analysis Data is not just numbers… It tells a story over time 📈 SQL helps you uncover that story 👇 📊 Example Dataset: date | sales 🎯 3 Key Questions You Can Answer: 1️⃣ Daily Sales Trend SELECT date, SUM(sales) AS total_sales FROM orders GROUP BY date ORDER BY date; 2️⃣ Monthly Growth SELECT DATE_TRUNC('month', date) AS month, SUM(sales) AS monthly_sales FROM orders GROUP BY month ORDER BY month; 3️⃣ Growth Rate (MoM %) SELECT month, monthly_sales, LAG(monthly_sales) OVER (ORDER BY month) AS prev_month, (monthly_sales - LAG(monthly_sales) OVER (ORDER BY month)) / LAG(monthly_sales) OVER (ORDER BY month) * 100 AS growth_percent FROM ( SELECT DATE_TRUNC('month', date) AS month, SUM(sales) AS monthly_sales FROM orders GROUP BY month ) t; 💡 What you learn: ✔ Is your business growing or declining ✔ Seasonal patterns 📊 ✔ Sudden spikes or drops #SQL #DataAnalytics #TrendAnalysis #BusinessAnalytics #LearnSQL #DataScience #Analytics #SQLTips #DataAnalyst #Growth

🚀 SQL Scenarios – Day 07 Continuing my journey of solving real-world SQL problems to strengthen my data analytics skills 💻📊 📌 Today’s Focus: ✔️ Custom sorting using CASE WHEN ✔️ Fixing running total calculations using window functions 🔹 What I Practiced Today: 👉 Learned how to force priority rows like “India” to appear first in dashboards using custom sorting 👉 Used CASE WHEN inside ORDER BY to control business-driven report presentation 👉 Understood why running totals behave incorrectly with duplicate values 👉 Fixed cumulative sum issues using ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 💡 Key Takeaway: Small changes in SQL logic can completely improve reporting accuracy and dashboard presentation — especially in real-world analytics projects. Grateful to Ankit Bansal and Shashank Singh 🇮🇳 Singh for the valuable insights 🙌 #SQL #DataAnalytics #DataAnalyst #SQLPractice #WindowFunctions #LearningJourney #InterviewPreparation #Analytics #SQLTips #LinkedInLearning

📊 SQL Lesson: HAVING vs WHERE Day 7 of my BI journey, and today I learned a small SQL concept that makes a big difference: 👉 WHERE and HAVING are both used to filter data… but they work at different stages. 🔹 WHEREUsed to filter rows before grouping or aggregation. Example: SELECT customer_id, sales FROM orders WHERE sales > 100; 👉 This filters individual rows first. 🔹 HAVINGUsed to filter results after GROUP BY and aggregation. Example: SELECT customer_id, COUNT(*) AS total_orders FROM orders GROUP BY customer_id HAVING COUNT(*) > 3; 👉 This filters grouped results. 💡 Easy way to remember: WHERE = filter rows HAVING = filter groups Since aggregate functions like COUNT(), SUM(), and AVG() usually work with grouped data, HAVING becomes essential. Small concept, but knowing when to use each one makes your SQL much stronger 🚀 #SQL #DataAnalytics #BusinessIntelligence #LearningInPublic #BIJourney

Day 5 of my Data Analytics journey I’m starting to realize something important: Writing SQL queries is easy. Writing good queries is not. Understanding when to use JOINs, how to filter efficiently, and how tables are structured makes all the difference. Still early, but I’m focusing on building strong fundamentals instead of rushing ahead. Consistency continues. #SQL #DataAnalytics #LearningJourney