Zainab Makrani’s Post

Nobody warns you about what real data actually looks like. The assumption going in is that data analysis works like this: get a dataset, run some numbers, get an answer. The reality is very different. Before any analysis can happen, the data has to be cleaned. That means going through the dataset and dealing with missing values, duplicate entries, and information that just does not make sense. Working with over 2 million records, not every row was usable. Some had missing information. Some had conflicting entries. None of that is visible in the final dashboard. But without that step, nothing that comes after it can be trusted. And that is the lesson. The quality of the insight is only as good as the quality of the data going in. A beautiful chart built on messy data is just a beautiful mistake. Once the data was clean, patterns started to emerge. Findings that challenged the obvious assumptions. The kind of insights that only show up when the groundwork has been done properly. That unglamorous, time consuming prep work is what makes everything else possible. It just never makes it into the highlight reel. 📊 #DataAnalytics #DataCleaning #PowerBI #SQL #LearningInPublic #DataAnalyst #OpenToWork

🚀 Day 7/100 ~ The "WHERE" Clause One of the most important skills every data analyst must develop is the ability to filter what truly matters from a dataset. In data analysis, more data doesn’t mean better insights. What matters is relevance. Today, we will focus on the SQL WHERE clause. A simple yet powerful tool that helps filter data and focus only on what’s important. 🔍 Why this matters: It removes unnecessary noise It sharpens decision-making It allows you to answer precise questions The SELECT and WHERE clauses go hand in hand in SQL — one retrieves data, the other refines it #Opentowork #100daysofdataanalytics #sql #100dayschallenge

🚀 Day 9 – Data Analyst Journey Today’s focus was strengthening my foundation in Excel and SQL, working on both data handling and database design concepts. 📊 Excel Skills Covered: Mastered AVERAGE functions for data summarization Created Drop-down lists using Data Validation for cleaner inputs Applied Conditional Formatting to highlight key insights Used Freeze Panes to improve data navigation Explored Split functionality to organize raw data effectively 🗄️ SQL Concepts Learned: Different ways of creating tables (basic & advanced structures) Used ALTER to modify existing tables Practiced WHERE and ORDER BY for filtering & sorting data Understood Constraints (PRIMARY KEY, NOT NULL, UNIQUE) Deep dive into FOREIGN KEY relationships Learned ON DELETE CASCADE for maintaining referential integrity Explored Soft Delete strategy for safer data handling Used DEFAULT constraints for setting initial values Studied Keys in DBMS: Minimal Key Natural Key Surrogate Key 💡 Today helped me understand how structured data is created, maintained, and optimized both in Excel and relational databases. #DataAnalytics #SQL #Excel #LearningJourney #FutureDataAnalyst #PlacementPrep

One SQL function every Data Analyst must know 👇 RANK() 🎯 Simple concept. Powerful output. RANK() skips numbers on a tie! 🤯 And its two cousins 👇 → DENSE_RANK() — No gaps in ranking 🔢 → ROW_NUMBER() — Always unique, no ties 🔑 Quick memory trick 🧠 🏅 RANK() = Olympics rankings — gaps exist 📦 DENSE_RANK() = Packed tight — no gaps 🔑 ROW_NUMBER() = Always unique — no ties One interview rule that will save you ⚠️ Never filter RANK() directly in WHERE clause. Always wrap it in a CTE first. 💡 Save this. Thank me later. 😄 Found this helpful? Like and share so others benefit too! 🙌 #SQL #DataAnalyst #DataAnalytics #SQLTips #LearningInPublic #DataScience #Upskilling #CareerGrowth #OpenToWork #SQLServer #MySQL #InterviewPrep

Happy to see you all again with the most important and often asked question Excel or SQL? Knowing the difference is a Data Analyst’s real superpower. A common question we all ask and hear is: "If I am a pro at Excel, why do I need SQL?" Or in other words “Should I learn SQL or Excel alone is enough?” After 4 years in retail operations and cataloging, I haveseen firsthand where Excel shines and where it starts to struggle with scale. In my journey to becoming a Data Analyst, I have learned that it is not about which tool is "better”, it is about choosing the right tool for the specific business problem. Here is how I break it down: ✅ Use Excel when: • You need quick, ad-hoc calculations or one-off reports. • The dataset is small to medium. • You need to create flexible, immediate visualizations for a quick stakeholder update. 🔥 Use SQL when: • You are dealing with "Big Data" (Millions of rows? No problem). • Data Integrity is a priority: Ensuring rules are not broken and data remains consistent. • Automation: Writing a query once to handle the heavy lifting every single day. The Reality: While Excel is the "Shot Gun with medium range” of the office, SQL is the "Machine gun with long range and multiple rounds” of data infrastructure. To land a role in 2026, you need to be comfortable switching between both. #DataAnalytics #SQL #Excel #CareerTransition #DataScience #BusinessIntelligence #MondayMotivation #TDC #Techdatacommunity #praveenkalimuthu

Mastering SQL is a game-changer for any Business Analyst. 🚀 If you want to move beyond basic spreadsheets and truly unlock the power of your data, these core commands are your best starting point. Here is a quick cheat sheet of the essential SQL operations every BA should know: SELECT: The foundation to fetch your data. WHERE: Filter out the noise and find specific records. GROUP BY: Aggregate your data for high-level insights. ORDER BY: Sort your results to spot trends easily. HAVING: Filter your newly aggregated groups. JOIN: Combine multiple tables to see the full picture. CASE: Apply conditional if/then logic directly in your queries. SUBQUERIES: Nest queries for complex, multi-step analysis. Save this infographic for your next data project! 💡 #SQL #BusinessAnalyst #DataAnalytics #DataScience #TechCareers #DataSkills

🚀 Day 2 of My Data Analyst Journey – SQL Practice 💡 Ever wondered how companies identify their most valuable customers? Today, I worked on a SQL problem to find the top-performing customers based on total spending. 🧠 Problem: Find the top 2 customers who have spent the highest total amount from an orders dataset. 💻 SQL Query: SELECT customer_id, SUM(total_amount) AS total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 2; 📊 Output: See results below 👇 • Customer 1 → ₹67,450 • Customer 2 → ₹3,500 📈 Key Learnings: ✔ Used GROUP BY to aggregate customer data ✔ Applied ORDER BY to rank customers ✔ Used LIMIT to extract top results ✔ Learned how SQL supports real business decisions I’m improving my SQL skills step by step by solving real-world problems. 💬 Feedback and suggestions are always welcome! #SQL #DataAnalytics #LearningInPublic #CareerGrowth #OpenToWork

𝗦𝗤𝗟 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵𝗲𝘀𝘁-𝗥𝗢𝗜 𝘀𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗮𝗻𝘆 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁. You do not need to memorize every advanced function. But you should be comfortable with the basics that help you ask better questions from data. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟱 𝗦𝗤𝗟 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄: 𝟭. 𝙎𝙀𝙇𝙀𝘾𝙏 - Used to choose the columns you want to analyze. 𝟮. 𝙁𝙍𝙊𝙈 - Tells SQL which table your data is coming from. 𝟯. 𝗪𝗛𝗘𝗥𝗘 - Filters your data so you only work with relevant records. 𝟰. 𝙂𝙍𝙊𝙐𝙋 𝘽𝙔 - Helps summarize data by category, like sales by region or users by month. 𝟱. 𝙊𝙍𝘿𝙀𝙍 𝘽𝙔 - Sorts your results so patterns are easier to spot. 𝗪𝗵𝘆 𝗱𝗼 𝘁𝗵𝗲𝘀𝗲 𝗺𝗮𝘁𝘁𝗲𝗿? Because most analysis starts with a simple question: “What happened?” 𝘚𝘘𝘓 𝘩𝘦𝘭𝘱𝘴 𝘺𝘰𝘶 𝘢𝘯𝘴𝘸𝘦𝘳 𝘵𝘩𝘢𝘵 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 𝘤𝘭𝘦𝘢𝘳𝘭𝘺, 𝘲𝘶𝘪𝘤𝘬𝘭𝘺, 𝘢𝘯𝘥 𝘳𝘦𝘱𝘦𝘢𝘵𝘢𝘣𝘭𝘺. 𝘔𝘢𝘴𝘵𝘦𝘳 𝘵𝘩𝘦 𝘣𝘢𝘴𝘪𝘤𝘴 𝘧𝘪𝘳𝘴𝘵. The advanced stuff becomes much easier later. CTA: Save this post if you’re learning SQL, and comment “SQL” if you want a beginner-friendly roadmap. #SQL #DataAnalytics #DataAnalyst #Analytics #CareerGrowth

Nobody handed me a dashboard. I was in the middle of a production issue invoices failing silently for an enterprise client. No alerts. No pattern. Just pressure and raw logs. I opened SQL. Started digging. Found a schema mismatch buried deep only triggered at a certain volume threshold. Had been sitting there for weeks. Fixed it before the client felt the real damage. That wasn’t a “data analytical task.” That was Tuesday. But that moment is exactly why I went all in on this path. Since then I stopped waiting for someone else to pull the numbers. I started learning the tools, asking better questions, and building the habit of finding answers before they become problems. Not because it looks good. Because I’ve seen firsthand what it costs when nobody in the room can answer the question. Data skills aren’t just for analysts. They’re for anyone who wants to stop guessing. #DataAnalytics #SQL #PowerBI #CareerGrowth #TechCareers #Vancouver

# SQL Tip for Data Analysts: STOP using only GROUP BY — Start using WINDOW FUNCTIONS! When I started learning SQL, I used to solve everything with GROUP BY. But then I realized… it limits your analysis. # Example Problem: 👉 Find each customer's total spending AND compare it with other customers. Most beginners do this: SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id; ✔️ This works, but… You cannot compare results within the same query. Better Approach: WINDOW FUNCTION SELECT customer_id, SUM(amount) AS total_spent, RANK() OVER (ORDER BY SUM(amount) DESC) AS spending_rank FROM orders GROUP BY customer_id; What changed? ✅ You still get total spending ✅ PLUS you get ranking without losing row-level detail Why this matters: Used in real MNC projects Helps in dashboards (Power BI / Tableau) Frequently asked in interviews # Pro Tip: Learn these 3 window functions first: ✔️ RANK() ✔️ ROW_NUMBER() ✔️ SUM() OVER() If you're learning SQL, focus on: Writing queries that give INSIGHTS, not just outputs #SQL #DataAnalytics #LearningSQL #DataAnalyst #CareerGrowth