Shubham Pandey’s Post

🚀 I thought I understood data… until I realized I was calculating it wrong Early on, my approach was simple: If the query runs If the dashboard looks clean If the numbers seem consistent 👉 Then it must be correct Turns out, that’s a dangerous assumption. I came across a case where everything looked perfect — no missing data, no errors, clean trends. But the metric was still wrong. The issue? 👉 Aggregation at the wrong level Fixing that changed the number by ~16%. Same data. Completely different outcome. That’s when I realized: 👉 Data doesn’t fail loudly 👉 It fails silently And the scariest part? Most incorrect metrics still look correct. Since then, I’ve stopped just writing queries — and started questioning the logic behind them. Curious — what’s one mistake that changed how you look at data? #DataAnalytics #SQL #DataEngineering #AnalyticsEngineering #DataQuality #BusinessIntelligence #LearningInPublic

The moment you finish a data analysis course, you just feel pumped. Like yeah, I'm ready now. Everything made sense, the examples were clean, the steps looked straightforward and you're even excited to finally work with real data. Then you open a dataset... And you're like: "oh my God, what's all this??" Missing values everywhere, wrong data types, the same thing spelt in 3 different ways, columns that don't even make sense at first glance. At that point, analysis is not even the problem again.😭 You have to start cleaning first — fixing, checking, correcting — just to get the data into a state you can actually work with. And this is the part people overlook or try to rush through. But if your data is messy, everything you build on it will be wrong. Your insights won't hold and your decisions won't stand. So you sit with it, take your time and clean it properly. Only then can you move to the analysis — asking the right questions, finding patterns, getting insights that actually mean something. It's funny because a course makes you feel ready, but real data will humble you instantly. That's when you realize the course prepared you, but the data will teach you. Can anyone relate? Drop it in the comments 👇 #DataAnalysis #DataCleaning #DataAnalytics #DataQuality #DataJourney #DataSkills

One thing I didn’t expect when working with data: Most problems aren’t clearly defined. There’s no perfect dataset. No exact question. No clean starting point. Earlier, that used to slow me down. I’d spend time trying to figure out: “What exactly am I supposed to find?” But now I approach it differently. Instead of waiting for clarity, I start with: → What does this system look like overall? → What could possibly go wrong here? → If something is inefficient, where would it show up first? From there, the analysis starts to take shape. Not because the data is perfect, but because the direction becomes clearer. That shift made a big difference. Because in real scenarios, you’re not given a problem statement. You’re expected to define it. And honestly, that’s the part I’ve started enjoying the most. Curious - how do you usually approach analysis when the problem isn’t clearly defined? #DataAnalytics #SQL #PowerBI #ProblemSolving #BusinessAnalytics

If I had to approach any dataset today, this is the simple framework I’d follow: 1. Understand the problem → What question am I trying to answer? 2. Explore the data → What columns exist? → Any missing or unusual values? 3. Clean the data → Handle nulls → Remove duplicates → Fix inconsistencies 4. Analyze → Write queries → Find patterns and trends 5. Validate → Does the result actually make sense? → Cross-check assumptions 6. Communicate → Present insights clearly → Focus on what matters Earlier, I used to jump straight to step 4. Now I’m realizing the real work happens before and after that. Still refining this approach, but it’s already helping me stay more structured. Do you follow a similar process, or something different? 👇 (Feel free to save this if it helps) #DataAnalytics #SQL #DataThinking #Learning #DataWorkflow

𝟰𝟬% 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗱𝗮𝘁𝗮. 𝗧𝗵𝗮𝘁 𝗻𝘂𝗺𝗯𝗲𝗿 𝗺𝗮𝗸𝗲𝘀 𝗺𝗼𝘀𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗳𝗿𝗲𝗲𝘇𝗲. The default reaction is always the same, drop it or throw a mean imputation at it and move on.  Here's what I actually do when I hit missing data at scale: 𝙁𝙞𝙧𝙨𝙩, 𝙄 𝙨𝙩𝙤𝙥 𝙖𝙣𝙙 𝙡𝙤𝙤𝙠 𝙗𝙚𝙛𝙤𝙧𝙚 𝙄 𝙩𝙤𝙪𝙘𝙝 𝙖𝙣𝙮𝙩𝙝𝙞𝙣𝙜. Which columns are affected? Is it scattered or concentrated? Are critical fields like IDs or prices involved? A missing optional tag is nothing. A missing primary key is a crisis. 𝙏𝙝𝙚𝙣 𝙄 𝙖𝙨𝙠 𝙬𝙝𝙮 𝙞𝙩'𝙨 𝙢𝙞𝙨𝙨𝙞𝙣𝙜 𝙣𝙤𝙩 𝙟𝙪𝙨𝙩 𝙬𝙝𝙚𝙧𝙚. This is the part most people skip. Missing data has patterns: Sometimes it's random noise (MCAR) Sometimes it correlates with other variables (MAR) Sometimes the value being missing is the signal (MNAR) Treating all three the same way leads to biased results that look clean on the surface. 𝘼𝙛𝙩𝙚𝙧 𝙩𝙝𝙖𝙩, 𝙄 𝙢𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙛𝙞𝙭 𝙩𝙤 𝙩𝙝𝙚 𝙨𝙞𝙩𝙪𝙖𝙩𝙞𝙤𝙣. High missingness in a non-critical column? Drop it. Critical column with 35% missing? That's not a data cleaning problem — that's a pipeline conversation. For moderate gaps, mean/median works for numbers; mode or an explicit "Unknown" category for categorical. 𝙏𝙝𝙚 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 𝙄 𝙖𝙡𝙬𝙖𝙮𝙨 𝙚𝙣𝙙 𝙬𝙞𝙩𝙝: why did this happen upstream? Cleaning missing data without fixing the source just means you'll be cleaning it again next month. In finance, imputing values is often a non-starter. The decision isn't technical it's a judgment call based on what the data is actually being used for.  #DataAnalytics #DataScience #Analytics #InterviewPrep

That moment you open a dataset and immediately see… Missing values everywhere. Column names like Column1, Column2, Final_Final_v2 Dates in three different formats. Numbers stored as text. Duplicates smiling at you. And suddenly… you realize you're not doing analysis today. You're doing data cleaning. 😭 In reality, data analysts some times spend more time cleaning than actually analyzing. And that's where the real work happens. Because once the data is clean, Everything else becomes easier. Learn how to clean data well. What’s the worst dataset you’ve ever worked with?

🚀𝗦𝗤𝗟 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗲𝗿𝗶𝗲𝘀 #𝟭𝟳: 𝗣𝗜𝗩𝗢𝗧🚀 In the world of data analysis, we often deal with Tall Data (rows upon rows of repeated categories). While great for storage, it’s a nightmare for side-by-side comparisons. That’s where the SQL PIVOT clause comes in. It’s the "magic trick" of SQL that transforms your rows into columns, turning messy logs into clean, executive-ready reports. 🛠️ 𝗧𝗵𝗲 𝟯-𝗦𝘁𝗲𝗽 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: → Identify the Pivot Point: Which column’s values (like 'Month' or 'Region') should become your new headers? → Choose your Aggregation: Do you want to SUM sales, COUNT leads, or AVG scores? → The Flip: SQL rotates the data, grouping everything by your remaining attributes (like 'Product') and filling the new columns with your calculated values. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁?  ❌ From: "I can't tell which month performed better." ✅ To: Clear, horizontal trends that even a non-technical stakeholder can read in seconds. Follow Vipin Puthan for more Data and AI content ♻️ If this information is useful to you, you're welcome to... 🤝 React 🧑💻 Comment 🔄 Share #SQL #DataAnalytics #Database #CodingTips #DataVisualization #TechCommunity #DataTest #ETL #DataTestAutomation

For a long time, I thought being fast with data was a good thing. • Write the query quickly. • Build the dashboard fast. • Move to the next task. What I eventually learned is this: Speed doesn’t matter if you don’t understand what you’re looking at. Every time I rushed, I missed something: • a wrong assumption in the data   • a number that didn’t make sense   • a detail that changed the whole picture  When I slowed down, things improved: • fewer mistakes   • cleaner logic   • clearer outputs  Now I spend more time understanding before doing. It feels slower. But the result is better. Data work isn’t about moving fast. It’s about getting it right. #dataanalytics #datascience #sql

Garbage In, Garbage Out: The Real Hurdles of Data Preparation Data is often called the "new oil," but before it can power any engine, it needs to be refined. Data scientists and analysts spend up to 80% of their time just preparing data and for good reason. While the process is critical, it’s rarely smooth sailing. Here are 5 common challenges we face during the data prep phase: 🔍 Insufficient Data Profiling: Without a deep dive into data characteristics early on, you risk overlooking biases and errors that lead to flawed analytical findings. 🧩 Incomplete Data: Missing values are more than just gaps; if not handled from the start, they compromise the integrity of your entire analysis. 🚫 Invalid Values: From typos to illogical numeric inputs, "dirty data" must be scrubbed early to ensure your results are grounded in reality. ⚖️ Lack of Standardization: Combining datasets is a nightmare without standardized formats (e.g., names and addresses). Consistency is key to successful data integration. 💎 Enrichment Hurdles: Knowing which external data will actually add value requires a unique blend of technical skill and deep business intuition. Data preparation isn't just a preliminary step; it’s the foundation of every reliable insight. What is your biggest headache when it comes to cleaning data? Let’s discuss below! 👇 #DataAnalytics #DataScience #BigData #DataCleaning #BusinessIntelligence #Business Analytics