Akshay Rathod’s Post

Everyone wants to build dashboards. But dashboards are not where analysis starts — they’re where the story ends. Real data work happens in the messy middle: ✅ Cleaning incomplete and inconsistent data ✅ Writing efficient SQL that scales ✅ Doing EDA to uncover patterns ✅ Understanding business context before building visuals ✅ Turning raw data into decisions A good dashboard doesn’t create insights. Good analysis does. After 2+ years in data, one lesson stands out: Strong foundations beat flashy visuals. Every time. Still learning. Still building. 🚀 What do you think is the most underrated skill in data analytics? #DataAnalytics #DataAnalyst #SQL #PowerBI #Python #EDA #BusinessIntelligence #Analytics

Your dashboard looks perfect. But your data might be completely wrong. And that’s more common than you think. Most beginner analysts: → Build charts → Add filters → Share dashboards But skip one critical step: **validating the data** Here are 3 quick checks I always do before any analysis 👇 1️⃣ Check for missing values → Are there nulls in important columns? → Missing data can silently distort results 2️⃣ Check for duplicates → Especially in transaction data → Duplicates = inflated numbers 3️⃣ Check for outliers → Sudden spikes or drops → Could be real… or just bad data Example: Revenue suddenly increased by 40%. Looks great, right? But after validation: → Duplicate entries were found in orders → Actual growth was only 8% That’s the difference between: “Celebrating fake growth” and “Making real decisions” Remember: If your data is wrong, your insights are useless. Before your next dashboard, ask: 👉 “Can I trust this data?” #DataAnalytics #DataAnalyst #PowerBI #SQL #Python #Analytics #DataQuality #BusinessThinking

Day 05 of learning Data Analyst 🚀 Today I explored one of the most powerful tools in Excel — Pivot Tables. At first, it looked complex… but once I understood it, everything became much easier 👇 🔹 What is a Pivot Table? It helps to summarize and analyze large datasets in a simple and structured way. 🔹 What I learned today: • Converting raw data into meaningful summaries • Using Rows, Columns, and Values to organize data • Finding totals, averages, and counts instantly • Grouping data (like monthly or category-wise analysis) 🔹 Real Example: I took a sample sales dataset and quickly found: • Total sales by product • Monthly performance • Top-performing categories 💡 Key Insight: Instead of manually calculating everything, Pivot Tables can do it in seconds. This is why it’s one of the most important tools for any Data Analyst. Learning step by step and getting better every day 💪 #DataAnalytics #DataAnalyst #LearningInPublic #CareerGrowth #DataScience #Analytics #Excel #SQL #Python #DataVisualization

📊 Data Analysis isn’t about tools. It’s about thinking. Anyone can build dashboards. But not everyone can turn data into decisions. 💡 Instead of: “Sales dropped by 12%” Say: “Sales dropped due to declining repeat customers — a retention issue.” 👉 That’s the difference between reporting and insight. 📈 Data is everywhere. Insight is rare. What matters more: tools or mindset? #DataAnalytics #DataAnalyst #DataScience #PowerBI #SQL #Python #DataVisualization #Analytics #CareerGrowth

The fastest way to embarrass yourself in front of stakeholders? Build a dashboard without doing this first. 📉 Too many analysts get a new dataset and jump straight into writing complex SQL queries or building flashy Power BI dashboards before they even understand what they are looking at. Big mistake. If you aren't doing Exploratory Data Analysis (EDA) first, you are flying completely blind. Here is why skipping EDA is the fastest way to present the wrong numbers to your stakeholders: 🔹 You'll Miss the "Gotchas": EDA exposes the hidden outliers, sneaky null values, and weird distributions that will completely skew your averages if left unchecked. 🔹 You're Guessing, Not Analyzing: You might think revenue spikes on weekends. EDA forces you to prove it statistically before you embarrass yourself in a meeting. 🔹 You'll Miss the Real Story: It uncovers the hidden correlations and trends that are physically impossible to see just by staring at rows in Excel. 🔹 It Dictates Your Next Move: Understanding the shape of your data tells you exactly how it needs to be cleaned and what models will actually work. The Bottom Line: EDA isn't a "nice-to-have" preliminary step. It is the absolute foundation of your entire analysis. 💬 What is the very first thing you do when you get your hands on a new dataset? A simple scatter plot? A correlation matrix? Let me know below! 👇 #DataAnalytics #DataScience #EDA #DataStrategy #Python #SQL #LearningInPublic

One of the biggest mistakes in analytics is only explaining what happened. Businesses care more about what’s likely to happen next. I worked on a project where teams were reacting to operational issues after they had already happened. Inventory delays. Resource planning issues. Missed forecasting targets. Everyone had reports showing historical performance… But no one had visibility into future demand patterns. So I worked on improving forecasting visibility. Here’s what I did: • Used Python (Pandas + forecasting models) to analyze historical trends • Identified seasonality and recurring demand patterns • Built forecasting models to estimate future operational needs • Created Power BI dashboards to help stakeholders monitor forecast vs actual performance • Highlighted risk areas where planning teams needed to act early The result? Better planning decisions Reduced reactive firefighting Improved operational visibility Big takeaway: 👉 Analytics becomes far more valuable when it helps teams act before problems happen. Descriptive analytics explains the past. Predictive analytics helps shape the future. Curious to hear from others: Have you worked on forecasting projects that changed business decisions? #DataAnalytics #Forecasting #Python #SQL #BusinessIntelligence #PredictiveAnalytics #PowerBI #DataScience #MachineLearning #AnalyticsEngineering #DataDrivenDecisionMaking #TechCareers #OperationsAnalytics #BigData #DataStrategy

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝟗𝟎 — 𝐃𝐚𝐲 9: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬t 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬— extreme or unusual values — can heavily influence analysis results if not handled correctly. Identifying and managing them is essential for building reliable and trustworthy insights. 🔍 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐦𝐞𝐭𝐡𝐨𝐝𝐬: Box plots, scatter plots, and histograms help spot unusual patterns at a glance. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: Methods like Z-scores and the Interquartile Range (IQR) highlight values that fall far from the normal range. 𝐑𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬 (𝐖𝐡𝐞𝐧 𝐀𝐩𝐩𝐫𝐨𝐩𝐫𝐢𝐚𝐭𝐞) 𝐓𝐫𝐢𝐦𝐦𝐢𝐧𝐠: Eliminating a small percentage of the most extreme values from both ends of the dataset. 𝐖𝐢𝐧𝐬𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Limiting extreme values by replacing them with the nearest acceptable percentile. 𝐂𝐚𝐩𝐩𝐢𝐧𝐠 𝐄𝐱𝐭𝐫𝐞𝐦𝐞 𝐕𝐚𝐥𝐮𝐞𝐬 Define upper and lower limits and replace values outside these boundaries with predefined cutoff points. 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐠 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Useful for reducing skewness and minimizing the influence of very large values. 𝐒𝐪𝐮𝐚𝐫𝐞 𝐫𝐨𝐨𝐭 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Another effective approach for moderating extreme variations. 𝐈𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐌𝐞𝐚𝐧 𝐨𝐫 𝐦𝐞𝐝𝐢𝐚𝐧 𝐢𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧: Replacing extreme values with a central tendency measure. 𝐊𝐍𝐍 𝐢𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧: Using similar data points to estimate a more reasonable value. 🧠 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐌𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬: Rare but valid events should often be retained. Data errors: Outliers caused by measurement or entry errors can be corrected or removed. ✅ 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 There’s no one-size-fits-all solution. The right technique depends on: How extreme the outliers are How frequently they occur Their impact on the analysis And, most importantly, domain knowledge 🔑 Thoughtful handling of outliers leads to more accurate models and better decision-making. Follow Sudeesh Koppisetti for such informative content on data analytics #DataAnalytics #DataAnalyst90 #SQL #Python #PowerBI #CareerGrowth #LearningResources #Books #DataPipelines #LinkedInLearning #PersonalGrowth #TechJourney

#️⃣ What Next After Analysis 🪜 As I climb the ladder of Data Analytics, I've come to realize that every step of the way teaches you something helpful, distinct, and peculiar. At first, I was much focused on knowing everything about the tools; Excel, Power BI, SQL, Python, and what have you... ✅ After building one or two dashboards, I was stuck at making meaningful insights from my data, at a glance. That is where what Senior Analysts in the system say concerning data storytelling became relevant to me, and indeed, it is. ✅ Create all the fancy dashboards, know all the necessary tools, but, Profit-driven companies, care less about your tools, though you need them, all they care most is how you create insights from data, that they can take actionable decisions on it. ✅ Be passionate, and much focused about what problem the data would help solve.

🧠 SQL is the language of data — and here’s your starter pack 📦 Whether you're analyzing customer behaviour, building dashboards, or cleaning messy datasets, these six commands are your daily essentials: 🔹 SELECT - Grab the data you need 🔹 WHERE - Filter out the noise 🔹 ORDER BY - Sort your results 🔹 GROUP BY - Summarize by category 🔹 JOIN - Connect the dots across tables 🔹 LIMIT - Keep it concise I created this visual cheat sheet to help fellow learners and aspiring analysts quickly reference the fundamentals. It’s simple, practical, and saves time when you're deep in query mode. 💬 If you're just starting out, master these first. If you're already using them, what’s your favourite SQL trick that makes your workflow smoother? Let’s make data work smarter 💡 #SQL #DataAnalytics #DataAnalyst #SQLTips #SQLCheatsheet #PowerBI #Excel #Python #BusinessIntelligence #DataScienceCommunity #TechTips #CareerGrowth #LearningSQL #Codebasics #DataDriven

Data storytelling is what separates a normal report from a powerful dashboard. Choosing the right chart is not just about visuals, it’s about communicating the right message. Whether it’s comparing data, showing trends, or identifying patterns, each chart has a purpose and using it correctly makes your analysis more impactful. This is one of the most important skills in data analytics that most people ignore. If you want to learn how to create meaningful dashboards and tell stories with data, I’m starting a complete Data Analytics batch where we cover Advanced Excel, Power BI, SQL, and Python from basic to advanced level with practical training. If you’re interested in joining, comment interested and I will share the details with you. For more learning content, visit www.alidataanalytics.com #DataAnalytics #DataStorytelling #DataVisualization #PowerBI #Excel #SQL #Python #DataSkills #AliAhmad