Shaikh Mohammad Saaqib Aasim’s Post

📊 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

In my previous post, I talked about Cumulative Sum 📊:https://lnkd.in/gAZZkrTd — showing overall growth by adding values over time. Now, let’s look at Rolling Average 🔄 — which calculates the average over consecutive periods. Here’s the key difference 👇 While cumulative sum shows a steady upward trend, rolling average highlights real fluctuations — like the drop from April to May 📉 much more clearly. Also, notice the range: 👉 Rolling Average stays within a smaller range (6–10) 👉 Cumulative Sum grows wider (0–50) A smaller range makes trends easier to read and even helps in better prediction 🔍 Different techniques, different insights — that’s the power of data! 💡 #DataAnalytics #DataScience #DataAnalyst #percentage #Python #SQL #Excel #PowerBI #KPI #businesskpi #Tableau #BusinessIntelligence #Analytics #MachineLearning #BigData #Visualization #DataDriven #CareerInData

Most people jump straight to dashboards. We should start with data profiling. 📊 Step 1 in any Data Analytics project: Analyze raw datasets in Excel before cleaning. 🔍 What do we usually find? • Inconsistent values across columns • Missing data in multiple fields • Mixed data types (text + numbers) • Data integrity issues across tables 💡 Key takeaway: We should understand the data first before cleaning or building dashboards. ➡️ Next step (already covered in previous post): Data Cleaning using Python 🤔 Quick question: Do you start with data profiling or jump directly into dashboards? #DataAnalytics #Excel #Python #PowerBI #LearningInPublic

Data without context is just noise. I’ve seen how easy it is to get impressed by numbers at first glance 📊 A 20% increase sounds great — until you realize it came from a very small base. A drop in revenue feels alarming 📉 until you factor in seasonality. That’s the thing about data… it doesn’t really speak for itself. It needs someone to step back, ask the right questions and connect the dots. That’s where the real value of an analyst comes in —not just reporting what happened, but explaining why it happened and what to do next. 🚀 #DataAnalytics #BusinessAnalytics #DataDriven #Analytics #BusinessIntelligence #DataStorytelling #Insights #DecisionMaking #SQL #PowerBI #Python

One underrated skill for every Data Analyst: asking the right questions 🤔 We often focus on tools like SQL, Power BI, or Python—but the real impact comes from understanding what problem we’re actually trying to solve. A good question can turn a basic report into a powerful business insight. 👉 Instead of asking: “What are the sales numbers?” Try asking: “Why did sales drop last month, and what can we do to improve it?” That shift in thinking is what separates a report creator from a true analyst. What’s one question you always ask before starting your analysis? 👇 #DataAnalytics #CriticalThinking #BusinessIntelligence #AnalyticsMindset

"When it comes to analytics, start small but think big. 📈 I often see analysts jump straight into modeling or complex algorithms—but the real magic happens in the exploration and preparation of data. Understanding trends, identifying anomalies, and cleaning data properly can unlock insights that impact business decisions significantly. In my upcoming post, I’ll share a step-by-step approach to exploratory data analysis (EDA) and building dashboards that really work. Do you usually start with EDA or jump into modeling? Would love to hear your approach!" #DataAnalytics #BusinessIntelligence #PowerBI #Tableau #SQL #Python #Insights

#𝗦𝗸𝗶𝗹𝗹𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆 📚 Tools don’t make you a Data Analyst. Thinking does. Real analytics isn’t about clicking buttons—it’s about structured curiosity: 🔍 What is the problem? 📊 What does the data reveal? 🚀 What action can we take? That’s the mindset that turns dashboards into decisions, and queries into impact. Because in the end— it’s not about dashboards or queries… it’s about decisions and impact. That’s what real analytics looks like. Are you focusing on tools… or thinking? Comment👇 #DataFamAP #DataThinking #CareerGrowth #PowerBI #Excel #SQL #Python #LearningInPublic

Everyone sees the dashboard. But no one sees the discipline behind it. 📊 As someone transitioning into Data Analytics, I’m realizing this journey is not just about tools like SQL, Excel, or Power BI. It’s also about: • Self-doubt • Confusion • Repeated practice • Failed attempts • Starting again Behind every good project, there are hours of learning, mistakes, and consistency that people don’t usually see. The journey is not always perfect, but it is real. And that’s what makes the growth meaningful. ✨ Still learning. Still improving. One step at a time. 🚀 #DataAnalytics #LearningJourney #DataAnalyst #CareerGrowth #Consistency #Excel #SQL #Python #PowerBI

What makes a data analyst valuable is not what you think. Day 78 of 180 | 10Alytics Business Analysis Consistency Challenge It’s easy to assume that value comes from knowing tools. SQL. Power BI. Python. But tools are only part of the story. What really makes a data analyst valuable is the ability to: • Understand business problems • Ask the right questions • Simplify complex information • Communicate insights clearly Because at the end of the day: Organizations don’t need dashboards. They need better decisions. Still learning to focus on impact, not just output. Consistency over intensity. 💬 What do you think makes an analyst valuable? #Day78of180 #10Alytics #DataAnalytics #BusinessAnalysis #LearningInPublic #CareerGrowth #PowerBI #IkeaOnyinyechiBusinessAnalysisJourneyWith10Alytics

📊 Same Data. Different Insight. Small design choices can completely change how people understand your data. Most dashboards fail not because the data is wrong — but because the story is missing. Showing raw numbers ≠ delivering insights. Here’s the difference 👇 🔹 Basic Visuals (Low Insight) • Plain bar charts • Raw tables with no context • Simple line charts without benchmarks Result? People spend more time trying to understand the chart than making decisions. 🔹 Enhanced Visuals (High Insight) • Average lines + highlighted values • Annotated trends with peaks & dips • KPI summary cards with key metrics Result? Insights become visible instantly. 💡 Great data visualization should: ✔ Reduce cognitive load ✔ Highlight patterns quickly ✔ Improve decision-making ✔ Communicate insights, not just numbers As data analysts, our job is not just to build charts. Our job is to help people make better decisions. Because the goal is never the dashboard. The goal is clarity. What’s one dashboard mistake you see most often? 👇 #DataScience #Python #SQL #Excel #DataAnalytics #MachineLearning #Pandas #CareerGrowth #PowerBI #LinkedInLearning