Mohammedali Saiyed’s Post

I’m not starting Data Analytics… I’m refining it 🚀 Over the past months, I’ve learned the fundamentals: SQL, Python, Excel, Power BI But here’s the truth… Learning alone doesn’t make you job-ready. I spent too much time consuming content: • Tutorials • Notes • Courses But not enough time building. That changes now. From here on: • Turning concepts into real-world projects • Solving actual data problems • Sharing insights, not just progress No more passive learning. Only execution. Let’s see where consistency takes this 📈 #DataAnalytics #OpenToWork #BuildingInPublic #SQL #Python

Day 12/75 — A mistake I made while analyzing data 👇 While working on a dataset, I calculated the average price and got: 👉 €97 I thought: “Okay, that makes sense.” But I didn’t question it. Later, I checked the median… 👉 €65 That’s a huge difference. 💡 What went wrong? I trusted a single metric without understanding the data. After digging deeper, I realized: • A few expensive listings were skewing the average • The data wasn’t evenly distributed 🚨 Lesson: 👉 Never rely on just one number 👉 Always check the distribution 👉 Context matters more than calculations 👨💻 Since then, I always: • Compare mean vs median • Look for outliers • Validate results before trusting them Small mistake… But a big learning. Have you ever trusted data too quickly? 👇 #DataScience #DataAnalysis #LearningInPublic #Python #OpenToWork

One of the biggest mistakes beginners make in data analytics is using the same datasets everyone else is using. If your project looks like everyone else’s, it won’t stand out. Instead, try creating your own dataset — messy, imperfect, and closer to real-world data. That’s what I did. It forced me to think deeper, clean smarter, and extract meaningful insights. Real learning happens when the data isn’t already perfect. If you want to grow, stop relying on perfect data. Build your own. Break it. Fix it. Learn from it. Check it out 👇 #DataAnalytics #DataScience #Python #SQL #DataAnalyst #PortfolioProjects #LearnByDoing #DataProjects #EDA #AnalyticsJourney #OpenToWork

📊 Data Cleaning: The Foundation of Every Insightful Analysis Let’s be honest—data cleaning isn’t the most exciting part of a project. But it is the most critical one. In almost every real-world dataset I’ve worked with, I’ve encountered: ❌ Missing values that hide key insights ❌ Duplicate records that distort results ❌ Inconsistent formats that break analysis Before jumping into dashboards or models, I make it a priority to clean and structure the data properly. 💡 Because no matter how advanced your analysis is. it’s only as good as the quality of your data. 🛠️ Tools I rely on: ✔ Python (Pandas) for efficient data wrangling ✔ Excel (Power Query) for quick transformations ✨ A small investment in data cleaning can lead to massive improvements in accuracy, reliability, and decision-making. 👉 Let’s discuss: What’s the most challenging data cleaning issue you’ve faced—and how did you solve it? #DataCleaning #DataPreparation #Python #Excel #DataAnalytics #OpenToWork

A few months ago, I thought learning Data Analytics was all about tools. Python, SQL, Power BI… I believed mastering them was enough. But working on projects slowly changed that thinking. I started realizing that: Data is messy Problems are not clearly defined And the “right answer” is not always obvious That’s when things became interesting. Instead of just learning tools, I started trying to understand: 👉 What problem am I actually solving? 👉 Why does this analysis matter? 👉 How would this help in real decisions? 💡 Biggest shift for me: From learning tools → to thinking like an analyst Still learning. Still improving. 💬 What was the biggest mindset shift in your learning journey? #DataAnalytics #Learning #CareerGrowth #Python #SQL #DataScience #Projects #OpenToWork

Day 22/75 — I thought my analysis was correct… until I checked this 👇 While working on a dataset, I calculated the average and built my conclusions around it. Everything looked fine. 👉 Numbers made sense 👉 Results looked clean So I moved on. Later, I decided to quickly plot the data. And that’s when I saw it. 📉 A few extreme values were completely skewing the results. 💡 My entire conclusion was based on a misleading average. 🚨 What I did wrong: • Trusted one metric • Didn’t visualize early • Assumed the data was “normal” 👨💻 What I do now: • Always visualize first • Check for outliers • Compare mean vs median 💡 Biggest lesson: Data doesn’t lie… 👉 But it can mislead if you don’t explore it properly. That one small check changed everything. Have you ever trusted your analysis too quickly? 👇 #DataScience #DataAnalysis #LearningInPublic #Python #OpenToWork

Completed a comprehensive project on Statistical Testing & Data Analysis. This project goes beyond A/B testing and covers multiple statistical techniques used in real-world business decision-making. * What I worked on: Z-Test → A/B testing for campaign performance T-Test → Comparing averages between groups Chi-Square Test → Relationship between categorical variables ANOVA → Comparing multiple groups * Key Learning: Each statistical test solves a different business problem. The real skill is knowing which test to use and why. * Insight: Data-driven decisions are not based on intuition — they are backed by statistical evidence. * Tools Used: Python | Pandas | NumPy | Scipy | Statsmodels | Matplotlib * Check out my project: -> [https://lnkd.in/dD-WH3bA] I’m currently building strong foundations in Data Analytics & Machine Learning. Would appreciate your feedback #DataAnalytics #Statistics #ABTesting #Python #MachineLearning #DataScience #OpenToWork

Day 13/75 — One small thing that improved my data analysis 👇 I stopped jumping into coding immediately. Before, I would: 👉 Load dataset → start writing code Now, I always do this first: 👀 Just look at the data • What columns exist? • What looks messy? • What doesn’t make sense? 💡 This simple habit changed everything. Because: 👉 Better understanding = better analysis 👉 Less confusion = faster work 👉 Fewer mistakes later 🚨 Biggest lesson: Don’t rush into coding. Take 5 minutes to understand the data first. It sounds simple… but it makes a huge difference. Do you explore data first or jump straight into coding? 👇 #DataScience #Python #LearningInPublic #Analytics #OpenToWork

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