Zaid Nayeem’s Post

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

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 27/75 — My simple process for analyzing any dataset 👇 When I first started learning data analysis, I would open a dataset and immediately feel confused. Now I follow a simple process every time. 📌 Step 1: Understand the data 👉 What do the columns mean? 👉 What problem could this solve? 📌 Step 2: Check data quality • Missing values • Wrong formats • Duplicates 📌 Step 3: Explore patterns 👉 Trends 👉 Outliers 👉 Relationships between variables 📌 Step 4: Visualize insights Because charts often reveal things tables don’t. 📌 Step 5: Ask business questions Not just: ❌ “What happened?” But: 👉 “Why did it happen?” 👉 “What can we improve?” 🚨 Biggest lesson: Good analysis is less about coding… And more about: 👉 Thinking clearly This simple structure helped me stop feeling overwhelmed by datasets. How do you usually approach a new dataset? 👇 #DataScience #Python #DataAnalysis #LearningInPublic #OpenToWork

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

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

🚀 From Raw Data to Real Insights — My Latest Python Learning Milestone Data tells a story — if you know how to read it. Recently, I worked hands-on with a dataset to uncover trends and patterns using Python. What started as raw numbers quickly transformed into actionable insights through structured analysis and visualization. 🔍 What I worked on: • Performed exploratory data analysis (EDA) to clean and understand the dataset • Applied groupby() techniques to identify category-wise and region-wise sales patterns • Built visualizations with charts to communicate findings clearly 💡 Key takeaway: Powerful data analysis doesn’t always require complex algorithms. Often, simple and well-executed steps reveal the most valuable insights. 🛠️ Tools leveraged: Python | Pandas | Matplotlib A special thanks to Praveen Kalimuthu and Tech Data Community for breaking down these concepts with practical examples and real-world scenarios that made learning both effective and relatable. #SQL #Oracle #PLSQL #DataAnalytics #MongoDB #ContinuousLearning #DatabaseManagement #CareerGrowth #SQLPlus #SQLLoader #PythonForDataAnalysis #PowerBI #TechDataCommunity #DataDriven #Upskilling #LearningJourney #ProfessionalGrowth #Constraints #Joins #ETL #Consistency #DataToDecision #DatabaseDesign #DatabaseAdministration #DataIntegrity #QueryOptimization #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

Day 10 / 30 — My Python Data Cleaning Workflow: the exact 6 steps I run every time . Let me be honest about something. When I started learning data, I thought the exciting part was the analysis the dashboards, the insights, the "aha" moments. Then I opened my first real dataset.It had null values in random columns. Dates stored as strings. Numbers stored as text. Duplicate rows that looked different. Column names like "First Name " with a trailing space. That was the day I learned the real truth about data work: 80% of the effort happens before you write a single chart. So I built a simple workflow I follow every time: 1. Understand the data df.info(), df.head(), df.describe() →Know the structure before doing anything. 2. Check missing values df.isnull().sum() → Decide what to drop, fill, or keep based on context. 3. Fix data types early Convert dates and numbers properly → Prevents issues later. 4. Handle duplicates carefully Check first, then remove if needed → Not all duplicates are mistakes. 5. Clean column names Lowercase, snake_case, no spaces → Makes everything easier downstream. 6. Validate again Compare before vs after using describe() and shape → Catch anything unexpected. Over time I learned You don’t need fancy tricks , you need consistency. Because clean data isn’t just a step… it’s the foundation. What’s the first thing you check when you open a dataset? Drop it in the comments I read every single one. 👇 #Sarjun #30DaysOfData #Day10of30 #Python #Pandas #DataCleaning #DataAnalytics #DataEngineering #LearningInPublic #DataEnthusiast #Chennai #TechIndia #Opentowork #Linkedinlearning #Trichy

⚡ Quick tip: you can save hours every week with SQL window functions for data analysis. Here's the short version: When working with SQL window functions for data analysis, one habit dramatically improves output quality — consistent structure from day one. 📋 The approach: • Define inputs & outputs before writing a single line • Validate at each stage, not just the end • Keep logic modular so it can be reused or tested independently This applies whether you're writing SQL, building a Python pipeline, or designing a dashboard in SQL window functions for data analysis. Try it on your next task and see the difference. Have you used this before? What's your go-to SQL window functions for data analysis tip? #DataAnalytics #Python #CareerGrowth Continue learning and growing — share your experiences with the community! #MachineLearning #CareerGrowth #Tech #DataScience #AI #DataAnalytics #LinkedIn #SQL #Database #DataEngineering