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

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝟗𝟎 — 𝐃𝐚𝐲 13: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬t 🔍 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝟏. 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Descriptive analysis involves summarizing and describing the main features of a dataset, such as its central tendency, dispersion, and distribution. Descriptive statistics, charts, and graphs are commonly used to present key characteristics of the data. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐞𝐝?" 𝟐. 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Diagnostic analysis focuses on identifying the causes of observed patterns or outcomes in the data. It involves analyzing relationships between variables, identifying correlations, and conducting root cause analysis to understand why certain events occur. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐲 𝐝𝐢𝐝 𝐢𝐭 𝐡𝐚𝐩𝐩𝐞𝐧?" 𝟑. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Predictive analysis involves using historical data to make predictions about future events or outcomes. It includes techniques such as regression analysis, time series forecasting, and machine learning algorithms to build predictive models and estimate the likelihood of future events. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐡𝐚𝐩𝐩𝐞𝐧?" 𝟒. 𝐏𝐫𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Prescriptive analysis involves recommending actions or decisions based on the insights gained from data analysis. It aims to optimize outcomes by identifying the best course of action given the available data and constraints. Optimization techniques, decision trees, and simulation models are commonly used. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐝𝐨?" Follow Sudeesh Koppisetti for such informative content on data analytics #DataAnalytics #DataAnalysis #DataCleaning #DataQuality #DataPreprocessing #AnalyticsEngineering #BusinessAnalytics #SQL #Python #PowerBI #Tableau #DataEngineering #ETL #DataPipeline

I got a dataset with 40% missing values. Here's exactly what I did. 🧵 Most beginners panic when they see missing data. I used to be one of them. Then I built a system for it. Here's my step-by-step process for handling messy, incomplete data: 𝗦𝘁𝗲𝗽 𝟭 — Understand WHY the data is missing Not all missing data is equal. ❓ Missing completely at random? → Safe to drop ❓ Missing for a reason? → That reason is valuable data ❓ Missing because of a system error? → Fix upstream Always ask WHY before doing anything. 𝗦𝘁𝗲𝗽 𝟮 — Assess the damage I calculate the % of missing values per column. → Under 5% missing → usually safe to drop those rows → 5–30% missing → impute with mean, median or mode → Over 50% missing → seriously consider dropping the column 𝗦𝘁𝗲𝗽 𝟯 — Choose the right fix For numerical columns → median imputation (more robust than mean) For categorical columns → mode or a new 'Unknown' category For time series → forward fill or interpolation 𝗦𝘁𝗲𝗽 𝟮 — Validate after cleaning Always check your data AFTER cleaning. → Did distributions change drastically? → Did you accidentally introduce bias? → Does the cleaned data still make business sense? The result? I went from 40% missing values to a clean, analysis-ready dataset in under 2 hours. Honest truth: Data cleaning isn't glamorous. But it's the difference between insights you can trust and insights that mislead. Save this for your next messy dataset. 🔖 What's the messiest dataset you've ever worked with? 👇 #DataCleaning #DataAnalytics #DataAnalyst #Python #SQL #DataScience #DataQuality #DataCommunity

📊 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

Global Superstore Data Analytics Project I recently developed a comprehensive data analytics project using the Global Superstore dataset, designed to transform raw business data into actionable insights with improved clarity and decision-making support. The project follows a systematic workflow: • Data Inspection: Understanding dataset structure and data types using .info() • Statistical Analysis: Generating descriptive statistics to uncover initial patterns • Data Cleaning: Handling missing values, duplicates, and inconsistencies • Exploratory Data Analysis: Identifying trends in sales, profit, and customer behavior • Outlier Detection: Detecting and managing anomalies in the dataset • Correlation Analysis: Evaluating relationships between variables for deeper insights • Dashboard Development: Building an interactive dashboard using Python and Streamlit. 🌐 Live Application: https://lnkd.in/dQk9QfXS 💻 Source Code: https://lnkd.in/dD7wSvw5 This project highlights the importance of data analysis and visualization in understanding business performance and reflects my ability to design clean, scalable, and interactive data solutions. I look forward to applying these techniques to more advanced analytics and machine learning projects. #DataAnalytics #Python #Streamlit #Dashboard #DataScience #BusinessAnalytics #LearningJourney

📊 Understanding Data Analytics As I continue building my skills, I started with the foundation — understanding what Data Analytics really means. Data Analytics is the process of turning raw data into meaningful insights that help in better decision-making. From business growth to user experience, data plays a key role everywhere. There are four main types of data analytics: 🔹 Descriptive – What happened? 🔹 Diagnostic – Why did it happen? 🔹 Predictive – What might happen? 🔹 Prescriptive – What should be done? Learning this made me realize how powerful data can be when used correctly. Looking forward to exploring more concepts and applying them in real-world scenarios. #DataAnalytics #LearningJourney #AspiringDataAnalyst #DataScience #SQL #Python #CareerGrowth

🔍 𝗗𝗮𝘁𝗮 𝗦𝗸𝗲𝗽𝘁𝗶𝗰𝗶𝘀𝗺: 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗔𝗹𝘄𝗮𝘆𝘀 𝗗𝗼𝘂𝗯𝗹𝗲 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗦𝗼𝘂𝗿𝗰𝗲 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗣𝗶𝘃𝗼𝘁 𝗧𝗮𝗯𝗹𝗲 Every analyst loves a good pivot table. But here is a hard truth: If your data source is wrong, your pivot table will confidently give you the wrong answer. That is where data skepticism comes in. Before you start analyzing, ask questions. Always. 1️⃣ 𝗪𝗵𝗲𝗿𝗲 𝗗𝗶𝗱 𝗧𝗵𝗶𝘀 𝗗𝗮𝘁𝗮 𝗖𝗼𝗺𝗲 𝗙𝗿𝗼𝗺 𝗪𝗮𝘀 𝗶𝘁 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆 𝗲𝗻𝘁𝗲𝗿𝗲𝗱, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱, 𝗼𝗿 𝗽𝘂𝗹𝗹𝗲𝗱 𝗳𝗿𝗼𝗺 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀? Understanding the source helps you spot potential errors early. 2️⃣ 𝗜𝘀 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 Missing rows can completely distort your analysis. What you do not see can hurt your conclusions. 3️⃣ 𝗜𝘀 𝗜𝘁 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 Different formats, naming styles, or time periods can quietly break your results. Consistency is the foundation of accuracy. 4️⃣ 𝗗𝗼𝗲𝘀 𝗜𝘁 𝗠𝗮𝗸𝗲 𝗦𝗲𝗻𝘀𝗲 If revenue suddenly doubles overnight, pause. That might be growth, or it might be a data issue. 5️⃣ 𝗖𝗮𝗻 𝗜𝘁 𝗕𝗲 𝗧𝗿𝘂𝘀𝘁𝗲𝗱 Cross check with another source if possible. Good analysts verify before they visualize. 💡 𝗣𝗿𝗼 𝘁𝗶𝗽: 𝗗𝗼 𝗻𝗼𝘁 𝗳𝗮𝗹𝗹 𝗶𝗻 𝗹𝗼𝘃𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘁𝗼𝗼 𝗾𝘂𝗶𝗰𝗸𝗹𝘆. 𝗙𝗮𝗹𝗹 𝗶𝗻 𝗹𝗼𝘃𝗲 𝘄𝗶𝘁𝗵 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗳𝗶𝗿𝘀𝘁. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗮 𝗯𝗲𝗮𝘂𝘁𝗶𝗳𝘂𝗹 𝗰𝗵𝗮𝗿𝘁 𝗯𝘂𝗶𝗹𝘁 𝗼𝗻 𝗯𝗮𝗱 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮 𝘄𝗲𝗹𝗹 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. 𝗖𝗮𝗹𝗹 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻 Have you ever discovered a major issue after starting your analysis? What did you learn from it? Share your story. Let us grow together 👇 #DataAnalytics #DataQuality #DataCleaning #BusinessIntelligence #PowerBI #Excel #SQL #Python #DataDriven #AnalyticsCommunity #AbdulrahamanTaye #DataStorytelling

📊 Outliers in Data – The Hidden Factor Behind Wrong Insights In data analytics, a single extreme value can completely change your results. That’s where outliers come in 👇 📌 What are Outliers? Outliers are data points that differ significantly from other values in a dataset. 👉 Example: ₹30K, ₹40K, ₹50K, ₹5L 📍 ₹5L is an outlier (far from the rest) 📌 Why Outliers Matter (Effects) ⚠️ Skew the mean (average) ⚠️ Distort data distribution ⚠️ Mislead dashboards & reports ⚠️ Reduce model accuracy 💡 Even one outlier can impact your entire analysis! 📌 How to Identify Outliers 🔍 1. IQR Method IQR = Q3 − Q1 Outliers: 👉 Below Q1 − 1.5×IQR 👉 Above Q3 + 1.5×IQR 🔍 2. Z-Score Method Measures distance from mean |Z| > 3 → Outlier 🔍 3. Visualization Box Plot 📦 (most effective) Scatter Plot Histogram 📌 Box Plot – Your Best Friend A box plot quickly shows: ✔️ Median (center line) ✔️ Q1 & Q3 (box) ✔️ Whiskers (range) ✔️ Outliers (points outside) 👉 Perfect for spotting anomalies in seconds! 🚀 Pro Tip: Don’t remove outliers blindly—first understand whether they are errors or valuable insights. ✅ Final Insight: Clean data + smart outlier handling = accurate insights & better decisions Ranjith Kalivarapu, Krishna Mantravadi, Upendra Gulipilli , Rakesh Viswanath, Frontlines EduTech (FLM) #DataAnalytics #DataCleaning #Outliers #Statistics #MachineLearning #PowerBI #Python #SQL #FLM

🚀 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐏𝐚𝐧𝐝𝐚𝐬 = 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬. If you're stepping into Data Analytics, this cheat sheet is your best friend 💡 Here are some must-know Pandas functions that every analyst should have at their fingertips: 🔹 Data Loading `read_csv()` | `read_excel()` 🔹 Quick Exploration `head()` | `info()` | `describe()` | `shape` 🔹 Data Cleaning `isnull()` | `dropna()` | `fillna()` | `drop_duplicates()` 🔹 Data Transformation `rename()` | `astype()` | `apply()` 🔹 Data Analysis `groupby()` | `pivot_table()` | `value_counts()` 🔹 Data Selection `loc[]` | `iloc[]` | `query()` 🔹 Data Merging `merge()` | `concat()` 💥 Pro Tip: Don’t just memorize practice on real datasets. That’s where real learning happens. 📊 Pandas is not just a library… it’s the backbone of modern data analysis. If you're serious about becoming a Data Analyst or Data Engineer, start mastering these today. 👉 Which Pandas function do you use the most? 👇 Drop it in the comments! 🔁 Repost if this helps 👍 Like for more such content 📌 Follow me for daily Data Analytics tips #Pandas #Python #DataAnalytics #DataScience #Learning #CareerGrowth #DataEngineer #ExcelToPython

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