Data Analysis Steps for Data Analysts

I once spent hours optimizing a dashboard… only to realize the real problem wasn’t the dashboard. It was the data model behind it. A reporting dashboard was taking too long to load. Pages lagged, filters were slow, and users struggled to interact with it. At first, it looked like a visualization issue. But after digging deeper, I found the bottleneck was happening earlier in the pipeline. The dashboard was built on top of an inefficient data model complex joins, duplicated logic, unnecessary columns, and heavy transformations in the reporting layer. So instead of tweaking visuals, I rebuilt it from the data layer up: • Optimized SQL joins and removed redundant aggregations • Refactored the model into a cleaner star schema • Reduced unnecessary columns and duplicate transformations • Simplified calculation logic and moved processing upstream • Improved refresh flow to reduce processing overhead Then I redesigned the visuals on top of the optimized model. The result? Faster load times. Smoother interactions. More reliable reporting. Most importantly, stakeholders actually started using the dashboard with confidence. It’s to rebuild the structure underneath them. Key takeaway: 👉 Dashboard performance is often a data modeling problem, not a visualization problem. Have you ever fixed a “dashboard issue” that turned out to be a data problem? #DataAnalytics #SQL #DataModeling #BusinessIntelligence #PowerBI #DataEngineering #ETL #DataPipelines #AnalyticsEngineering #BigData #DataPerformance #DashboardDesign #Python #DataScience #TechCareers

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

Not all data issues are obvious. Some hide in plain sight. I recently worked on a dataset where everything looked correct at first glance. No errors. No missing values. Dashboards were loading fine. But something felt off. The numbers didn’t fully align across reports. After digging deeper, I found the issue wasn’t in the dashboard… it was in how the data was being processed upstream. Here’s what was happening: • A join condition was unintentionally duplicating records • Aggregations were being applied after duplication • Result → inflated metrics in reporting To fix it, I focused on the pipeline logic: • Validated row counts at each stage of transformation • Reworked join conditions to prevent duplication • Applied aggregations at the correct level (before joins) • Added SQL validation checks to catch similar issues early The result? Accurate metrics. Consistent reporting. Restored trust in the data. What’s the most subtle data issue you’ve encountered in your analytics work? #DataAnalytics #SQL #DataEngineering #DataQuality #ETL #DataPipelines #BusinessIntelligence #AnalyticsEngineering #Python #BigData #DataValidation #TechCareers #DataModeling #DataScience #DataGovernance

SQL is still the most underrated skill in Data Science. Not because of SELECT statements — but because of: Window functions Query optimization Data modeling If you can’t extract clean, structured data efficiently, no model will save you.

📊 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

🚀 𝗗𝗮𝘆 𝟳 : 𝗧𝗼𝗱𝗮𝘆 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝗱 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 — 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 & 𝗚𝗿𝗼𝘂𝗽𝗕𝘆 𝗶𝗻 𝗣𝗮𝗻𝗱𝗮𝘀 📊 🔹 What is Aggregation? Aggregation means combining multiple data points to get summarized results. It helps in understanding patterns like total sales, average values, counts, etc.👉 Common aggregation functions: sum() → Total mean() → Average count() → Number of values max() / min() → Highest / Lowest 🔹 What is GroupBy? GroupBy is used to split data into groups based on some criteria and then apply aggregation functions on those groups. In simple words: Split → Apply → Combine 📌 Basic Syntax: df.groupby('column_name') 📌 Aggregation with GroupBy: df.groupby('column_name')['target_column'].sum() 📌 Multiple Aggregations: df.groupby('column_name')['target_column'].agg(['sum', 'mean', 'count']) 📌 Group by Multiple Columns: df.groupby(['col1', 'col2'])['target_column'].sum() ✨ Why is GroupBy important? Helps in data summarization Used in reports & dashboards Essential for business insights 📈 Learning GroupBy is a big step toward becoming a strong Data Analyst! #Day7 #DataAnalytics #Python #Pandas #LearningJourney #DataScience #GroupBy #Aggregation

Discover why mastering data joins is non-negotiable for data scientists. Learn how to combine multiple tables or data frames in Pandas and SQL to unlock deeper insights and create comprehensive reports using real-world examples. Watch the full video: https://lnkd.in/dE-bF_Re #datascience #pandas #sqljoins #pythonprogramming #dataanalysis #innerjoin #dataframes

"𝗦𝗤𝗟 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗾𝘂𝗲𝗿𝗶𝗲𝘀." I used to think that too. 𝗪𝗲𝗲𝗸 𝟵 of my 𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 & 𝙈𝙇 programme with ParoCyber brought me back to SQL but this time, I understood it at the level that actually matters. I wasn't just writing commands. I was deciding how data should exist; building tables with intention, choosing data types deliberately, and modifying structure in real time: adding fields, dropping what no longer served a purpose, renaming columns for clarity. The shift wasn't in the syntax. It was in the mindset. Before analysis. Before dashboards. Before models... there is structure. And getting that right makes everything else easier. Structure isn't setup; it's the decision that determines how accurate and efficient everything after it can be. Get it wrong and every query, result, and model built on top inherits the mess. Get it right and everything downstream just works. #DataScience #SQL #MachineLearning #LearningInPublic #DataEngineering #WomenInTech

Day 8/30 Sometimes breaking data into smaller parts makes it much easier to understand and work with. 🔹 Problem: Separate numbers into even and odd lists 🔹 What I focused on today: Using loops and conditions together to organize data 🔹 My Thinking Process: Take a list of numbers from the user Check each number If divisible by 2 → even list Otherwise → odd list 👉 Simple condition, but very useful in data handling 🔹 Inputs I used: List of numbers 🔹 Code: numbers = list(map(int, input("Enter numbers separated by space: ").split())) even_numbers = [] odd_numbers = [] for num in numbers: if num % 2 == 0: even_numbers.append(num) else: odd_numbers.append(num) print("Even numbers:", even_numbers) print("Odd numbers:", odd_numbers) 🔹 Example: Input: 1 2 3 4 5 6 Even → [2, 4, 6] Odd → [1, 3, 5] 🔹 Key Takeaway: Breaking data into categories helps in better analysis and organization, which is a core concept in data analytics #Day8#Python #30DaysOfCode #LearningInPublic #DataAnalytics #ProblemSolving

📊 𝗗𝗮𝘆 𝟯𝟮 – 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘𝘀 & 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 Today, I explored Recursive CTEs along with Hierarchical Queries, and understood how SQL can work with structured data such as trees, sequences, and parent-child relationships. 🔹 𝗪𝗵𝗮𝘁 𝗜 𝗙𝗼𝗰𝘂𝘀𝗲𝗱 𝗢𝗻 • Understanding recursion in SQL • Learning the anchor part and recursive part • Seeing how a query repeats until a condition is met 🔹 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 • START WITH • CONNECT BY PRIOR • LEVEL • SYS_CONNECT_BY_PATH • CONNECT_BY_ROOT 🔹 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✔️ Generated numbers from 1 to N using recursion ✔️ Identified missing values in a sequence 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Recursive queries are powerful for working with hierarchical data and repeating patterns, which are very common in real-world data scenarios. #𝗦𝗤𝗟 #𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲𝗖𝗧𝗘 #𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹𝗤𝘂𝗲𝗿𝗶𝗲𝘀 #𝗗𝗮𝘁𝗮𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 #𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝗝𝗼𝘂𝗿𝗻𝗲𝘆 #𝗦𝗤𝗟𝗝𝗼𝘂𝗿𝗻𝗲𝘆🚀