Archi Prasad’s Post

*Stop Googling the same syntax every 5 minutes. 🛑 *Transitioning between Excel, Python, and SQL is a daily reality for most Data Analysts. But switching your brain from =VLOOKUP to pd.merge() or JOIN can cause some serious mental lag. I found/created this "Rosetta Stone" for data tasks to keep the workflow seamless. Key takeaways from the guide: ✅ Cleaning: How to handle nulls and duplicates across all three platforms. ✅ Aggregations: Pivot Tables (Excel) vs. GroupBy (Pandas) vs. Group By (SQL). ✅ Time-Savers: Quick date extraction and top N row filtering. If you are constantly switching between spreadsheets and code, bookmark this for your next project. 📌 #DataAnalytics #Python #SQL #Excel #DataScience #DataCleaning #CareerGrowth

Deduplication is not just about removing duplicates. It is about defining: - what counts as a duplicate - which row should survive That decision changes everything. The same SQL function can be applied in different ways: - latest record - highest value - clean event signals Same function. Different logic. Different outcomes. Which one do you use most in your work? Advanced analytical techniques across Python, SQL, R and Excel 👉 The Data Analyst Playbook 👉 Follow for more #SQL #DataAnalytics #DataEngineering #Analytics #DataScience

Hey everyone 👋 Most data analysts don’t have a tools problem. They have a decision problem. Using Excel for everything. Or jumping to Python too early. I did the same. Until I started asking one simple question: “What does this data actually need?” Now it’s simple: Small data → Excel Repeated tasks → Power Query Complex data → Python That one shift changed everything. Faster work. Cleaner data. Better insights. Right tool. Right problem. How do you decide which tool to use? #DataAnalytics #DataCleaning #Excel #Python #PowerQuery #AnalyticsMindset

🚀 Excel → Python → SQL: The Ultimate Data Workflow Cheat Sheet 📊 Still switching between tools and getting confused? 🤯 Here’s a simple side-by-side breakdown of how the same data tasks are done in Excel, Python (Pandas), and SQL 👇 📊 One data task → 3 tools: ➡️ Excel ➡️ Python (Pandas) ➡️ SQL 💡 Learn the logic, not just syntax — that’s what actually matters in real jobs & interviews. 🔍 Covers essentials: ✔ Filtering & sorting ✔ Group By, SUM, AVG ✔ Joins & merging ✔ Handling missing values ✔ Removing duplicates ✔ Creating new columns ⚡ Stop learning tools separately. Start connecting them. That’s how real analysts think. 📌 Save this for future reference ➕ Follow Lulu Bind Abbas for daily data tips, cheat sheets & interview prep #data #analytics #excel #sql #python #datascience

A small data insight that changed my perspective While working with large datasets, I once analyzed user behavior where people were actively exploring options… but not taking the final action. At first, it looked like a simple drop-off. But after digging deeper, I noticed a pattern: ->Small differences in key variables (like pricing or clarity of information) were creating a big impact on decisions. That changed how I look at data. Not every problem needs a complex solution , sometimes the biggest insights come from simple patterns hidden in plain sight. Since then, I always ask: “What small factor could be making a big difference?” #DataAnalytics #DataInsights #SQL #Python #ThinkingInData

Day 3 — Industry Immersion Program Today I worked on the complete data lifecycle as part of my Data Analyst journey. ✔ Created and structured data using Excel ✔ Performed analysis using Python (pandas) ✔ Built visualizations using matplotlib ✔ Queried data using SQL Key Learning: Understanding how grouping (groupby) and visualization help uncover meaningful insights from raw data. Goal for this week: Strengthen my data analysis fundamentals and start working on real-world datasets. #IndustryImmersion #DataAnalytics #Python #SQL #LearningInPublic #FutureDataAnalyst

🚀 From Raw Data to Real Insights – My Data Cleaning Journey Yesterday, I worked on a dataset that looked clean at first glance… but as always, the truth was hidden beneath the surface. I asked myself a simple question: 👉 “Where is my data incomplete?” So, I started digging deeper… Using Python, I analyzed missing values across all columns and visualized them with a clean bar chart. And that’s when the real story appeared: 📊 Key Findings: Rating, Size_in_bytes, and Size_in_Mb had the highest missing values (~14–16%) Most other columns were nearly complete A clear direction for data cleaning and preprocessing emerged 💡 This small step made a big difference. Because in Data Analytics, better data = better decisions 🔥 What I learned again: Don’t trust raw data. Explore it. Question it. Visualize it. Every dataset has a story… Your job is to uncover it. 💬 What’s your first step when you get a new dataset? #DataAnalytics #Python #DataCleaning #DataScience #LearningJourney #Visualization #Pandas #Matplotlib

📊 Day 2 of My Data Analytics Journey Today I explored data visualization using Matplotlib. 🔍 What I learned: - How to create bar charts and line charts - Visualizing data makes patterns easier to understand 💻 What I did: - Created a bar chart for average subject marks - Plotted student performance using a line chart 💡 Key Insight: A simple chart can reveal insights faster than raw data! 📌 Slowly moving from data → insights 🚀 #DataAnalytics #Python #Matplotlib #DataVisualization #LearningJourney #Day2

Thrilled to launch my new professional portfolio I believe that data is only as powerful as the story it tells. I decided to build a central hub to showcase my journey in #DataScience, #BusinessIntelligence, and #DataAnalytics. Key features of the site: 📊 My Projects 🛠️ A deep dive into my 3+ years of experience in the telecom and tech sectors. 💻 My technical arsenal: Python, R, SQL, and Power BI. Check it out here: https://lnkd.in/gjRdAHW4 #PortfolioLaunch #DataScientist #BusinessAnalyst #DataAnalytics #MScDataScience #WebDevelopment #Python #MachineLearning

Think of Data Analytics like a burger 🍔 Each layer matters — from business understanding to SQL, data cleaning, visualization, and real-world projects. It’s not just about tools… it’s about connecting the layers to create meaningful insights. Master the stack, not just one ingredient. #DataAnalytics #DataAnalyst #SQL #Python #PowerBI #LearningJourney