Sajid Mahmood’s Post

Day 18/30 of my Data Analyst + AI journey 🚀 After learning how to filter and sort data, today I explored one of the most powerful concepts in Pandas — GroupBy and Aggregation. This is where data actually starts giving insights. 👉 What I learned today: 🔹 GroupBy Used to group data based on a column df.groupby("City") 🔹 Aggregation Functions Used to perform calculations on grouped data 👉 SUM df.groupby("City")["Salary"].sum() 👉 AVG df.groupby("City")["Salary"].mean() 👉 COUNT df.groupby("City")["Name"].count() 👉 What I understood: • GroupBy helps summarize data • Aggregations help extract insights • This is how reports and dashboards are built 👉 Real-world use: • Total sales by city • Average salary by department • Number of customers per region How I used AI today: 👉 Practiced groupby operations 👉 Understood aggregation functions 👉 Improved analysis thinking 💡 Key Learning: Raw data tells nothing… But summarized data tells everything. Today I felt like a real Data Analyst 📊🔥 If you’re also learning Pandas or Data Analytics, comment “IN” — let’s grow together 🤝 #Python #Pandas #DataAnalytics #AI #Learning #Consistency #Day18 #BuildInPublic

Seeds ➡️ Trees. 🌳 Ideas ➡️ Empires. 🏰 Transformers ➡️ AI Revolution. 🤖✨ The "AI touch" is no longer optional—it’s becoming the standard for every tool, product, and process in the software world. We are seeing a massive shift in how work gets done. 🏗️ The Corporate Divide: ✅ Some demand AI adoption for speed. 🔒 Others build custom AI for privacy and security. The Personal Challenge: How do we become better versions of ourselves in this new era? Is simply using these tools enough, or do we need to master the logic behind them? 🧐 Drop a comment with your perspective! 💭👇 #DataAnalytics #DataScience #DataAnalyst #percentage #Python #SQL #Excel #PowerBI #KPI #businesskpi #Tableau #BusinessIntelligence #Analytics #MachineLearning #BigData #Visualization #DataDriven #CareerInData

Future of Data Analysts - do you feel the same ?? 🤔 I’ve been noticing.. the way I approach data analysis has changed a lot. Earlier, it was about: Business requirement → write SQL/Python → get stuck, use google/stack overflow → repeat Now it’s more like… Formulate the problem → ask AI to write the Python/SQL → spend time validate the logic if actually it makes sense → follow up questions with AI → repeat ‘Validating AI generated SQL/Python query’ is surprisingly tricky and time consuming. Honestly, generating complex queries and charts feels much easier now. But ensuring ‘data integrity’ feels… harder. Sometimes AI hands over a flawless-looking analysis or a complex set of JOINs and smart looking logics. I think the core skill in analytics is shifting. Earlier : “Can you analyse efficiently?” Now : “Can you validate efficiently?” AI is powerful, no doubt. But someone still has to take the responsibility for the insights driving the business. And that hasn’t changed. Because of this validation step, it actually made me care a lot more about the fundamentals- basic logics, statistics, data modeling, and deep business context (insights writing). Does anyone else in the data space feel this shift? What’s your workflow looking like these days? #ai #dataanalytics

🚀 Exploratory Data Analysis with R Cheat Sheet Most people learn R syntax. Very few learn how to do proper Exploratory Data Analysis with R. This visual cheat sheet focuses on how data scientists actually explore, clean, analyze and visualize data using R and the Tidyverse. 👉 What this cheat sheet covers - Setting up Tidyverse for real projects - Understanding tidy data structure - Inspecting data using glimpse, summary and skim - Handling missing values correctly - Core dplyr verbs like filter, select, mutate, arrange and summarise - Group by and aggregation patterns - Data visualization with ggplot2 - Univariate and bivariate analysis - Formatting plots and using facets - Advanced cleaning like renaming, recoding and reshaping - Correlation checks and outlier detection - Working with dates using lubridate - String operations with stringr If you are learning Data Science with R, preparing for analytics roles, or working on real datasets, this will help you think like a data scientist. 👤 Follow Mounica Tamalampudi for more content on Data Science, AI, ML, and Agentic AI 💾 Save this post for future reference 🔁 Repost if this helps your network #EDA #ExploratoryDataAnalysis #RStats #Tidyverse #DataScience #DataAnalysis #Analytics #MachineLearning #AI #DataAnalyst #DataEngineer #TechLearning

Day 17/30 of my Data Analyst + AI journey 🚀 After cleaning the data, today I moved to the next step — analyzing it using filtering and sorting in Pandas. This is where raw data starts turning into meaningful insights. 👉 What I learned today: 🔹 Filtering Data Used to select specific rows based on conditions df[df["Age"] > 22] 👉 Multiple conditions: df[(df["Age"] > 22) & (df["City"] == "Delhi")] 🔹 Sorting Data Used to arrange data in ascending or descending order df.sort_values(by="Age", ascending=True) 👉 Descending: df.sort_values(by="Age", ascending=False) 👉 What I understood: • Filtering helps find specific data • Sorting helps organize data • Together, they help in better decision making 👉 Real-world use: • Find top customers • Filter high-value sales • Sort data for reports How I used AI today: 👉 Practiced filtering conditions 👉 Understood sorting logic 👉 Improved my data analysis thinking 💡 Key Learning: Data analysis is not about looking at all data… It’s about focusing on the right data. Today I started thinking like a Data Analyst 📊🔥 If you’re also learning Pandas or Data Analytics, comment “IN” — let’s grow together 🤝 #Python #Pandas #DataAnalytics #AI #Learning #Consistency #Day17 #BuildInPublic

Future of Data Analysts - do you feel the same ?? 🤔 I’ve been noticing.. the way I approach data analysis has changed a lot. Earlier, it was about: Business requirement → write SQL/Python → get stuck, use google/stack overflow → repeat Now it’s more like… Formulate the problem → ask AI to write the Python/SQL → spend time validate the logic if actually it makes sense → follow up questions with AI → repeat ‘Validating AI generated SQL/Python query’ is surprisingly tricky and time consuming. Honestly, generating complex queries and charts feels much easier now. But ensuring ‘data integrity’ feels… harder. Sometimes AI hands over a flawless-looking analysis or a complex set of JOINs and smart looking logics. But deep down, my "data spidey-sense" tingles. And then I have to spend time untangling it to make sure it isn't lying with data. I think the core skill in analytics is shifting. Earlier : “Can you analyse efficiently?” Now : “Can you validate efficiently?” AI is powerful, no doubt. But someone still has to take the responsibility for the insights driving the business. And that hasn’t changed. Because of this validation step, it actually made me care a lot more about the fundamentals- basic logics, statistics, data modeling, and deep business context (insights writing). Does anyone else in the data space feel this shift? What’s your workflow looking like these days? #ai #dataanalytics

𝗧𝗵𝗲 "𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗧𝗮𝘅" 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. We’ve traded "writing" time for "validation" time and the tax is currently too high. While AI has solved the "blank page" problem, it has introduced a massive shift in the workflow. We are now spending more time auditing AI-generated code than we used to spend writing it manually. The reason? 𝗔𝗜 𝗶𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. To reduce validation time to minutes instead of hours, I see two foundational pillars and one high-speed approach for smaller teams: 𝟭. 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗟𝗮𝘆𝗲𝗿 (𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸) Before the AI touches anything, you must know the "shape" of your data. 𝗪𝗵𝘆: If you don't know your null rates or distributions, you can’t verify the AI’s assumptions. Spotting an anomaly before running the query saves 80% of the debugging time. 𝟮. 𝗧𝗵𝗲 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗟𝗮𝘆𝗲𝗿 (𝗧𝗵𝗲 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻) AI needs a map of what things mean in your specific business. 𝗪𝗵𝘆: It connects raw columns to governed definitions like "Active Revenue. "When AI maps to a governed definition instead of guessing from a column name" validation becomes a quick final sign-off rather than a forensic audit. 𝙏𝙝𝙚 "𝙎𝙥𝙚𝙚𝙙" 𝙋𝙡𝙖𝙮: 𝙏𝙝𝙚 𝙈𝙖𝙨𝙩𝙚𝙧 𝙏𝙖𝙗𝙡𝙚 For smaller or more agile teams, providing the AI with a pre-joined Master Table is a game-changer. It eliminates the "relationship" errors and fan-outs that AI often trips over, shifting the focus entirely to the logic of the analysis. 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗶𝘀𝗻'𝘁 𝘁𝗼 𝘀𝘁𝗼𝗽 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗻𝗴. 𝗜𝘁'𝘀 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗮 𝟮-𝗺𝗶𝗻𝘂𝘁𝗲 𝗰𝗵𝗲𝗰𝗸 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗮 𝟮-𝗵𝗼𝘂𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻. Does this framework align with how you're structuring your data flow? Or is "Validation" still the biggest hurdle in your AI implementation? H/T Rishabh Mishra for the original framing - my take above. #DataAnalytics #AI #DataQuality #SemanticLayer #Workflows #ModernDataStack

Everyone can run the query now. The scarce skill is knowing when to not trust the answer. Spent 20 minutes last week staring at a "perfect" output that was completely wrong. AI had no idea. Looked immaculate.

📊 **Data Analysis: More Than Just Numbers** Data analysis today is about driving decisions—not just reporting results. With AI and predictive analytics, we’re shifting from “what happened” to “what’s next.” The real value lies in: ✔️ Clear data storytelling ✔️ Ethical and responsible use of data ✔️ Continuous learning in a fast-changing space 🚀 The future belongs to those who can turn data into action. #DataAnalytics #AI #Analytics #BusinessIntelligence #Powerbi #SQL #Excel #Python

🚀 Why Statistics & Probability Are the Backbone of Data Analysis & AI If you're stepping into Data Analysis or Artificial Intelligence, there’s one truth you can’t ignore: 👉 No Statistics = No Real Understanding Let’s break it down simply 👇 📊 1. What is Statistics? Statistics helps us summarize, understand, and interpret data. Descriptive Statistics → Describe the data (Mean, Median, Standard Deviation) Inferential Statistics → Make predictions & decisions (Hypothesis Testing, Confidence Intervals) 💡 In real life: You don’t just look at data… you extract meaning from it. 🎲 2. What is Probability? Probability measures uncertainty. 👉 In AI, everything is about likelihood: Will this customer churn? Is this email spam? Is this tumor malignant? 💥 Models don’t give answers… they give probabilities. 🤖 3. Role in Data Analysis & AI ✔️ Understand patterns ✔️ Handle uncertainty ✔️ Build predictive models ✔️ Evaluate model performance Without statistics & probability: ❌ Your model is just guessing 🐍 4. NumPy — The Foundation of Data NumPy is all about numbers & arrays. Why it matters: Fast computations ⚡ Handles large datasets Mathematical operations made easy 💡 Think of it as: 👉 The engine behind data processing 📊 5. Pandas — The Data Manipulation Tool Pandas helps you clean, transform, and analyze data. Key structures: Series → One column DataFrame → Full table What you can do: ✔️ Clean messy data ✔️ Handle missing values ✔️ Filter & group data ✔️ Prepare data for models 💡 Real talk: 👉 80% of your work as a data analyst = Pandas 🧠 6. The Real Workflow Collect Data Clean it (Pandas) Analyze it (Statistics) Model it (AI/ML) Evaluate using Probability 🔥 Final Insight Garbage In = Garbage Out ❌ Clean Data + Strong Statistics = Powerful AI ✅ 💬 If you’re learning Data Science: 👉 Don’t skip the fundamentals Because tools change… but concepts stay. This video was prepared for my students at Instant Software Solutions. 📌 Good Luck 📊 #DataScience #MachineLearning #ArtificialIntelligence #Statistics #Probability #Python #NumPy #Pandas #DataAnalysis #AI #Learning #TechCareers #Analytics #BigData #DataEngineer