Choosing the right chart for data analysis

One thing I’ve learned while working with data.. “Data doesn’t lie… but it can definitely mislead.” The real skill is not just creating charts, but asking the right questions. Good analysis = Good questions + Clean data + Clear thinking Still exploring, still learning 📊 #DataAnalytics #Python #BusinessAnalysis #LearningJourney

🔥 While working with data, I noticed something interesting. The same dataset can lead to different conclusions depending on how it is visualized. 📊 Using Matplotlib and Seaborn in Python helped me see this clearly. Matplotlib gives more control to design charts the way we want. Seaborn helps create clean and structured visuals quickly. #DataAnalytics #Python #Matplotlib #Seaborn #DataVisualization

Starting your ML journey? Begin with the fundamentals 🎯 Day 1 tip: Master these before diving into algorithms: ✅ Python basics (variables, loops, functions) ✅ NumPy & Pandas for data manipulation ✅ Linear algebra & calculus concepts ✅ Statistics & probability Remember: Strong foundations = Better ML models The quality of your features determines your model's ceiling. Garbage in, garbage out! #MachineLearning #LearningJourney #Python #DataScience

📊 From Raw Data to Insights using Python I recently practiced Data Cleaning and Exploratory Data Analysis (EDA) using Python on a cars dataset. Sharing a quick walkthrough of my notebook. In this project I performed: ✔ Dropping irrelevant columns ✔ Handling duplicates and missing values ✔ Detecting and removing outliers using the IQR method ✔ Finding unique values and distributions ✔ Creating visualizations like count plots for better insights Tools used: Python | Pandas | NumPy | Seaborn | Matplotlib This practice helped me understand how important data cleaning is before analysis. Always open to feedback and suggestions as I continue learning. #Python #DataAnalytics #DataCleaning #EDA #Pandas #LearningInPublic

Most people default to Pandas. Works fine… until your data scales. That’s where Polars wins: > Similar syntax for most operations > Faster execution > Lazy evaluation (big performance boost) Don’t ditch Pandas. But ignoring Polars now? That’s a mistake. Learn both. Use what fits. Found Insightful? ♻️ Repost in your network and follow Sahil Alam for more. #DataEngineering #Python #Pandas #Polars #BigData #DataAnalyticsSahil Alam for more.

One practical habit that improved my data analysis workflow Before starting any analysis, I create a quick data profiling summary In Python using pandas it takes less than a minute 🗯️ This instantly shows: • statistical distribution • missing data ratio • columns with low or high cardinality It helps me detect problems in the dataset before building any model or visualization #DataAnalysis #Python #DataScience

Back to basics: The Iris dataset is the 'Hello World' of Machine Learning. I used it to demonstrate how clear-cut decision boundaries can be when features are perfectly separated. What was the first dataset that made you fall in love with Machine Learning? Tech Stack: Python | Scikit-Learn | Pandas | Matplotlib | Plotly | Machine Learning #DataScience #Python #MachineLearning #ArtificialIntelligence #Portfolio

Student Performance Prediction Model using Python! I developed a Multiple Linear Regression model using Scikit-learn to predict marks based on study hours, sleep, and practice sessions. What's inside? Multiple Features: Used data like study hours & sleep to train the model. Performance: Evaluated using Train-Test split and Visualization: Insights plotted using Matplotlib. Score. Building this helped me understand how raw data can be turned into predictive insights. Excited to explore more in the world of Data Science! #MachineLearning #Python #DataScience #ScikitLearn #LinearRegression #DataAnalytics #Coding #Project

🐍 Day 75 — Variance Day 75 of #python365ai 📐 Variance measures how spread out data is. Example: np.var(data) 📌 Why this matters: Variance helps understand how much values differ from the mean. 📘 Practice task: Calculate the variance of a small dataset. #python365ai #Variance #DataScience #Python

𝐌𝐞𝐬𝐬𝐲 𝐜𝐨𝐥𝐮𝐦𝐧 𝐧𝐚𝐦𝐞𝐬. 𝐄𝐱𝐭𝐫𝐚 𝐬𝐩𝐚𝐜𝐞𝐬. 𝐌𝐢𝐱𝐞𝐝 𝐔𝐏𝐏𝐄𝐑𝐂𝐀𝐒𝐄 𝐚𝐧𝐝 𝐥𝐨𝐰𝐞𝐫𝐜𝐚𝐬𝐞. 𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫? These 12 Python string methods can fix all of that — sometimes in just one line of code. While learning Python for data analytics, I realized that small string methods like .𝐬𝐭𝐫𝐢𝐩(), .𝐥𝐨𝐰𝐞𝐫(), .𝐫𝐞𝐩𝐥𝐚𝐜𝐞(), .𝐬𝐩𝐥𝐢𝐭(), 𝐚𝐧𝐝 .𝐣𝐨𝐢𝐧() are extremely useful for cleaning text data before analysis. Strong fundamentals make advanced work easier. #Python #DataAnalytics #DataCleaning #PythonForDataAnalysis #LearningPython #AspiringDataAnalyst #PythonTips #DataScience #CodingJourney