Python Data Science Stack & Free Hosting Options

The Modern Developer's Power Couple: Data Science & Free Hosting! 🚀 If you are looking to build and deploy data-driven applications in 2026, these two infographics cover the essentials: The Python Data Science Stack: From the foundations of NumPy and Pandas to advanced AI with PyTorch and TensorFlow. The Deployment Layer: 20 incredible platforms like Vercel, Netlify, and Railway that let you host your projects for free. Whether you're performing complex EDA with Seaborn or deploying a fast API on Render, the barrier to entry has never been lower. Which hosting platform is your favorite for side projects? 👇 #Python #DataScience #WebDevelopment #MachineLearning #CloudComputing #OpenSource

The Modern Developer's Power Couple: Data Science & Free Hosting! 🚀 If you are looking to build and deploy data-driven applications in 2026, these two infographics cover the essentials: The Python Data Science Stack: From the foundations of NumPy and Pandas to advanced AI with PyTorch and TensorFlow. The Deployment Layer: 20 incredible platforms like Vercel, Netlify, and Railway that let you host your projects for free. Whether you're performing complex EDA with Seaborn or deploying a fast API on Render, the barrier to entry has never been lower. Which hosting platform is your favorite for side projects? 👇 #Python #DataScience #WebDevelopment #MachineLearning #CloudComputing #OpenSource

Most data science projects don't fail at modeling they fail at understanding the data. Day 1 of 100: I built a real-world dataset from scratch and ran a full EDA pipeline using Pandas & NumPy. Checked for null values, analyzed distributions, and flagged outliers that would have silently destroyed any model trained on top of them. The insight that hit different: skewed distributions look completely normal in raw tables , you only catch them when you actually plot the data. Day 2 of 100. Tomorrow: feature engineering starts. 📂 Full notebook → https://lnkd.in/denkS294 #DataScience #Python #100DaysOfCode #MachineLearning #EDA #Pandas #AIEngineering

🚀 Quick NumPy Revision + Assignment Completed While learning Data Science, I created these quick notes for NumPy to revise important concepts like: ✔ Creating NumPy arrays ✔ Understanding array dimensions (ndim) ✔ Reshaping arrays ✔ Random number generation ✔ Functions like zeros, eye, and linspace ✔ Array operations & indexing ✔ Mathematical operations on array ✔ Searching array These small notes help me revise NumPy faster while practicing Python for Data Science and Machine Learning. 📂 Assignment available on GitHub: https://lnkd.in/dX66epMw #Python #NumPy #DataScience #MachineLearning #LearningInPublic #100DaysOfCode

Registering a Virtual Environment as a Jupyter Kernel: If you’ve ever: ❌ Activated a virtual environment ❌ Opened Jupyter Notebook ❌ Still seen ModuleNotFoundError This is why 👇 👉 Jupyter doesn’t detect environments — it detects kernels. In this reel, I show: ✅ Why your venv doesn’t appear ✅ Why installing Anaconda is NOT required ✅ How to properly register your venv as a Jupyter kernel ✅ A lightweight, production-ready workflow This fix is: ✔ VM-friendly ✔ Low-RAM ✔ Industry standard If you work with Python, Data Science, or Machine Learning, this will save you hours. 📖 Complete setup guide available on GitHub 🖇Follow the link for the full walkthrough shown in this video: https://lnkd.in/dxu-nBYD 🔁 Re-share to help other developers 💾 Save this for later 💬 Comment “kernel” if this helped you #Python #JupyterNotebook #VirtualEnvironment #DataScience #MachineLearning #DeveloperTips #PythonTips #VSCode

Can we predict a stroke before it happens? 🧠 I recently finished a project using the Healthcare Stroke Dataset to build a prediction tool from scratch. Instead of using high-level libraries, I built the Logistic Regression model using only NumPy to truly understand the math behind the predictions. Key Highlights: Data Cleaning: Handled imbalances and missing values using Pandas. Feature Engineering: Created custom features like "Age-Glucose" interaction to improve model sensitivity. Deployment: Built a live dashboard with Streamlit so users can interact with the model in real-time. make sure to remove the space when you copy the link Check out the app here: [https://lnkd.in/e3knnnXe] #DataAnalytics #MachineLearning #Python #HealthTech #DataScience

Tools Don’t Matter (But They Do) People ask: “Which tool did you use?” The real question is: 👉 Why that tool fits the architecture This project helped me understand: • When object storage makes sense • Why Delta beats plain parquet for pipelines • Why incremental loads are non-negotiable Tools change. Principles don’t. #dataengineering #python #data #storage #minio #incrementalpipeline #spark #tools

🚀 Solved the “Two Sum” Problem | Data Structures & Algorithms Practice Today I solved the classic Two Sum problem—a fundamental question in data structures & algorithms. 🔹 Problem: 1 Given an array of integers and a target value, return the indices of two numbers such that they add up to the target. ⏱️ Complexity: Time Complexity: O(n) Space Complexity: O(n) 🔗 GitHub Repository (more DSA problems inside): https://lnkd.in/gdrbnQDF #DSA #ProblemSolving #Python #CodingJourney #SoftwareEngineering #LeetCode

Day 4 of 47: Stop writing loops to find your data! 🛑 Using a for loop on 1M rows? That’s the slow way. Today I explored NumPy’s high-speed Search & Sort: 🔍 np.where() – Finds values instantly using vectorization and returns their indices. 📊 np.sort() – Efficiently sorts large datasets (QuickSort by default). 💎 argsort() – Returns sorting indices without disturbing original data (perfect for sorting one column while keeping others aligned). 💡 In Data Science, we care more about where the value is than the value itself. Next: Analyzing Batting Performance! 🏏 #DataScience #NumPy #Python #LearningJourney