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| title: DataScience Learning Hub | |
| emoji: 📊 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: static | |
| pinned: false | |
| # 📊 DataScience Learning Hub v2.0 | |
| Welcome to a comprehensive collection of educational web projects for learning data science! This repository contains multiple interactive courses covering statistics, machine learning, deep learning, visualization, mathematics, and feature engineering. | |
| ## ✨ What's New in v2.0 | |
| - 🔍 **Global Search** - Press `Ctrl/Cmd + K` to search across all modules | |
| - 📊 **Progress Tracking** - Track your learning journey with persistent progress | |
| - 🌙 **Light/Dark Mode** - Toggle between themes or follow system preference | |
| - 📱 **PWA Support** - Install as an app for offline access | |
| - ♿ **Accessibility** - ARIA labels, keyboard navigation, skip links | |
| - 🎨 **Unified Design System** - Consistent look and feel across all modules | |
| - ⚡ **Performance** - Optimized loading with service worker caching | |
| --- | |
| ## 🎯 Live Demos | |
| Visit our courses directly in your browser: | |
| | Course | Link | Topics | | |
| |--------|------|--------| | |
| | 🧠 Deep Learning | [Launch](https://aashishgarg13.github.io/DataScience-v2/DeepLearning/) | 12 | | |
| | 🤖 Machine Learning | [Launch](https://aashishgarg13.github.io/DataScience-v2/ml_complete-all-topics/) | 42 | | |
| | 📊 Statistics | [Launch](https://aashishgarg13.github.io/DataScience-v2/complete-statistics/) | 41 | | |
| | 📐 Mathematics | [Launch](https://aashishgarg13.github.io/DataScience-v2/math-ds-complete/) | 15 | | |
| | ⚙️ Feature Engineering | [Launch](https://aashishgarg13.github.io/DataScience-v2/feature-engineering/) | 12 | | |
| | 📈 Visualization | [Launch](https://aashishgarg13.github.io/DataScience-v2/Visualization/) | 8 | | |
| | 🐍 Python | [Launch](https://aashishgarg13.github.io/DataScience-v2/Python/) | 10 | | |
| | 💬 Prompt Engineering | [Launch](https://aashishgarg13.github.io/DataScience-v2/prompt-engineering-guide/) | 12 | | |
| --- | |
| ## 🧠 Course Overview | |
| ### Deep Learning Masterclass 🔥 | |
| **The flagship course**. Zero to Hero journey through neural networks. | |
| **Topics include:** | |
| - Neural Network Foundations (Architecture, Activation Functions) | |
| - Backpropagation & Gradient Descent (with full math derivations) | |
| - Convolutional Neural Networks (CNNs) | |
| - Recurrent Neural Networks (RNNs, LSTMs, GRUs) | |
| - Transformers & Attention Mechanisms | |
| - Generative Adversarial Networks (GANs) | |
| - Diffusion Models | |
| - Regularization & Optimization Techniques | |
| **Methodology:** "Paper & Pain" - rigorous mathematical derivations with step-by-step worked examples. | |
| --- | |
| ### Machine Learning Complete Guide | |
| The foundational course covering all classical ML algorithms. | |
| **Topics include:** | |
| - Supervised Learning (Linear/Logistic Regression, Trees, SVMs, Ensembles) | |
| - Unsupervised Learning (K-Means, DBSCAN, Hierarchical Clustering, PCA) | |
| - Reinforcement Learning Fundamentals | |
| - NLP & GenAI (Word Embeddings, Transformers, RAG) | |
| - Model Evaluation & Selection | |
| --- | |
| ### Statistics Course | |
| 41 interactive topics covering probability and statistical inference. | |
| **Topics include:** | |
| - Descriptive Statistics (Mean, Median, Mode, Variance) | |
| - Probability Distributions (Normal, Binomial, Poisson) | |
| - Hypothesis Testing (T-test, Chi-squared, ANOVA) | |
| - Confidence Intervals | |
| - Bayesian Statistics | |
| --- | |
| ### Mathematics for Data Science | |
| The engine room of AI and ML. | |
| **Topics include:** | |
| - Linear Algebra (Vectors, Matrices, Eigenvalues) | |
| - Calculus (Derivatives, Gradients, Chain Rule) | |
| - Probability Theory | |
| - Optimization | |
| --- | |
| ### Feature Engineering Guide | |
| The art of data preparation. | |
| **Topics include:** | |
| - Data Cleaning & Missing Values | |
| - Feature Scaling & Normalization | |
| - Encoding Categorical Variables | |
| - Feature Selection & Dimensionality Reduction | |
| --- | |
| ### Data Visualization | |
| Communicating insights effectively. | |
| **Topics include:** | |
| - Matplotlib Fundamentals | |
| - Seaborn Statistical Visualizations | |
| - Plotly Interactive Charts | |
| - Best Practices for Data Storytelling | |
| --- | |
| ### Prompt Engineering Guide | |
| Mastering LLMs and AI assistants. | |
| **Topics include:** | |
| - Prompt Fundamentals | |
| - Zero-shot & Few-shot Learning | |
| - Chain of Thought Prompting | |
| - System Prompts & Personas | |
| - Advanced Techniques (ReAct, ToT) | |
| --- | |
| ## 📁 Project Structure | |
| ``` | |
| DataScience-v2/ | |
| ├── index.html # Enhanced landing page | |
| ├── manifest.json # PWA manifest | |
| ├── service-worker.js # Offline caching | |
| ├── offline.html # Offline fallback | |
| ├── shared/ # Shared resources | |
| │ ├── css/ | |
| │ │ ├── design-system.css # Core styles & tokens | |
| │ │ └── components.css # Reusable components | |
| │ ├── js/ | |
| │ │ ├── search.js # Global search (Cmd+K) | |
| │ │ ├── progress.js # Progress tracking | |
| │ │ └── theme.js # Theme toggle | |
| │ └── icons/ | |
| │ └── favicon.svg | |
| ├── DeepLearning/ # Deep Learning course | |
| ├── ml_complete-all-topics/ # Machine Learning course | |
| ├── complete-statistics/ # Statistics course | |
| ├── math-ds-complete/ # Mathematics course | |
| ├── feature-engineering/ # Feature Engineering | |
| ├── Visualization/ # Data Visualization | |
| ├── prompt-engineering-guide/ # Prompt Engineering | |
| ├── ML/ # Experiments & datasets | |
| └── README.md # This file | |
| ``` | |
| --- | |
| ## 🚀 Quick Start | |
| ### Local Development | |
| ```bash | |
| # Clone the repository | |
| git clone https://github.com/aashishgarg13/DataScience.git | |
| cd DataScience-v2 | |
| # Serve locally (any of these options) | |
| python -m http.server 8000 | |
| # or | |
| npx serve . | |
| # or | |
| php -S localhost:8000 | |
| # Open in browser | |
| open http://localhost:8000 | |
| ``` | |
| ### Deploy to GitHub Pages | |
| ```bash | |
| # Push to main branch | |
| git add . | |
| git commit -m "Deploy" | |
| git push origin main | |
| # Enable GitHub Pages in repository settings | |
| # Settings > Pages > Source: main branch | |
| ``` | |
| ### Deploy to Hugging Face Spaces | |
| 1. Create a new Space with "Static HTML" SDK | |
| 2. Push this repository: | |
| ```bash | |
| git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/DataScience | |
| git push hf main | |
| ``` | |
| --- | |
| ## 🛠️ Features | |
| ### Global Search (Ctrl/Cmd + K) | |
| Search across all modules instantly. Uses Fuse.js for fuzzy matching. | |
| ### Progress Tracking | |
| - Persistent localStorage-based tracking | |
| - Per-module progress bars | |
| - "Continue where you left off" feature | |
| - Export/Import progress data | |
| ### Theme Toggle | |
| - Light and Dark modes | |
| - Respects system preference | |
| - Smooth transitions | |
| - Persisted choice | |
| ### PWA Support | |
| - Install as standalone app | |
| - Offline access to cached pages | |
| - Background sync | |
| - App shortcuts | |
| ### Accessibility | |
| - Skip to main content links | |
| - ARIA labels on interactive elements | |
| - Keyboard navigation | |
| - Focus indicators | |
| - Reduced motion support | |
| --- | |
| ## 🤝 Contributing | |
| Contributions are welcome! Please: | |
| 1. Fork the repository | |
| 2. Create a feature branch (`git checkout -b feature/amazing-feature`) | |
| 3. Commit your changes (`git commit -m 'Add amazing feature'`) | |
| 4. Push to the branch (`git push origin feature/amazing-feature`) | |
| 5. Open a Pull Request | |
| --- | |
| ## 📄 License | |
| This project is open source and available under the [MIT License](LICENSE). | |
| --- | |
| ## 🙏 Acknowledgments | |
| - The Data Science and ML community | |
| - Contributors and students worldwide | |
| - Open source projects that made this possible | |
| --- | |
| **Made with ❤️ by [Aashish Garg](https://github.com/aashishgarg13)** | |