README.md

metadata

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	12
🤖 Machine Learning	Launch	42
📊 Statistics	Launch	41
📐 Mathematics	Launch	15
⚙️ Feature Engineering	Launch	12
📈 Visualization	Launch	8
🐍 Python	Launch	10
💬 Prompt Engineering	Launch	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

# 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

# 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:

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.

---

🙏 Acknowledgments

• The Data Science and ML community
• Contributors and students worldwide
• Open source projects that made this possible

---

Made with ❤️ by Aashish Garg