📊 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

Create a new Space with "Static HTML" SDK
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:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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