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---
title: Data Science Masterclass
emoji: π
colorFrom: blue
colorTo: purple
sdk: static
pinned: false
---
# π DataScience Learning Hub
Welcome to a comprehensive collection of educational web projects for learning data science! This repository contains multiple interactive courses and resources covering statistics, machine learning, visualization, mathematics, and feature engineering.
## π― Live Demos
Visit our courses directly in your browser:
- [π Interactive Statistics Course](https://aashishgarg13.github.io/DataScience/complete-statistics/)
- [π€ Machine Learning Guide](https://aashishgarg13.github.io/DataScience/ml_complete-all-topics/)
- [π§ Deep Learning Masterclass](https://aashishgarg13.github.io/DataScience/DeepLearning/Deep%20Learning%20Curriculum.html)
- [π Data Visualization](https://aashishgarg13.github.io/DataScience/Visualization/)
- [π’ Mathematics for Data Science](https://aashishgarg13.github.io/DataScience/math-ds-complete/)
- [βοΈ Feature Engineering Guide](https://aashishgarg13.github.io/DataScience/feature-engineering/)
- [π€ Prompt Engineering Guide](https://aashishgarg13.github.io/DataScience/prompt-engineering-guide) - Interactive AI prompting course
## π Prompt Engineering Resources
Essential resources for mastering AI prompt engineering:
- [Learn Prompting](https://learnprompting.org/) - Comprehensive interactive guide
- [Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) - Community-maintained resource
- [Anthropic's Claude Guide](https://www.anthropic.com/index/constitutional-ai-the-basics) - Advanced prompting techniques
- [OpenAI Best Practices](https://platform.openai.com/docs/guides/prompt-engineering) - Official guidelines
## π Contents
### π Complete Statistics Course
- **Location:** `complete-statistics/`
- **Features:**
- 40+ Interactive Topics
- Descriptive Statistics
- Probability & Distributions
- Hypothesis Testing
- Interactive Visualizations & Canvas
- Hands-on Learning Experience
### π€ Machine Learning Guide
- **Location:** `ml_complete-all-topics/`
- **Features:**
- Comprehensive ML Topics
- Interactive Demonstrations
- Visual Learning Aids
- Step-by-Step Explanations
### π§ Deep Learning Masterclass
- **Location:** `DeepLearning/`
- **Features:**
- **"Paper & Pain" Methodology:** Rigorous mathematical derivations
- Neural Network Foundations (MLP, Backprop, Optimizers)
- Convolutional Neural Networks (CNNs) & Computer Vision
- Generative AI (GANs, Diffusion Models)
- Transformers & Large Language Models (LLMs)
- Interactive Canvas Visualizations
### π Data Visualization
- **Location:** `Visualization/`
- **Features:**
- Interactive Data Visualization Examples
- Chart Types and Best Practices
- Dynamic Visualization Techniques
- Data Presentation Guidelines
### π’ Mathematics for Data Science
- **Location:** `math-ds-complete/`
- **Features:**
- Linear Algebra Fundamentals
- Calculus for Machine Learning
- Statistical Mathematics
- Optimization Theory
### βοΈ Feature Engineering Guide
- **Location:** `feature-engineering/`
- **Features:**
- Data Preprocessing Techniques
- Feature Selection Methods
- Feature Transformation
- Dimensionality Reduction
> π‘ All projects are pure static websites - no server needed! Open directly in your browser or use a simple static server.
## π Auto-Update & Integration
The repository supports automatic updates for:
- New AI prompting techniques and best practices
- Interactive visualization improvements
- Additional learning resources and examples
- Community contributions and fixes
## π Quick Start
### Option A: View Online
Visit our GitHub Pages hosted versions:
1. [Statistics Course](https://aashishgarg13.github.io/DataScience/complete-statistics/)
2. [Machine Learning Guide](https://aashishgarg13.github.io/DataScience/ml_complete-all-topics/)
3. [Deep Learning Masterclass](https://aashishgarg13.github.io/DataScience/DeepLearning/Deep%20Learning%20Curriculum.html)
### Option B: Run Locally (Recommended for Development)
#### Simple Browser Opening:
1. Clone this repository
2. Navigate to either project folder
3. Double-click `index.html`
#### Using Local Server (Recommended to avoid CORS issues):
From the repository root, run one of the following in a terminal:
```bash
# Python 3 (simple static server, available on macOS):
python3 -m http.server 8000
# or using Node.js http-server (if installed):
npx http-server -c-1 . 8000
```
Then open http://localhost:8000/complete-statistics/ or http://localhost:8000/ml_complete-all-topics/ in your browser.
## Deploy to GitHub Pages
1. Push your changes to the `main` branch on GitHub (already done for this repo).
2. In your repository settings on GitHub, go to "Pages" and select the `main` branch and root (`/`) as the source, or set the `gh-pages` branch if you prefer.
3. Save β GitHub Pages will publish the site. For multi-site repos you can add a `docs/` folder or create separate branches, or create a small repo per site.
Because these are static sites you can also host them on Netlify, Vercel, or any static host.
## π Project Structure
### Statistics Course
```
complete-statistics/
βββ index.html # Main course interface
βββ style.css # Course styling
βββ app.js # Interactive visualizations
```
### Machine Learning Guide
```
ml_complete-all-topics/
βββ index.html # Main guide interface
βββ style.css # Guide styling
βββ app.js # Interactive components
```
### Deep Learning Masterclass
```
DeepLearning/
βββ Deep Learning Curriculum.html # All-in-one interactive curriculum
```
### Data Visualization
```
Visualization/
βββ index.html # Visualization examples
βββ style.css # Visualization styling
βββ app.js # Interactive charts
```
### Mathematics for Data Science
```
math-ds-complete/
βββ index.html # Mathematics course interface
βββ style.css # Course styling
βββ app.js # Interactive math demonstrations
```
### Feature Engineering Guide
```
feature-engineering/
βββ index.html # Feature engineering guide
βββ style.css # Guide styling
βββ app.js # Interactive examples
```
## Notes about repository cleanup
While repairing the repository I removed macOS Finder metadata files (names beginning with `._`) that had been added inside the `.git` metadata and working tree. Those `._*` files are resource-fork metadata and are not project code. A `.gitignore` entry was added to ignore these moving forward:
```
._*
.DS_Store
>__MACOSX/
```
If you want to inspect any backup I created of the original `.git`, look for directories named `.git.broken_<timestamp>` in the repository root β they contain the backed-up git metadata.
## π€ Contributing
This project welcomes contributions! Here's how you can help:
1. **Content Improvements**
- Add new interactive examples
- Improve existing visualizations
- Update documentation and guides
- Share prompt engineering techniques
2. **Technical Enhancements**
- Optimize JavaScript performance
- Add responsive design features
- Improve accessibility
- Create new interactive components
3. **Documentation**
- Add topic descriptions
- Create usage examples
- Write tutorial content
- Share prompt templates
Please check our [contribution guidelines](CONTRIBUTING.md) for detailed instructions.
## License
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