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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
- π€ Machine Learning Guide
- π§ Deep Learning Masterclass
- π Data Visualization
- π’ Mathematics for Data Science
- βοΈ Feature Engineering Guide
- π€ Prompt Engineering Guide - Interactive AI prompting course
π Prompt Engineering Resources
Essential resources for mastering AI prompt engineering:
- Learn Prompting - Comprehensive interactive guide
- Prompt Engineering Guide - Community-maintained resource
- Anthropic's Claude Guide - Advanced prompting techniques
- OpenAI Best Practices - 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:
Option B: Run Locally (Recommended for Development)
Simple Browser Opening:
- Clone this repository
- Navigate to either project folder
- Double-click
index.html
Using Local Server (Recommended to avoid CORS issues):
From the repository root, run one of the following in a terminal:
# 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
- Push your changes to the
mainbranch on GitHub (already done for this repo). - In your repository settings on GitHub, go to "Pages" and select the
mainbranch and root (/) as the source, or set thegh-pagesbranch if you prefer. - 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:
Content Improvements
- Add new interactive examples
- Improve existing visualizations
- Update documentation and guides
- Share prompt engineering techniques
Technical Enhancements
- Optimize JavaScript performance
- Add responsive design features
- Improve accessibility
- Create new interactive components
Documentation
- Add topic descriptions
- Create usage examples
- Write tutorial content
- Share prompt templates
Please check our contribution guidelines for detailed instructions.