/psdv-teaching

Primary LanguageJupyter NotebookMIT LicenseMIT

Probability, Statistics & Data Visualization Teaching Resources

Website Course 2025 License Quarto

A comprehensive collection of interactive Jupyter notebooks, visualizations, and educational resources for teaching probability, statistics, and data visualization.

Developed by Prof. Nipun Batra at Indian Institute of Technology, Gandhinagar for the ES 114 course.

Features

  • 24 Interactive Notebooks: Covering probability theory, statistics, and data visualization
  • Beautiful Visualizations: High-quality plots and interactive demonstrations
  • Mathematical Rigor: From basic concepts to advanced topics like PCA and joint distributions
  • Practical Applications: Real-world datasets including MNIST, height-weight data, and more
  • Self-Contained Learning: Each notebook includes learning objectives, theory, and practical implementations

Content Overview

Core Topics Covered

Topic Category Notebooks Key Concepts
Foundations intro.ipynb, set.ipynb, probability.ipynb Probability theory, set operations, random number generation
Random Variables random-variables.ipynb, pmf-discrete.ipynb, pdf-continuous.ipynb Discrete and continuous distributions, PMF, PDF
Expectation & Variance expectation.ipynb, iid.ipynb Expected values, variance, independence
Advanced Distributions 2d-distributions.ipynb, joint-distribution-properties.ipynb Multivariate distributions, covariance, correlation
Statistical Concepts law-large-numbers.ipynb, cdf.ipynb, cdf-discrete.ipynb LLN, CLT, cumulative distribution functions
Machine Learning pca.ipynb, logistic-regression-generative.ipynb, embeddings-angle.ipynb Dimensionality reduction, classification, word embeddings
Data Science Tools intro-numpy.ipynb, introduction-pandas.ipynb, introduction-matplotlib.ipynb NumPy, Pandas, Matplotlib fundamentals
Interactive Learning widgets.ipynb, quiz1.ipynb Interactive visualizations, self-assessment

Learning Path

graph TD
    A[Start Here: intro.ipynb] --> B[Mathematical Foundations: set.ipynb, probability.ipynb]
    B --> C[Random Variables: random-variables.ipynb]
    C --> D[Distributions: pmf-discrete.ipynb, pdf-continuous.ipynb]
    D --> E[Expectation: expectation.ipynb]
    E --> F[Advanced Topics: 2d-distributions.ipynb, pca.ipynb]
    F --> G[Applications: logistic-regression-generative.ipynb]
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Quick Start

Prerequisites

  • Python 3.8+
  • Jupyter Notebook or JupyterLab
  • Required packages: numpy, matplotlib, torch, pandas, sklearn

Installation

# Clone the repository
git clone https://github.com/nipunbatra/psdv-teaching.git
cd psdv-teaching

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter lab notebooks/

Browse Online

Visit https://nipunbatra.github.io/psdv-teaching/ to explore all notebooks in your browser.

Course Information

This repository supports the ES 114: Probability, Statistics and Data Visualization course at IIT Gandhinagar.

Interactive Demos

The repository includes several interactive applications in the /apps directory:

  • Distribution Visualizer: Interactive plotting of probability distributions
  • Central Limit Theorem Demo: Live demonstration of LLN and CLT
  • COVID Testing Simulation: Bayesian probability in action
  • Neural Network Digits: MNIST classification demo

Repository Statistics

  • 24 Jupyter Notebooks with comprehensive educational content
  • 500+ Python code cells with detailed explanations
  • 100+ Mathematical equations with proper LaTeX formatting
  • 50+ High-quality visualizations and interactive plots
  • Comprehensive coverage from basic probability to advanced ML topics

Technical Details

  • Built with: Quarto for beautiful website generation
  • Notebook Format: Jupyter notebooks with rich metadata
  • Visualization: Matplotlib, PyTorch, interactive widgets
  • Mathematical Notation: LaTeX rendering throughout
  • Responsive Design: Works on desktop, tablet, and mobile

Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for details.

How to Contribute

  1. Report Issues: Found a bug or have a suggestion? Open an issue
  2. Improve Content: Submit pull requests for notebook improvements
  3. Add Examples: Contribute new examples or applications
  4. Documentation: Help improve documentation and explanations

Contact

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Teaching Assistants: Excellent TAs who have contributed to course development over the years
  • Students: Feedback and suggestions from multiple course iterations
  • Open Source Community: Built using amazing open-source tools like Jupyter, Quarto, and PyTorch

Related Resources

Made with care for students learning probability, statistics, and data visualization