CAL-ICOR Modules Showcase

This textbook serves as an interactive resource for courses across various disciplines. By leveraging Jupyter Notebooks and MyST Markdown, this collection provides hands-on, code-driven explanations of key concepts in data science, economics, environmental science, and more. Each chapter is designed to be accessible, engaging, and reproducible, ensuring students can experiment with real-world datasets and computational models while learning.

Contributions¶

To contribute to this collection, please submit a pull request to this repository. See instructions for contribution and the pull request template.

How to use this textbook¶

The live textbook is available at https://cal-icor.github.io/textbook/. You can read pages statically or run code interactively as follows.

Launch and run code¶

Run in Binder or your own hub¶

Other actions¶

• Modules Showcase
• JupyterCon Demo
  • San Diego Get It Done (311)
• City Planning
  • Introduction to GeoPandas
  • Working With and Visualizing Large Datasets
• Economics
  • Game Theory and Behavioral Economics
  • Econometrics
  • Survival Analysis
• Environmental Science, Policy, and Management
  • Biodiversity
  • Introduction to Prediction
  • Oh Rats!: A Data Science Dive Into Invasive Species and Island Sustainability
• Engineering
  • Numerical Integration and Differentiation
• Ethnic Studies
  • COVID-19 in Prisons
• Geography
  • Intro to Human Geography!
  • Development Geographies!
  • Population
  • Globalization & Agriculture
• Meteorology
  • Intro to Weather and Climate!
  • Climate Change and Causes
• Anthropology
  • An Introduction to Data Analysis with COVID-19 Data
  • Exploring HIV and AIDS Data
  • Working with Diabetes Data
• Data Science
  • Notebook 1: Introduction to Jupyter Notebooks and Python
• Psychology
  • Correlation, regression, and prediction
• Cognitive Science
  • Human Brains vs. Big Data
• Political Science
  • Candidate Ideology and Turning Out the Base in US Elections
  • Hypothesis Testing with 2016 Presidential Election
  • Bivariate Regression
• Legal Studies
  • Text Preprocessing