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📚 Python for Introductory Statistics
Chapters ▾
0. Why Learn Python?1. Introduction to Python2. Python Lists3. Python Syntax4. Python Loops5. Frequency Distributions and Graphs6. Measures of Center and Variation7. Measures of Position8. Discrete Probability Distributions9. Continuous Probability Distributions10. Confidence Intervals11. Hypothesis Testing12. Linear Regression and Correlation13. Analysis of Categorical Data14. Advanced Statistics TopicsAppendix 1: Python Installing StepsAppendix 2: Random Integer GeneratorAppendix 3: Counting, Permutations and CombinationsCode ReferencesResources

When AI tools are available

In many future workplaces, students will likely have access to AI-powered coding tools. Even then, the real value of learning Python remains strong. Students still need to:

  1. understand the statistical question well enough to ask for the right analysis
  2. judge whether AI-generated code is correct
  3. notice when an answer is incomplete, misleading, or wrong
  4. ask better follow-up questions and refine an analysis thoughtfully

AI can help with syntax and code generation, but it cannot replace statistical understanding.

Adapted from Python for Introductory Statistics, by Simon Aman (Truman College, City Colleges of Chicago), licensed under CC BY 4.0. Changes were made: reformatted as an accessible XYZ web edition with live in-browser code cells. License: CC-BY-4.0.