Introduction to Data Analysis, with Python and Jupyter

Catalog description

How do we draw conclusions about fundamental physics from experimental data? This course covers basic data analysis techniques and practical statistics used in experimental and computational physics research. Weekly Python-based labs will allow students to explore topics including data visualization, error propagation, evaluating hypotheses, and fitting analytical models. These labs incorporate real and simulated data from existing experiments such as a gamma-ray telescope and a detector that searches for dark matter. Students will learn to use Python libraries running in Jupyter Notebooks to analyze data and will, for example, study the rate at which the universe is expanding using existing data from multiple telescopes. No prior coding experience is required. This course was offered as PHYSICS 89L prior to Academic Year 2025-2026. Students requiring 89L credit may use this course to satisfy that requirement.