PHY231 Data Analysis I

This course provides the basics of data analysis for the physical sciences, focussing on statistics. The course covers the following topics:

• Bayesian and frequentist probability
• Statistical and systematic uncertainties
• Probability distribution functions (PDFs)
• Correlation and covariance
• Error propagation
• Hypothesis testing
• Least squares fitting
• Maximum likelihood method
• Confidence and credibility

The course is structured with weekly 45 min lectures along with 2 hr exercises in python in the afternoon. The content is strongly based on the book by R. J. Barlow, titled "Statistics"

Lectures: The lectures will be delivered live at the campus, lived streamed on zoom, with a podcasts recorded for offline. Zoom link: https://cern.zoom.us/j/67877652936 Password: Communicated via email

Exercises: There are two rooms available for exercise classes in python. The places will be decided on a first-come-first-serve basis but is enough space for everyone.

The course will be graded 1-6. With 100% of the grade from exercise sheets that are given every two weeks. The requirements to pass the course will be to get at least 50% of the marks from the exercise sheets. The exercise sheets will be given in python (apart from one in the middle of the course). 

Patrick Owen

Marta Babicz, Giovanni Celotto, Vadym Denysenko

• Lecture notes (OLAT)
• Exercise sheets (OLAT)

• Data analysis (HS2023; P. Owen) - Lectures from last year
• Datenanalyse (HS2018; O. Steinkamp) - Lectures from 2018 (slightly different content).
• Datenanalyse (SS2004; H.Pruys) - Lectures from 2004, which have some things in common.
• Statistical Methods and Analysis Techniques in Experimental Physics, (FS2015; C.Grab) - Further lectures within the joint masters program "Particle Physics" between UZH and ETH