PHYS 321 — Data Science and Observational Astrophysics

Data analysis methods as applied in experimental physics, with an emphasis on applications in observational astrophysics. An introduction to Bayesian inference, model selection, Markov Chain Monte Carlo, common probability distributions, jackknives and null tests, as they are used in the analysis of observational data from across the electromagnetic spectrum.

• Credits: 3
• Faculty: Faculty of Science
• Department: Physics
• Prerequisite(s): COMP 208; PHYS 257