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PHYS 321. Data Science and Observational Astrophysics.

Credits: 3
Offered by: Physics (Faculty of Science)
Terms offered: Winter 2027
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Description

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.
  • Corequisite(s): PHYS 241; PHYS 258
  • Prerequisite(s): COMP 208; PHYS 257
  • Students with sufficient knowledge of computer programming do not need to have taken COMP 208

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