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'''CS&SS504, Applied Regression''' is an applied, but still technical course on regression. It may vary based on who is teaching the class. This will be the default option for [[CDSC]] students. | '''CS&SS504, Applied Regression''' is an applied, but still technical course on regression. It may vary based on who is teaching the class. This will be the default option for [[CDSC]] students. | ||
Β You might also consider CS&SS 560, hierarchical modeling, but you could also just read Andrew Gelman's book. You might also take CS&SS 564 (Baysian Statistics) but if you take ECON 580 you could probably learn the material in this class on your own. | |||
'''CSSS564, Bayesian Statistics for the Social Sciences''' CS&SS 564 is very good. This may vary by the instructor/text, but in 2023 it was taught using R/Jags/Stan with a project and no tests; the content is a nice blend of mathematical and applied perspectives. There are a lot of online resources that accompany the text so you can learn/re-learn the material a few different ways. It's a fair amount of work because you are building familiarity with doing a lot of simulation and digging your hands into how models are working, but the pre-requisites are low; it's not brain-breaking, just some solid grinding and that takes time. Probably easier than 560 because you will review basics of probability, binomial model, etc. from the intro-sequence but in a Bayesian way. That said, the R is a bit more intense in 564 than it is in 560. Taught using mostly base R -- not tidyverse! | '''CSSS564, Bayesian Statistics for the Social Sciences''' CS&SS 564 is very good. This may vary by the instructor/text, but in 2023 it was taught using R/Jags/Stan with a project and no tests; the content is a nice blend of mathematical and applied perspectives. There are a lot of online resources that accompany the text so you can learn/re-learn the material a few different ways. It's a fair amount of work because you are building familiarity with doing a lot of simulation and digging your hands into how models are working, but the pre-requisites are low; it's not brain-breaking, just some solid grinding and that takes time. Probably easier than 560 because you will review basics of probability, binomial model, etc. from the intro-sequence but in a Bayesian way. That said, the R is a bit more intense in 564 than it is in 560. Taught using mostly base R -- not tidyverse! |