Bayesian data analysis is increasingly becoming the tool of choice for many data-analysis problems.
This free course on Bayesian data analysis will teach you basic ideas about random variables and probability distributions, Bayes' rule, and its application in simple data analysis problems. You will learn to use the R package brms (which is a front-end for the probabilistic programming language Stan). The focus will be on regression modeling, culminating in a brief introduction to hierarchical models (otherwise known as mixed or multilevel models).
This course is appropriate for anyone familiar with the programming language R and for anyone who has done some frequentist data analysis (e.g., linear modeling and/or linear mixed modeling) in the past.
Here are some of the advantages of Bayesian methods over the standard frequentist approach used in data analysis:
We assume the following in this course:
This course is not appropriate for participants who don't know R programming and who have no experience at all with data analysis.
After completing this course, you will be in a good position to learn how to use more advanced Bayesian methods, such as hierarchical models, finite mixture models, multinomial processing tree models, measurement error models, etc.
This four-week course consists of
We expect a weekly time commitment of 5-10 hours to complete the course, depending on your prior knowledge.
The course follows the structure of an online textbook, which will be published by CRC Press soon. You can view the textbook here.
Find out more in the certificate guidelines.
Shravan Vasishth is professor of linguistics at the University of Potsdam, Germany. His background is in Statistics, Computer Science, Linguistics, and Japanese. He is a chartered statistician with the Royal Statistical Society, UK. For more details about him and his research, see vasishth.github.io.
I am a cognitive scientist with a PhD in cognitive science from the University of Potsdam, Germany. I am interested in data science and computational cognitive modeling.