[Note: This course is still being developed. Any feedback would be greatly appreciated and can be sent to tessler@mit.edu
]
Learning statistics is like learning pottery. With pottery, you can learn how to make different shapes (e.g. a bowl, a vase, a spoon) without understanding general principles. The other way is to learn the basic strokes of forming pottery (e.g. how to mold a curved surface, a flat surface, long pointy things). In this course, we are going to learn the basic strokes of statistics, and compose those strokes to make shapes you’ve seen before (e.g. a ttest), some shapes you’ve probably never seen before, and develop ideas how you would make new shapes if you needed to. We won’t learn what tests apply to what data types but instead foster the ability to reason through data analysis. We will do this through the lens of Bayesian statistics, though the basic ideas will aid your understanding of classical (frequentist) statistics as well.
Chapters

Why analyze data
Course overview 
Probabilistic programming
A brief introduction. 
Learning about a hypothesis
Models formalize hypotheses 
Comparing hypotheses
Hypothesis testing is model comparison 
Causal models
Reasoning with structured knowledge 
Elaborating models
A pinch of sophistication and elegance 
Inference Algorithms
The various approximate inference algorithms WebPPL provides and the classes of programs for which they are each best suited. 
Analyzing Bayesian cognitive models
The fully Bayesian treatment
Appendix

Coming up with priors
Systematically interrogating one’s knowledge 
Bayesian inference in a probabilistic program
Understanding observe, condition, and factor via rejection sampling
Citation
M. H. Tessler (in prep). Bayesian data analysis: An introduction using probabilistic programs. Retrieved from https://mhtess.github.io/bdappl/
Useful resources
WebPPL support and packages
 webppl.org: An online editor for WebPPL
 WebPPL documentation
 WebPPL dev Google Group: Public forum for discussing issues with WebPPL
 WebPPLviz: A summary of the vizualization options in WebPPL
 RWebPPL: If you would rather use WebPPL within R
 WebPPL packages (e.g. csv, json, fs).
 A WebPPL package with useful BDA helper functions
Basic WebPPL tutorials
Bayesian Data Analysis (using WebPPL)
 Probabilities and Bayes Rule in WebPPL by Michael Franke
 Comparing methods for computing Bayes Factors by Michael Franke
 BDA of Bayesian language models
 Old BDA course syllabus by MH Tessler
Other WebPPL applications
 Probabilistic Models of Cognition: An introduction to computational cognitive science and the probabilistic programming language WebPPL
 Probabilistic Language Understanding: An introduction to probabilistic models of language (in particular, the Rational Speech Act theory)
 Modeling Agents with Probabilistic Programs: An introduction to formal models of rational agents using WebPPL
 Forest: A Repository for probabilistic models
Great textbooks on Bayesian Data Analysis
 Doing Bayesian Data Analysis (Kruschke)
 Bayesian Data Analysis (Gelman)
 Bayesian Cognitive Modeling (Lee & Wagenmakers)