This course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering the basic components of any Bayesian model - the prior distribution and the likelihood, and how to find a posterior distribution, credible intervals, and predictive distributions. Along the way, you'll become more comfortable with probability in general and gain a new perspective on how to analyze data!
We start from scratch - no experience in Bayesian statistics is required. Students should have a strong grasp of basic algebra and arithmetic. R and RStudio, or Python, is required if you would like to run the optional coding sections
The course includes:
5.5 hours of video lectures
Interactive demonstrations using R and Stan (Python code is included too!)
Quizzes to check your understanding
Review assignments with solutions to practice what you have learned
You will learn:
The basic rules of probability
Bayes' rule, including common examples with medical testing and flipping coins
The terminology of different components of a Bayesian model: the prior distribution, posterior, likelihood, and predictive distribution
Conjugate priors
Credible intervals and Bayes estimators
Modeling binary data with the Bernoulli and Binomial Distribution, and the Beta distribution prior
Modeling count data with the Poisson Distribution, and the Gamma distribution prior
Modeling continuous data with the Normal Distribution, and the Normal distribution prior
An introduction to simple linear regression
This course is ideal for many types of students:
Anyone who wants to learn the foundations of Bayesian statistics and understand concepts like priors, posteriors and credible intervals
Data science and data analytics professionals who would like to refresh and expand their statistics knowledge
Academics in the social, biological, and physical sciences
This course is ideal for anyone, from beginners to seasoned professionals. It doesn't matter if you're just starting your journey in data science, looking to upgrade your existing skills, or simply have an interest in Bayesian statistics. My goal is to make Bayesian statistics accessible and understandable for all.