Intuitive Bayes Introductory Course
Have you found most statistics books overly theoretical? Math-heavy? Or lacking a clear focus on application?
Want to keep your skills sharp to improve your career prospects?
Have you heard about these new fangled Probabilistic Programming Languages and want to know what they're all about?
Then this course is for you.
About the Course
This course is made for the quick learners, busy professionals, and for those positively curious about the modern Bayes revolution.
You'll learn about cutting edge tools which contain the most up to date algorithms and are used by modern practitioners in academia and industry.
Meet your Instructors
Applied Practitioners, Tool Builders, and Community Leaders
We're Bayesian enthusiasts, tool builders, and practitioners that use these methods every day. All three of us are authors of the PyMC Probabilistic Programming Language, a production grade package used at leading organizations around the world. We're also practitioners that use Bayes Theorem in a wide variety of settings from political science, supply chains, venture capital, and many other disciplines.
Thomas is enthusiastic about teaching statistics using code and examples, rather than arduous math. Through his many talks and blog posts, he has shown that there is a different way to teach statistics.
Alex is a Bayesian modeler at the PyMC Labs consultancy. Over 11k people listen to his Learning Bayesian Statistics podcast every month, to hear about the human aspects and cutting edge research and applications in the field. An always-learning statistician, he loves building models and studying elections and human behavior.
What other are saying
I took a whirdwind tour of the course. I'm impressed. The layout is clean and clear, motivation is good, and points of emphasis are well chosen. One area perhaps to add is the Bayesian t-test a la John Kruschke which allows parameters for non-normality and non-equal variance. This highlights that Bayes can allow you to be honest about what you don't know, instead of dichotomously looking at model diagnostics.
We're currently building the course with a focus on our upcoming limited beta.
Have some thoughts? Contact us directly
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