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.
The course consists of content that focuses on the intuition and application of Bayes. It contains short videos, relevant references, and downloadable code examples, that explain, show, and guide you through the modern statistics ecosystem.
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.
Ravin learned the power of Bayes Theorem at SpaceX when improving the supply chains of the world's most advanced rockets. He's now an advocate of applied Bayesian methods and has since authored a textbook about Bayes Theorem and writes about appllied data science on his blog.
Thomas is the CEO and founder of PyMC Labs – The Bayesian Consultancy, and an author of PyMC. He discovered his passion for Bayesian modeling during his PhD in cognitive neuroscience. Frustrated with how theory-focused Bayesian modeling was taught, he found a more intuitive, code-first way of teaching Bayesian modeling, show-cased in his blog, talks, and now, this course.
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.
Tomás discovered his passion for statistics about ten years ago when he was just starting his career. Since then, he has worked diligently to improve statistics education and develop more appealing tools to appeal to a variety of users. He's one of the leading developers of Bambi, a library that makes Bayesian modelers' life easier. Constantly seeks to learn new stuff and loves sharing cool things with others on his blog.
Bill is a Data Scientist at PyMC Labs with experience solving problems in several different industries using Bayesian methods. Bill in particular is an expert in Gaussian Processes, having implemented the initial GP functionality in PyMC in just one summer. His catchphrase is some variation of "if that doesn't work for you, there's this one type of GP that might do the trick here...
What other are saying
Frank Harrell
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.
Development Schedule
We're currently building the course with a focus on our upcoming limited beta.
Have some thoughts? Contact us directly
All rights are reserved for Intuitive Bayes 2021