The Intuitive Bayes Educational Collection
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 you're in the right place.
What is Intuitive Bayes
Intuitive Bayes is everything you need to learn Bayesian Statistics fast. It's a community, a series of courses, a collection of many other resources to get you from Bayes curious to practioner quickly.
It's made for the rapid 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.
You'll also connect with other professionals and have access to a curated list of references so you can focus the learning intuition and applying your new skills.
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 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 others are saying
Frank Harrell
I took a whirlwind 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.
Vishal
Intuitive Bayes is the course I wish I had when I was starting to learn Bayesian statistics. The subject can be pretty intimidating (especially if you’re like me, coming at it from industry without a heavy stats background or PhD), but the practical, example-first, code-first approach is how I prefer to learn. This course built a solid foundation, and since taking the course I’ve started to use Bayesian methods at work. If you’re on the fence, I hope this data point updates your priors.
Robert
Thomas, Ravin, and Alex have created something special with their IntuitiveBayes course. Having arrived in Data Science by accident and without a rigorous background in mathematics at university, everything I've learned has been self-taught and hard-fought. This can lead to some measure of anxiety in connection to whether or not I'm really getting this stuff. I often would find myself returning to specific resources to ensure my intuition is correct, which can often amplify imposter syndrome. Going through this course has helped me stop second guessing myself and to instead build a uniform (pun intended) intuitive framework from distributions to hierarchical models. You build this by coding things yourself and playing around with the examples, asking what you think are dumb questions, and rewatching the material. This cornerstone now can help me scaffold other things on top and I'm excited about the new things I'll learn to do with pymc. Thanks for the course guys!
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