Applied Bayesian Modeling Workshop
— Interactive, Real-World Bayesian Modeling with PyMC Experts
Join our expert-led, 4-week cohort to build a deep, practical understanding of Bayesian modeling with PyMC. Learn through live sessions, hands-on projects, and personalized guidance — starting January 12.
What you'll learn
- How to build and interpret Bayesian models to solve real-world problems.
- How to run and diagnose MCMC workflows for reliable, interpretable results.
- Practical skills in using PyMC to model uncertainty and understand complex systems.
- Debugging, improving, and scaling models that apply to your own work.
About this course
This hands-on, expert-led workshop from the developers of PyMC will take you from foundational Bayesian thinking to advanced modeling techniques. Designed for engineers, analysts, and scientists, this course emphasizes practical use over academic formality, helping you unlock the power of Bayesian methods in your work. At the end of this course, you'll understand how Bayesian models work and how to confidently apply them to your own data and challenges. Topics include:
- Bayesian Thinking & Model Building
- Hands-On MCMC & Inference Techniques
- Advanced Topics: Hierarchical, Causal, & Time Series Models
The workshop is delivered through 2-hour live sessions twice a week over four weeks. All sessions are recorded and available for the duration of the workshop. Students will have access to a private GitHub repository with all code examples and a dedicated Discord server for communication with instructors and fellow learners.
Who should join?
This course is ideal for software engineers, data analysts, and data scientists who want to move beyond black-box models and start building interpretable, flexible Bayesian models. No prior Bayesian experience is required, but in order to get the most value out of the workshop participants will need:
- Basic Python programming experience
- Familiarity with NumPy
- Comfort working in Jupyter Notebooks
Course Outline
| Date | Time (ET) | Instructor | Topic |
|---|---|---|---|
| Thurs, Jan 08 | 11am - 1pm | Optional: Pre-Workshop Install Session | |
| Mon, Jan 12 | 11am - 1pm |  Allen Downey | Intro to Bayesian modeling and PyMC |
| Wed, Jan 14 | 11am - 1pm |  Vianey Leos Barajas | Priors and Likelihood Choices |
| Mon, Jan 19 | 11am - 1pm |  Chris Fonnesbeck | Building Models in PyMC |
| Wed, Jan 21 | 11am - 1pm | Vianey Leos Barajas | Bayesian Regression |
| Mon, Jan 26 | 11am - 1pm | Chris Fonnesbeck | Hierarchical Models |
| Wed, Jan 28 | 11am - 1pm | Vianey Leos Barajas | MCMC |
| Mon, Feb 02 | 11am - 1pm | Chris Fonnesbeck | Causal Inference Models |
| Wed, Feb 04 | 11am - 1pm | Allen Downey | Time Series Models |
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
- Share your certificate with prospective employers and your professional network on LinkedIn.
Instructors
Still Having Doubts?
No prior Bayesian experience is required, but in order to get the most value out of the workshop participants will need:
- Basic Python programming experience
- Familiarity with NumPy
- Comfort working in Jupyter Notebooks
This workshop will not cover MMMs specifically, but will cover the foundational tools and techniques that are needed for Bayesian MMMs.
You’ll get a payment confirmation right away, and within two business days you’ll receive a welcome email with everything you need to get started — including how to access course materials.
There are 16 hours of live instruction: two 2-hour live sessions each week over four weeks, for eight sessions total. You’ll also have notebooks and access to instructors via Discord between sessions.
Yes, sessions will be recorded and available to participants for the duration of the course.
Yes, a shared private repository will have all the code used in the class. Workshop participants have access to all the code used in sessions for 8 weeks following the workshop.
No worries — recordings are available for the duration of the workshop, and you’ll still have access to instructors via Discord for help.
If you cancel before the course begins (at least 7 days prior), you’re eligible for a refund. Additionally, as a natural person, you have a 14-day withdrawal right from registration, provided the workshop hasn’t yet started. (See full Terms and Conditions for details.)
Applied Bayesian Modeling Workshop
- Beginner - Intermediate
- Starts January 12
- 4 weeks
- 8 Live Workshops
- Access to Alumni Discord Q&As
- Instructors: Chris Fonnesbeck, Allen Downey, Vianey Leos Barajas
Need an invoice?
If you need an invoice to submit to your employer, please email info@pymc-labs.com with the following details:
- Purchaser name
- Purchaser billing address (including country)
- VAT or tax ID information (if applicable)
We’ll generate and send your invoice promptly.
Looking for a team offer?
Contact us at info@pymc-labs.com to get a special group rate for multiple registrations from the same company.