Decision-Focused Marketing Analytics
— 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 February 02.
What you'll learn
- Understand the marketing measurement ecosystem and how MMM, quasi-experiments, CLV models, customer choice models, and adoption modeling, and causal designs inform marketing and product decisions.
- Build and interpret probabilistic models in PyMC-Marketing and CausalPy, including MMMs, causal inference designs, CLV models, customer choice models, and diffusion/adoption models.
- Turn model outputs into decisions by propagating uncertainty, simulating scenarios, evaluating channel efficiency, and making risk-aware recommendations.
- Operationalize measurement with refresh workflows, experiment planning, data governance, and clear communication of uncertainty to stakeholders.
About this course
This course is a hands-on, notebook-driven program designed for data scientists and analysts who work with marketing teams and need practical, domain-specific modeling frameworks. Through instructor-led Jupyter/Colab notebooks, participants will build probabilistic media-mix models, design and analyze quasi-experiments, estimate customer lifetime value, apply multivariate interrupted time-series and discrete choice models for measuring customer choice, and use the Bass diffusion model to forecast adoption patterns.
A core focus of the course is developing uncertainty-aware decision rules, enabling teams to translate model outputs into clear, risk-informed recommendations across marketing and product strategy. Throughout the course, participants will work directly with PyMC-Marketing and CausalPy to apply these methods in real-world contexts.
Who should join?
This course is ideal for data scientists and analysts with marketing experience who work with marketing teams and want to apply domain-specific modeling frameworks to real-world marketing challenges.
Participants will get the most value from the workshop if they have:
- Technical skills: intermediate experience with Python, comfort running and modifying Jupyter or Colab notebooks.
- Statistical foundations: familiarity with linear regression, basic understanding of probability and distributions, and causal-inference intuition.
- Marketing domain knowledge: working familiarity within the marketing and advertising space.
Course Outline
| Date | Time (ET) | Instructor | Topic |
|---|---|---|---|
| Feb 02 | 3-5pm EST |  Timothy McWilliams | Introduction to Marketing Analytics |
| Feb 04 | 3-5pm EST | Timothy McWilliams | MMM Fundamentals |
| Feb 09 | 3-5pm EST | Timothy McWilliams | Hierarchical & Advanced Modeling Methods for MMMs |
| Feb 11 | 3-5pm EST |  Timothy McWilliams, Carlos Trujillo | Optimization and Scenario Planning |
| Feb 16 | 3-5pm EST |  Timothy McWilliams, Ben Vincent | Calibrating MMM's with quasi-experiment: CausalPy & PyMC Marketing |
| Feb 18 | 3-5pm EST |  Timothy McWilliams, Colt Allen | Customer Lifetime Value: Estimating Monetary Value of Each Customer |
| Feb 23 | 3-5pm EST | Timothy McWilliams | Capturing Product Adoption with Bass Diffusion Models |
| Feb 25 | 3-5pm EST | Timothy McWilliams | Customer Choice Modeling: Multivariate Interrupted Time-series and Discrete Choice 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
Colt Allen is a Principal Data Scientist at PyMC-Labs, and brings over 10 years of experience in a variety of industries including marketing analytics, renewable energy & electric utilities, transportation & logistics, and manufacturing. He is a regular contributor to open-source software and the lead developer for Customer Lifetime Value modeling in PyMC-Marketing.
Colt holds an MS in Mineral & Energy Economics from Colorado School of Mines and a BS in Industrial Engineering. He is a Six-Sigma Greenbelt and INFORMS Certified Analytics Professional (CAP).
Still Having Doubts?
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.)
Decision-Focused Marketing Analytics
- Beginner
- Starts February 02
- 4 weeks
- 8 Live Workshops
- Access to Alumni Discord Q&As
- Instructors: Timothy McWilliams, Colt Allen, Ben Vincent, Carlos Trujillo
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.