INTRO TO MARKETING SCIENCE
A five-part certification series from the Data Science Team at Postie.
You don't need a PhD to think like a data scientist. You just need the tools to know where to start.
Led by Postie Data Scientist Bryan Scott, PhD, this course is built for marketers who live inside campaigns, dashboards, and reports, and want to truly understand what the numbers are telling them, not just take a platform’s word at face value.
We’ll break down the real mechanics behind marketing measurement and modeling, in plain language. You'll learn how a one-size-fits-all view of your customers can hide your most valuable ones, the real difference between attribution and true incremental lift, and what's actually happening inside a model when it makes a prediction. Along the way, you'll become an expert in asking sharper questions about your own results.
No coding background or statistics degree required — just curiosity about what's behind the metrics you already work with every day.
This series of live and on-demand sessions is designed so you can work at your own pace. A live intro session: a kickoff to set the stage, three courses of learning on-demand, followed by a live closing session where you can bring your questions directly to the instructor.
Complete the course and earn a certificate of completion you can add to your resume.
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✨ Start Your Journey! ✨
Class 1 — Marketing science foundations.
The three pillars that run through everything else: modeling, marketing, and measurement —
and why treating them as separate departments is where most programs quietly fail.
Class 2 — Probability and uncertainty.
Why an average is a single point pulled from a much richer distribution, and why the range around a number often matters more than the number itself.
Class 3 — The bias-variance trade-off.
Why a "smarter" model can perform worse, and how to tell whether a disappointing model underfit, overfit, or just needs better data.
Class 4 — Introduction to machine learning.
How a computer derives its own targeting rules from examples instead of rules you write by hand — and why the model needs testing on data it's never seen before you trust it.
Class 5 — Marketing science in practice.
A case study, start to finish: a fictional client walked through all three pillars at once —
audience construction, why testing one model is a losing bet, and why offline measurement has to be designed in from day one, not bolted on after.
Your Instructor
Bryan Scott, PhD
Data Scientist
Postie 🤖
Dr. Bryan Scott is a Data Scientist at Postie and an astro-statistician whose career has spanned some of the most data-intensive research environments in modern science. He holds a PhD in Physics and Astronomy from the University of California, Riverside, where his research focused on intensity-mapping applications in galaxy evolution and modifications of Einstein's theory of General Relativity. He was named the 2024 Hunstead Lecturer at the University of Sydney — an annual distinction reserved for internationally recognized scientists — and has held active memberships in three major cosmological research collaborations: CASTOR, the LSST Dark Energy Science Collaboration, and the Subaru Telescope Prime Focus Spectrograph Galaxy Evolution Survey Working Group.
Before joining Postie, Dr. Scott served as the Data Science Fellowship Program Postdoctoral Scholar at Northwestern University's Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), where he also led data science training and education for the next generation of researchers. He holds graduate certifications in higher education pedagogy from UC Riverside and Northwestern's Searle Center for Advancing Teaching and Learning, and previously trained and supported over 600 Teaching Assistants across UC Riverside's academic programs.
In Marketing Science 101, Dr. Scott brings that same rigor — and a genuine gift for making complex methodology accessible — to the frameworks marketers need to make smarter decisions with data.