Intermediate · Cohort
Statistics for Model Builders
Bridge the gap between textbook statistics and modeling decisions. Covers distributions, hypothesis testing, and A/B test interpretation without economics-heavy framing.
Request informationFeatures
- Bayesian and frequentist intuition modules
- Simulation-based power analysis labs
- Experiment design case studies
- R and Python parallel examples
- Office hours with statistician mentors
- Take-home problem sets with worked solutions
Outcomes
- Design a simple A/B test with pre-registered metrics
- Interpret confidence intervals in model monitoring contexts
- Identify common p-hacking pitfalls in dashboards
Dr. Mei Chen
PhD applied statistics; corporate training background.
FAQ
High-school algebra and comfort with logarithms; we provide calculus refresher PDFs optional.
Reviews
"The simulation lab on false positives changed how I read our experiment dashboards."
"Dense but fair — the confidence interval module was worth the weekend hours."