Python for analytics and A/B testing

Python takes web analytics and A/B testing past the limits of standard dashboards. It handles very large datasets, automates complex statistical tests, and supports custom attribution models that GA4 can’t build out of the box.

Data acquisition and cleaning

Python notebook merging GA4 sessions with CRM sales data

Python merges data from different sources, such as GA4 session data with internal CRM sales data. It handles missing values, filters out bot traffic and aligns timestamps so funnel analysis is accurate.

Bayesian A/B testing

Bayesian A/B test showing the probability that variant B beats variant A

Standard (frequentist) A/B testing gives you a p-value, which is hard to interpret. A Bayesian test gives you the number people actually want: the probability that variant B is better than variant A.

Sample size and power calculation

Most people guess how long a test should run. It can be calculated exactly.

I built Python scripts for power analysis that set the minimum sample size and minimum detectable effect (MDE) before launch. That optimizes traffic allocation and prevents underpowered tests.

See how this fits into a wider experimentation program.