The Unofficial Google Data Science Blog

Quantifying the statistical skills needed to be a Google Data Scientist

Towards optimal experimentation in online systems

Measuring Validity and Reliability of Human Ratings

Uncertainties: Statistical, Representational, Interventional

Why model calibration matters and how to achieve it

Adding common sense to machine learning with TensorFlow Lattice

Changing assignment weights with time-based confounders

Humans-in-the-loop forecasting: integrating data science and business planning

Estimating the prevalence of rare events — theory and practice

Misadventures in experiments for growth

Crawling the internet: data science within a large engineering system

Compliance bias in mobile experiments

Designing A/B tests in a collaboration network

Unintentional data

Fitting Bayesian structural time series with the bsts R package

Our quest for robust time series forecasting at scale

Attributing a deep network’s prediction to its input features

Causality in machine learning

Practical advice for analysis of large, complex data sets

Statistics for Google Sheets

Next generation tools for data science

Mind Your Units

To Balance or Not to Balance?

Estimating causal effects using geo experiments

Using random effects models in prediction problems