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The Unofficial Google Data Science Blog

unofficialgoogledatascience.com

AI / MLMath

’15’16’17’18’19’20’21’22’23’24’25’26
Posts
36 from 26 Aug 2015 to 24 Mar 2025
Posting
now and then
Kind
company blog
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2025 1

  1. Quantifying the statistical skills needed to be a Google Data Scientist

2024 1

  1. Towards optimal experimentation in online systems

2023 1

  1. Measuring Validity and Reliability of Human Ratings

2021 2

  1. Uncertainties: Statistical, Representational, Interventional
  2. Why model calibration matters and how to achieve it

2020 2

  1. Adding common sense to machine learning with TensorFlow Lattice
  2. Changing assignment weights with time-based confounders

2019 3

  1. Humans-in-the-loop forecasting: integrating data science and business planning
  2. Estimating the prevalence of rare events — theory and practice
  3. Misadventures in experiments for growth

2018 3

  1. Crawling the internet: data science within a large engineering system
  2. Compliance bias in mobile experiments
  3. Designing A/B tests in a collaboration network

2017 5

  1. Unintentional data
  2. Fitting Bayesian structural time series with the bsts R package
  3. Our quest for robust time series forecasting at scale
  4. Attributing a deep network’s prediction to its input features
  5. Causality in machine learning

2016 9

  1. Practical advice for analysis of large, complex data sets
  2. Statistics for Google Sheets
  3. Next generation tools for data science
  4. Mind Your Units
  5. To Balance or Not to Balance?
  6. Estimating causal effects using geo experiments
  7. Using random effects models in prediction problems
  8. LSOS experiments: how I learned to stop worrying and love the variability
  9. Variance and significance in large-scale online services

2015 9

  1. Replacing Sawzall — a case study in domain-specific language migration
  2. How to get a job at Google — as a data scientist
  3. Using Empirical Bayes to approximate posteriors for large "black box" estimators
  4. Data scientist as scientist
  5. Experiment design and modeling for long-term studies in ads
  6. Causal attribution in an era of big time-series data
  7. On procedural and declarative programming in MapReduce
  8. An introduction to the Poisson bootstrap
  9. Welcome to the unofficial Google data science blog