The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

MARCH 23, 2021

From 2006: Is Real-Time Analytics Really Relevant? ). In our in-flight optimization journey thus far, we have worked to identify signals that are believable, and identifying at which point they become believable (ex: statistically significant). You have the start of a fabulous in-flight optimization engine.

Analytics 131

Analytics Scorecard Strategy Testing 131

The Unofficial Google Data Science Blog

APRIL 17, 2017

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. Selection and aggregation of forecasts from an ensemble of models to produce a final forecast.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111

Modeling Statistics Testing Metrics 111

Occam's Razor

MARCH 12, 2012

Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more. The current flawed data org structure, its challenges, and the new optimal org structure to truly bring big action to big data. 01:15 – 04:05 Part 1.

Big Data 127

Big Data Data-driven Unstructured Data Marketing 127

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40