The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

MARCH 31, 2016

Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. bandit problems). 5] Anoop Korattikara, et al.

Modeling 40

Modeling Statistics Advertising Testing 40

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

APRIL 16, 2019

The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason. As a direct result, less IT support is required to produce reports, trends, visualizations, and insights that facilitate the data decision making process. Qualitative data analysis is based on observation rather than measurement.

Data-driven 287

Data-driven Dashboards Visualization Cost-Benefit 287

O'Reilly on Data

JANUARY 25, 2021

AI, Machine Learning, and Data. Healthy growth in artificial intelligence has continued: machine learning is up 14%, while AI is up 64%; data science is up 16%, and statistics is up 47%. These problems will be solved eventually, with a new generation of tools—indeed, those tools are already being built—but we’re not there yet.

Machine Learning 363

Machine Learning Software Testing Technology 363