The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. You're choosing only one metric because you want to optimize it. Remember that the raw number is not the only important part, we would also measure statistical significance. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Occam's Razor

SEPTEMBER 13, 2016

If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis. Hypothesis development and design of experimentation. Take this as an example… How do you know that this is a profoundly sub-optimal collection of choices to provide? Pattern recognition and understanding trends.

Modeling 127

Modeling Reporting Strategy Consulting 127

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] You can home in on an optimal value by specifying, say, 32 dimensions and varying this value by powers of 2. If we were using CBOW, then a window size of 5 (for a total of 10 context words) could be near the optimal value. Note: Maas, A.,

Deep Learning 111

Deep Learning Modeling Metrics Testing 111