datapine
NOVEMBER 19, 2019
In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward.
The Unofficial Google Data Science Blog
JANUARY 14, 2016
by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data. And an LSOS is awash in data, right?
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datapine
NOVEMBER 19, 2019
In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data Discovery tools available in the market to take their brand forward.
The Unofficial Google Data Science Blog
JULY 22, 2020
For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. Ramp-up solution: measure epoch and condition on its effect If one wants to do full traffic ramp-up and use data from all epochs, they must use an adjusted estimator to get an unbiased estimate of the average reward in each arm.
The Unofficial Google Data Science Blog
FEBRUARY 29, 2016
And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Let’s go back to our example of measuring the fraction of user sessions with purchase. Let $Y_i$ be the response measured on the $i$th user session.
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