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Data Mining Use Cases

TDAN

Given that the global big data market is forecast to be valued at $103 billion in 2027, it’s worth noticing. As the amount of data generated […]. “Information is the oil of the 21st century, and analytics is the combustion engine,” says Peter Sondergaard, former Global Head of Research at Gartner. And he has a point.

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KDD 2020 Opens Call for Papers

Data Science 101

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. The details are below.

KDD 81
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How Do Super Rookies Start Learning Data Analysis?

FineReport

For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. From Google. There are two points here.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

A/B testing isn’t simple just because data is big — the law of large numbers doesn’t take care of everything! Even with big data, A/B tests require thinking deeply and critically about whether or not the assumptions made match the data. 2] Scott, Steven L. armed bandit experiments in the online service economy."

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LSOS experiments: how I learned to stop worrying and love the variability

The Unofficial Google Data Science Blog

We could do this but in our big data world, we would avoid materializing such an inefficient structure by reducing the regression to its sufficient statistics. When solved with an intercept term, regression coefficients for the binary predictors are maximum likelihood estimates for the experiment effects under assumption of additivity.