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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics includes the tools and techniques used to perform data analysis.

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MLOps and the evolution of data science

IBM Big Data Hub

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments. Multiparameter experiments, however, generate richer data than standard A/B tests, and automated t-tests alone are insufficient to analyze them well. Testing Statistical Hypotheses.

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

One way to check $f_theta$ is to gather test data and check whether the model fits the relationship between training and test data. This tests the model’s ability to distinguish what is common for each item between the two data sets (the underlying $theta$) and what is different (the draw from $f_theta$).

KDD 40
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Magnificent Mobile Website And App Analytics: Reports, Metrics, How-to!

Occam's Razor

Upsight (nee Kontagent) provides mobile app analytics, with a pinch of advanced segmentation (including sweet cohort analysis ) and big data mining thrown in for good measure. One way to overcome this issue is to use media-mix modeling to run tests and measure incrementality in results and attribute it to the optimal channel.

Metrics 141