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Many data analysis tasks can be represented or approximated as sensitivity analyses, including frequentist covariance estimates, linear response Bayesian covariances, local sensitivity to priors, data, or hyperparameters, approximate leave-k-out estimators, and the infinitesimal jackknife.
Many data analysis tools now exist, and several offer a large selection of generic applications thanks to the use of their own internal data format, but the idea of a common data format for all these systems is utopic.
Confronted with high dimensional data in many data analysis tasks, feature selection techniques are designed to find a relevant feature subset of the original features which can facilitate classification.
Similarly to many data analysis and management tasks, kNN joins suffer from the curse of dimensionality [16].
However, many data analysis techniques need iterative processing such as machine learning, graph analysis, and data mining algorithms [8 10].
While many data analysis tools are known to date, their effective usage in practical application poses challenges which need to be overcome.
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Many data-analysis companies explicitly exclude login credentials from the data they collect, but the researchers found that analytics provider Mixpanel still accidentally collected user passwords under certain conditions.
It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.
Deep learning is widely accepted as the state-of-the-art methodology in many automated data analysis tasks.
In many retellings, data analysis became the reason George W. Bush was re-elected.
And Linux is the preferred operating system for much Big Data software, notably Hadoop, the foundation layer that manages many distributed, data analysis applications.
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