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Human Participation - Still an indispensable element in Business Analytics

DataFloq

Business Analytics synergizes the strengths of various sciences including data mining, knowledge discovery, machine learning, pattern recognition, statistics, neurocomputing, and artificial intelligence. Business Analytics has evolved a lot. Business Analytics was designed for addressing the need for deriving intelligence out of ‘data’, which is nowadays referred to affectionately by many as the ‘crude oil’ or ‘gold ore’ of modern times.

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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). Topics of interest include artificial intelligence, big data, data analytics, data science, data mining, deep learning, knowledge graphs, machine learning, relational databases and statistical methods.

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Education Trends 2022: Data Science in schools

DataFloq

There were previously several statistical surveys that could assess these social-emotional abilities. Combining large amounts of information with existing tools is possible using the formalized knowledge discovery models in Data Science or Data Mining techniques.

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How Do Super Rookies Start Learning Data Analysis?

FineReport

Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown.

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Fundamentals of Data Mining

Data Science 101

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Domain Knowledge. The foremost step of this process is to possess relevant domain knowledge regarding the problem at hand. To anyone looking at a large pile of data, it may seem like a collection of junk unless the person has the background knowledge and information about the business.

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

We can group by study arm and calculate various statistics as mean and standard deviation. The openness of the Domino Data Science platform allows us to use any language, tool, and framework while providing reproducibility, compute elasticity, knowledge discovery, and governance.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

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

The Unofficial Google Data Science Blog

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. In statistics, such segments are often called “blocks” or “strata”.