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Book Review of Closing the Analytics Talent Gap: An Executive’s Guide to Working with Universitiesby Dr. Jennifer Priestley and Dr. Robert McGrath (CRC Press 2021, part of the Data Analytics Applications series, edited by Jay Liebowitz). We started Enolytics a few years ago in order to fill the gap we saw between the abundance of […]
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By: Sam Koslowsky, Senior Analytic Consultant,
Harte Hanks
You have been invited to serve as a juror in a criminal related case. After hearing testimony, the presiding judge offers a summary of the proceeding. “Evaluate the evidence,” he declares. Whether it was an eyewitness account, an affidavit, an image, or a recording, “it is your responsibility” to assess what was heard. Although “I […]
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Originally published in KDnuggets, Nov 2020. This article is based on a transcript from Eric Siegel’s Machine Learning for Everyone (on Coursera). View the video version of this specific article Just as businesses tap the value of machine learning, so too can charitable and non-profit organizations. There are a wide variety of ways people […]
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By: Sherrill W. Hayes, Executive Director of the Analytics and Data Science Institute and Professor of Conflict Management,
Kennesaw State University
The complexity of the ethical issues facing professionals who work in machine learning, data science, analytics, and related professions have all the hallmarks of a “wicked problem”. Rittel and Weber, the researchers responsible for coining the term “wicked problems”, believed a more inclusive approach to problem-solving was especially important in diverse and pluralistic societies where […]
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Straight from the horse’s mouth – the founding chair of the all-new Predictive Analytics World for Climate, Eugene Kirpichov, along with his colleague, Cassandra Xia – read this article for the central role machine learning has to play for climate tech and access the viral “Goodbye, Google” posts that marked their departure from big […]
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Originally published in KDNuggets. This article is based on a transcript from Eric Siegel’s Machine Learning for Everyone. View the video version of this specific article Nowhere could the application of machine learning prove more important — nor more risky — than in law enforcement and national security. In this article, I’ll review this […]
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Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk […]
