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A predictive big data analytics algorithm using a variety of demographic and clinical data...
Today’s business executives are increasingly applying pressure to their Human Resources departments to “use...
Over the last five years, electronic health records (EHRs) have been widely implemented in...
Data miners employ a variety of techniques to develop robust predictive models. Often, our analysts are confronted with a dilemma. Should we construct one model to address the business objective? Or perhaps, multiple models may be in order? Take, for example, a marketer that has a presence on the east coast and in the mid-west. […]
This commentary first appeared in the San Francisco Chronicle. Originally published as the cover piece for the Insight commentary section in the Sunday San Francisco Chronicle, this op-ed by Eric Siegel points out that, although many believe banning or monitoring Muslims would keep us safer, religious screening compromises the advancements in security we stand to […]
In anticipation of his upcoming conference presentation, The Sprint for Teaching Data Science: LinkedIn Learning, Analytics and the New Era of Just-In-Time Skills Training at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Steve Weiss, Content Manager, Data Science and Business Analytics, at LinkedIn, a few questions about his work […]
In anticipation of her upcoming conference presentation, Which Predictive Model Will Best Help Increase Retention? at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Emilie Lavoie-Charland, Research & Innovation Analyst at The Co-operators, a few questions about her work in predictive analytics. Q: In your work with predictive analytics, what behavior […]
This author will present at Predictive Analytics World, Oct 29 – Nov 2 in New York. This article is excerpted from his book, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. The book delivers a fresh overview of big data with an emphasis on the intriguing insights revealed by […]
As I have stated in previous articles, the most difficult challenge in building predictive models is the creation of the analytical file. Typically, this comprises between 80%-90% of the data scientist’s time with 10%-20% comprising the actual run or runs of the different mathematical/statistical algorithms. In the creation of the analytical file, the two elements […]
Talent Analytics uses data gathered from our own proprietary talent assessments as an input variable to predict hiring success – pre-hire. We treat this dataset just like any other dataset in our predictive work. We are careful to analyze it for a strong (or weak) correlation to actual job performance. Our theory? If there is […]