Target Shuffling is a process for testing the statistical accuracy of data mining results. It is particularly useful for identifying false positives, or when two events or variables occurring together are perceived to have a cause-and-effect relationship, as opposed to a coincidental one. The more variables you have, the easier it becomes to ‘oversearch’ and identify (false) patterns among them—called the ‘vast search effect’. Learn more
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What is Target Shuffling?
How Target Shuffling Can Tell if What Your Data Says is Real
John Elder presented "How Target Shuffling Can Tell if What your Data Says is Real" at the Haas School of Business, University of California, Berkeley, as part of the Data Science & Strategy Lecture Series.
John Elder Interviewed for the BerkleyHaas Data Science & Strategy Lecture Series
In this interview with John Elder, host Professor Greg La Blanc discusses the crisis of reproducibility in academic and scientific research and how Target Shuffling can help confirm results. Held at the Haas School of Business, University of California, Berkeley, the Data Science & Strategy Lecture Series seeks to provide an understanding of the role of data and statistical analysis in managerial decision-making. The focus is on the role of managers as both consumers and producers of information, illustrating how finding and/or developing the right data and applying appropriate statistical methods can help solve problems in business. In this video series, Haas lecturer and lecture series host Greg La Blanc interviews industry executives and data science practitioners on key topics in data science, including data mining, machine learning, visualization, and more.
Predict: Bringing Analytic Fire to the Tribal Circle
John Elder was a keynote speaker at Predict 2016 in Dublin, Ireland, October 2016. In his talk "Bringing Analytic Fire to the Tribal Circle" Dr. Elder pointed out that finding the answer and proving it, doesn’t mean it will be used. He highlighted why data scientists must build trust as well as great models, so your work doesn’t end up on the shelf.
Postal Service’s Inspectors General Office Discusses Data Analytics Solutions Developed by Elder Research
Kelly Tshibaka, chief data officer in the Office of Inspectors General at the U.S. Postal Service, discusses the way the OIG is using data analytics. The fraud detection solutions discussed were developed by Elder Research. Read the case study.
In total, the Office of the Chief Data Officer she heads has used analytics to peruse millions of pages of paperwork to produce more than 500 leads for investigators across the $13 billion the Postal Service spends on contracts annually, with “only one of them a false positive.”
“Last year, our tools and models helped us contribute to $920 million in findings,” Tshibaka said. “Anyone in the position of having to do more with less? Your answer is data analytics.” Read more here.
Learn more about our Risk Assessment Data Repository (RADR), a powerful risk analytics platform used to enhance productivity in the investigation of fraud, waste and abuse. RADR was deployed by the U.S. Postal Service OIG and is used by more than 1000 investigators.
Elder Research Validates RightShip's Qi Model for Predicting Maritime Vessel Risk
RightShip Qi brings the benefits of big data and predictive analytics to improve maritime safety and sustainability. Qi builds on the incumbent SVISTM expert opinion platform, but harnesses big data, predictive analytics and real-time risk assessments to better target substandard maritime performance. Vast quantities of ever-changing data are analysed by sophisticated algorithms to spot patterns and draw conclusions from data sets too large, diverse and dynamic for analysis with previous technology. Prior to launch RightShip contracted Elder Research to validate Qi's predictive performance.
Dr. Miriam Friedel Participated on the Next-Generation Analytics Social Influencer Roundtable at BigDataNYC 2016 Conference
Predict: Journey From Data to Predictive Analytics
John Elder will keynote and give a workshop at "Predict 2016", in Dublin, Ireland, Oct. 4-6, 2016. Dr. Elder also participated in the inagural conference in 2015 and was selected from among the 40 speakers to be interviewed when Siliconrepublic.com visited the opening day of Predict 2015.
Interview with Data Scientist Dave Saranchak About Statistical Data Modeling Techniques for National Security Clients
Join IBM data science evangelist James Kobielus and Dave Saranchak, a data scientist with Elder Research, to discover how Dave develops and applies statistical data modeling techniques for national security clients. Also, learn how Dave identifies technologies that can suit clients’ needs as he leads training for Elder Research’s Maryland office.
John Elder at Predict 2015 on The Power of Predictive Analytics
John Elder Interviewed at Predict 2015
John Elder Interviewed at SAS Analytics Conference
Predictions 2014: John Elder on SAP Coffee Break
John Elder appears on Coffee Break with Game-Changers presented by SAP and hosted by Bonnie D. Graham about predictions for 2014. ~11 minutes
For the full show and original source visit http://www.voiceamerica.com/episode/74952/game-changers-2014-predictions-part-2.
Big Data: John Elder Keynote on Business Innovation in America (May 2013)
Jeff Deal - Common Data Mining Business Mistakes (March 2012)
Jeff Deal discusses some of the more common data mining mistakes that organizations make, including failing to define an objective and waiting on perfect data.
Deal, Vice President of Operations at Elder Research, is interviewed by Anna Brown, Editor of the SAS Business Analytics Knowledge Exchange.
John Elder - Influence of "Big Data" on Analytics (March 2012)
John Elder discusses how "Big Data" is changing traditional and text analytics. John also talks about his new book "Practical Text Mining and Statistical Analysis."
Elder, of Elder Research, is interviewed by Anna Brown, Editor of the SAS Business Analytics Knowledge Exchange.
John Elder on Innovation with Analytics
John Elder on Text Analysis
At the M2010 conference, Dr. Elder was interviewed on mining and analyzing unstructured text to expedite government review of disability claims.
Predictive Analytics Training: Interview with John Elder
Top 10 Data Mining Mistakes
Dr. Elder gives his famous talk on the Top Ten Data Mining Mistakes. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also view the whitepaper (PDF).
Don't Rely on Only One Technique
A continuation of Dr. Elder's talk on the top ten data mining mistakes and how to avoid them. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also view this talk as a PDF.
The third part of Dr. Elder's talk on the top ten data mining mistakes and how to avoid them. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also view this talk as a PDF.
The Path to Data Mining Success
The fourth and final part of Dr. Elder's talk on the top ten data mining mistakes and how to avoid them. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also view the whitepaper (PDF).
John Elder Interview at M2009
Dr. Elder sits down with Stacey Hamilton to talk about his book, The Handbook of Statistical Analysis and Data Mining Applications at the M2009 conference.