John Elder was featured on the SuperDataScience podcast where he discussed a wide range of topics such as turning real-world problems into data, complex math, finding data anomalies, campfire data tales from his career, leaks from the future, how to measure complexity, Occam’s razor, and more.
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Data Science Campfire Tales with John Elder
Predict 2019: John Elder Presents the Top 3 Things I've Learned in 3 Decades of Data Science
Predictive Analytics World 2019: Gerhard Pilcher Podcast
In this segment of the 2019 Predictive Analytics World (PAW) Conference Series recorded live in Las Vegas, Happy Market Research host Jamin Brazil interviews Gerhard Pilcher, CEO of Elder Research.
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Datapalooza 2017 Keynote: The Data Science Revolution in Industry
Predict 2017: Adaptability and Intuition in Data Science
At Predict 2017 in Dublin, Ireland, Elder Research CEO Gerhard Pilcher discussed the gap between the promise of analytics to transform a company and the actual results, and how adaptability and intuition by leadership can help close that gap.
Predict 2017: Misleading Discoveries in Data
John Elder was a keynote speaker at Predict 2017 in Dublin, Ireland, October 2017. In this video Dr. Elder discusses the extent of misleading discoveries in data, especially in medical research, and how data science can help validate these discoveries.
What is Target Shuffling?
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
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.
Validating RightShip's Qi Model for 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.
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
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.
Top 10 Data Science Mistakes
Dr. Elder gives his famous talk on the Top Ten Data Science Mistakes. The Top Ten Mistakes are covered in chapter 20 of the Handbook of Statistical Analysis & Data Mining Applications. You can also download the Ebook (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).