Elder Research codeveloped HOTPITS, a modular, physics-based framework for predicting and simulating the hot corrosion of Ni-based superalloy components in gas-turbine engine components.
Data Mining for Business Analytics is used at over 560 universities and colleges, and has been translated into Korean and Chinese. It has been adapted for four software environments (R, Python, Excel and JMP) and, since it was first published in 2007, has been through 11 editions.
Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers at multiple levels of exposure to basic probability and statistics.
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective.
Practical Statistics explains how to apply key statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
A global humanitarian organization asked Elder Research to develop a customized training and collaborative modeling framework.
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.
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.
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.
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.