Predictive Maintenance: Common Challenges & How to Overcome Them

How to Use Random Forests to Beat Gerard Butler

Using Data to Win Gold for U.S. Ski & Snowboard with Gus Kaeding | Mining Your Own Business

Building AI at Home Depot with John Carroll & Jon Weininger | Mining Your Own Business Podcast

The Problem With Forecasting in the Hospitality Industry

Building Analytic Momentum with Alex Cunningham | Mining Your Own Business

Delivering a Model vs. Delivering Change with Gerhard Pilcher | Mining Your Own Business

You Want the Truth – Adapt Training Domain to Improve Q&A on Technical Text

Predicting and Simulating Hot Corrosion of Gas-Turbine Engine Components

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

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

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.

Practical Statistics for Data Scientists: 50 Essential Concepts

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.

Creating A Predictive Modeling Framework

A global humanitarian organization asked Elder Research to develop a customized training and collaborative modeling framework. 

Detecting Fraud Rings with Graph Databases

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.

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.

Uplift Modeling: Making Predictive Models Actionable

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.

Top 10 Data Science Mistakes

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.