Blog Header 1.jpg

Blog

Sort by Topic

How to Automate Machine Learning Model Tuning

Trent Bradberry

December 7, 2018

BLOG_How to Automate Machine Learning Model Tuning-1

I’ve long heard that “a watched pot never boils,” but when I am heavily invested in the outcome of a process, I still tend to monitor it intently. In my impatience I begin to wonder if my attention is worse than useless, and is actually impeding the progress of the process! When considering machine learning models, it may be true. Let me explain.

Read More

Top 3 Lessons Learned While Drinking from the Data Science Firehose

Sam Ballerini

November 30, 2018

BLOG_Top 3 Lessons Learned While Drinking from the Data Science Firehose

I had one hard requirement during my job search: wherever I ended up, I wanted to drink from the "data science firehose." I wanted to work alongside seasoned data scientists with diverse skillsets and an unadulterated passion for solving problems with data. I wanted to leave the office after my first day asking myself, “How in the world am I going to keep up with these people?” And that's exactly what I've gotten at Elder Research.

Read More

Monte Carlo Simulation - a Venerable History

Peter Bruce

November 16, 2018

BLOG_Monte Carlo Simulation

One of the most consequential and valuable analytical tools in business is simulation, which helps us make decisions in the face of uncertainty.

Read More

Simple, Attractive, and Wrong: An Introduction to Linearity Bias

Will Goodrum, Ph.D.

November 2, 2018

BLOG_Introduction to Linearity Bias

This blog is 3rd in a series of short posts where we explore common biases that can impair analytics projects.

Read More

Top 3 Objectives Before Starting an Analytics Project

Gerhard Pilcher

October 26, 2018

BLOG_Top 3 Objectives Before Starting an Analytics ProjectUnderstanding the organization’s business objectives and requirements, converting this knowledge into a definition of a problem, and developing a preliminary plan to solve that problem is crucial to the successful application of analytics and machine learning. In order to construct a successful model, the data scientist must understand how the business functions and how it will use the data. Even the most technologically advanced analytics model will produce trivial and possibly misleading results if it is disconnected from the purposes and goals of the business.

Read More

Machine Learning: It is a Mistake to Lack Relevant Data

John Elder, Ph.D.

October 19, 2018

BLOG_It's a Mistake to Lack Relevant DataIn his Top 10 Data Science Mistakes Dr. John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #0 you will learn that the less probable the interesting event is, the more data it takes to obtain enough to generalize a model to unseen cases, and why some projects probably should not proceed until enough critical data is gathered to make them worthwhile.

Read More

Finding Fraud When No Cases are Known

Carlos Blancarte

October 12, 2018

BLOG_Finding Fraud When No Cases are Known

Fraud detection is about finding needles in haystacks and requires reliably labeled instances of fraudulent (needle) and non-fraudulent (straw) behavior. A predictive model can be trained using these labels to learn the underlying patterns in the input variables that best separate fraud from non-fraud cases, and thereby estimate the fraud-likeness of any future case. Typically, the interesting cases are very scarce, in which case we might have to carefully up-sample the rare class and/or down-sample the abundant class to help the model pay enough attention to the rare class to be useful. But what do we do when labels are not just rare, but are completely absent?

Read More

Hiring a Data Analytics Consultant

Jeff Deal

October 5, 2018

BLOG_Hiring Data Analytics Consultants-1In the earliest days of data analytics, our new clients would typically say, “Solve this problem for us.” As they saw the enduring power of analytics, their request then became, “We want to launch our own data analytics capability. Will you help us set it up?” However, growing an analytics capability from scratch is a huge challenge, and today more companies appreciate its difficulty. 

Read More

Who Invented the Null Hypothesis?

Peter Bruce

September 28, 2018

BLOG_Who Invented the Null Hypothesis

For many students, statistics is a troublesome subject, and the root of that trouble can be traced to the concept of the null hypothesis. In these days of big data, machine learning, and predictive analytics, formal hypothesis testing has receded in relative importance. Nonetheless, it retains considerable inertia and ability to cause difficulty - even in data science circles.

Read More

Workshops, Workshops, Workshops!

Paul Derstine

September 14, 2018

BLOG_PAWgov Workshops

Predictive Analytics World for Government, the premier analytics and AI conference for government, starts next week. Keynote speakers include David Williams, USPS Board of Governors, Tom Davenport, a world-renowned analytics thought leader, author, and industry expert, and Dr. John Elder, Chairman and Founder of Elder Research, author and analytics practitioner. Elder Research will teach three workshops at the conference, offering insight on Machine Learning Methods, Deadly Analytics Mistakes, and Data Science for Managers.

Read More

Fluency in The Language of Data Models

Michael Lieberman

September 7, 2018

BLOG_Fluency in The Language of Data Models

My job as a data scientist and research strategist is getting easier. Over the past 50 years, statisticians have developed a number of practical models that are highly effective to explain consumer patterns and predict consumer behavior. As new forms of computing power and information technology provide every increasing descriptions of individual-level purchasing tendencies, these models offer great value for business managers.

Read More

Data Science, Statistics, and the "Method of Moments"

Peter Bruce

August 31, 2018

 

BLOG_Data Science, Statistics, and the Method of Moments

I got my introduction to statistics via resampling, working with Julian Simon, an early resampling pioneer. Demonstrating this "brute force" computer method to my father, I saw that he was vaguely offended by its inelegance.

Read More

Are We Using Machine Learning?

Gerhard Pilcher

August 24, 2018

BLOG_Are We Using Machine Learning

In the midst of a recent engagement an executive suddenly asked, “Are we using Machine Learning?”. This caught us off-guard; working in the field for many years, we use the “learning sciences” virtually every day to solve hard problems. Machine Learning (ML), Data Science (DS) and Artificial Intelligence (AI) are exciting and very powerful; still, we’re happy to use conventional techniques whenever they’re the best choice to solve the client’s challenge. 

Read More

Using Machine Learning to Predict Parkinson’s Disease

Jennifer Schaff, PhD

August 17, 2018

BLOG_Parkinson’s Test Recommendation Engine

Recent research supported by the Michael J. Fox Foundation (MJFF) (and other benefactors) collected multifaceted data sets from patients with Parkinson’s Disease. They wanted to determine which medical test, or combination of tests, best predicts Parkinson’s disease.

Read More

It is a Mistake to Ask the Wrong Questions

John Elder, Ph.D.

August 10, 2018

BLOG_It is a Mistake to Ask the Wrong QuestionsIn his Top 10 Data Science Mistakes Dr. John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #3 you will learn why it is very important to have the right project goal; that is, to aim at the right target; and even with the right project goal it is essential to also have an appropriate model goal.

Read More

Automating Demand Forecasting with Machine Learning

Will Goodrum, Ph.D.

August 3, 2018

BLOG_Automating Demand Forecasting

Elder Research implemented an automated framework for time-series forecasting at a major logistics company. Our system, combining R and Apache Spark™, produces 35 million forecasts in under one hour, and selects the optimal time-series forecast algorithm in each of three forecasting windows. Forecast results from our framework were 88% accurate at a four-week horizon.

Read More

3 Myths About the Normal Distribution

Peter Bruce

July 27, 2018

BLOG_3 Myths About the Normal Distribution

Is the Normal Curve Normal?

I saw an article recently that referred to the normal curve as the data scientist's best friend, and it is certainly true that the normal distribution is ubiquitous in classical statistical theory. Still, it's overrated.

Read More

Hype or Reality: The ROI of Machine Learning

Paul Derstine

July 20, 2018

BLOG_Hype or Reality-The ROI of Machine Learning

The hype around “the thinking sciences” — Artificial Intelligence, Machine Learning, and Data Science — is enormous, so it’s tempting to be skeptical of the return on investment (ROI) claimed. Still, most of the results are real. The capabilities of Data Science and Machine Learning, where models are inductively built from real history, have been growing steadily.

Read More

Do Algorithms Have Bias?

Peter Bruce

July 6, 2018

BLOG_Do Algorithms Have Bias

Algorithmic bias is a popular topic; see for example this article describing how Microsoft is working on a dashboard product to detect unfair bias in algorithms. When a typical person (not a statistician) uses the term "bias" they usually have in mind unfair prejudgment, or stacking of the deck, against a person based on some aspect of that person's identity (race, gender, ethnic background, religion, nationality, etc.). Until recently, "bias" meant something very different to statisticians.

Read More

Sophisticated Text Analysis Is Hard, but it Works

John Elder, Ph.D.

June 29, 2018

BLOG_Sophisticated Text Analysis Is Hard, but it Works

Since its founding more than twenty years ago Elder Research has been involved in hundreds of data mining projects. Most of those projects employ numerical data, but for about a decade now we have been called on increasingly to extract information from unstructured or semi-structured text.  Though Gartner recently classified Text Analytics as just exiting the “Trough of Disillusionment” on their famous “Hype Cycle”,[1] we have found that every text mining project we have worked on has been a success.

Read More

Prediction in the Public Sector: Why the Government Needs Predictive Analytics

Eric Siegel

June 22, 2018

BLOG_Prediction in the Public Sector

Data can appear lifeless and dull on the surface—especially government data—but the thought of it should actually get you excited. Data is the very most interesting and powerful thing. First off, data is exactly the stuff we bother to write down—and for good reason. But its potential far transcends functions like tracking and bookkeeping: Data encodes great quantities of experience, and computers can learn from that experience to make everything work better.

Read More

Team Diversity: Women In Data Science

Paul Derstine

June 15, 2018

BLOG_Team Diversity_Women in Data Science

According to Women in Tech: The Facts, a report by the National Center for Women & Information Technology (NCWIT), “In 2015, women held 57% of all professional occupations, yet they held only 25% of all computing occupations.”  The NCWIT report authors believe that this pattern is “especially troubling given ample evidence of the critical benefits diversity brings to innovation, problem-solving, and creativity. Indeed, a solid body of research in computing and in other fields documents the enhanced performance outcomes and benefits brought about by diverse work teams.”

Read More

Share Your Case Study at Predictive Analytics World for Government

Paul Derstine

June 1, 2018

BLOG_Apply to Speak at PAW Gov

The only conference of its kind, Predictive Analytics World for Government advances the deployment of analytics within federal, state and local government -- to drive smarter decisions, automate manual processes, and reduce fraud, waste, and abuse -- by extracting actionable insights from vast quantities of data. Are you interested in sharing your case studies, lessons learned, or best practices for using analytics to further your mission? 

Read More

Developing an Analytics Strategy: The Analytics Champion

Robert Pitney

May 25, 2018

 BLOG_The Role of the Analytics Champion

As outlined in Developing an Analytics Strategy: The Role of Culture, there are five facets to “cultural infrastructure” that organizations must address to realize the full potential of analytics.  This may require significant cultural change, which must begin with executive leadership, i.e. setting the “tone at the top”.  We recommend appointing an Analytics Champion to maximize value from analytics. Here, I’ll describe the key traits of an Analytics Champion and how they can set up an organization for future “wins” with analytics.

Read More

Why You Should Attend Predictive Analytics World

Paul Derstine

May 18, 2018

 BLOG_Predictive Analytics World-Las Vegas

This year there will be only ONE Predictive Analytics World conference in the U.S. Billed as Mega-PAW, it will be the largest Predictive Analytics World event to date and is the premier cross-vendor conference for machine learning and predictive analytics professionals, managers and commercial practitioners. The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail.

Read More