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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.

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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.

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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.

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Are We Using Machine Learning?

Gerhard Pilcher

August 24, 2018

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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. 

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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.

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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 Mining 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.

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Automating Demand Forecasting with Machine Learning

Will Goodrum, Ph.D.

August 3, 2018

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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.

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3 Myths About the Normal Distribution

Peter Bruce

July 27, 2018

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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.

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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.

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Machine Learning for Disease Event Detection

Jennifer Schaff, PhD

July 13, 2018

BLOG_Machine Learning for Disease Event Detection

Viral respiratory illnesses can be particularly challenging for people with asthma. A cold or flu can lead to acute respiratory complications or even death in asthmatics. Elder Research developed a predictive algorithm to define asthma, and identify asthma sufferers who are ill and at risk for major respiratory complications, allowing for intervention prior to onset of acute asthma symptoms, thereby potentially
preventing hospital stays and even loss of life.

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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.

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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.

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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.

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Team Diversity: Women In Data Science

Paul Derstine

June 15, 2018

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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.”

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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? 

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