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Fraud, Anomaly Detection, and the Interplay of Supervised and Unsupervised Learning

Peter Bruce

February 8, 2019

 BLOG_Fraud, Anomaly Detection, and the Interplay of Supervised and Unsupervised Learning-2

Mike Thurber, Lead Data Scientist and fraud specialist at Elder Research, presented Elder Research's fraud detection methodology at Predictive Analytics World for Government last year. Consider the scenario of detecting fraudulent insurance claims, such as the audacious "accidental" death scheme in the 1944 noir film Double Indemnity.

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Group Optimization – An Application of the Nash Equilibrium

Michael Lieberman

February 1, 2019

BLOG_Group Optimization_Nash Equilibrium

The Nash Equilibrium—see A Beautiful Mind—in economics and game theory is defined as a stable state of a system involving multiple participants, where no one can gain by a change of strategy if the strategies of the others remain unchanged. More simply, it is a maximized state where no other players will agree if one player changes her position. In terms of economics or business, it is the most equitable, though not always the most obvious, solution to a multi-party conflict.

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The Data Speak: The World Is Getting Better

Peter Bruce

January 25, 2019

Blog_Data Doesn't Lie

In the visualization below, which line do you think represents the United Nation’s forecast for the number of children in the world in the year 2100?

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Supervised vs. Unsupervised Machine Learning

Gerhard Pilcher

January 18, 2019

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Analytics and Machine Learning techniques are important decision-making tools. Analytics asks questions from data and gets answers, largely for predicting what is likely to happen.  But, it’s easy to get confused by terminology such as supervised and unsupervised learning. What exactly is unsupervised learning? How does it differ from supervised learning, and why are these terms important? 

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Why Data Literacy in the C-Suite Matters

Will Goodrum, Ph.D.

December 21, 2018

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In one recent week, I heard about the need for increased “data literacy” from executives at a major insurance company, leaders at a large consumer packaged goods manufacturer, and senior administrators at a preeminent research university. All these leaders understand the need for a comprehensive data strategy and want to create one for their organizations. Yet, they are finding it difficult to effectively communicating their strategy to employees who are not used to working in a data-driven environment, and who don’t understand how to implement that strategy in their day-to-day work.

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A Strategic Guide to Developing Your Analytics Team

Ozan Ersoy

December 14, 2018

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Good data scientists are hard to find! This is in spite of the rapid growth of University master’s degree programs and commercial boot camps. Those sources do produce a much-needed supply for the data scientist demand projected over the next decade. However, becoming a good data scientist takes more than what these programs can provide. 

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How to Automate Machine Learning Model Tuning

Trent Bradberry

December 7, 2018

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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.

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Top 3 Lessons Learned While Drinking from the Data Science Firehose

Sam Ballerini

November 30, 2018

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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.

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Monte Carlo Simulation - a Venerable History

Peter Bruce

November 16, 2018

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

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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.

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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.

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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.

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Finding Fraud When No Cases are Known

Carlos Blancarte

October 12, 2018

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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?

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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. 

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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.

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