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Top Mistakes when Backtesting Investment Strategies

John Elder, Ph.D.

February 16, 2018

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A market index provides a tough hurdle to beat for any investment strategy. Employing an index is almost always better than a strategy that systematically picks a subset of its space or time (i.e., does portfolio-picking, or market timing ). The cost of a predetermined index is low, since no thought is required, and the long-term results over the last century of major market indices have been impressive.  So, the argument goes:  “Why waste your money paying for expensive managers who may only beat the market by luck?”

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Be a Data Detective

Ryan McGibony

February 9, 2018

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You’ve probably heard it before – analytics professionals working directly with data spend as much as 80% of their time on data preparation, leaving only 20% for actual analytics and modeling. There are several common terms for the activities making up this 80%, including data “cleaning,” “wrangling,” or “munging,” with perhaps the highest-profile example being “data janitor work,” as discussed in The New York Times. The consensus seems to be that this work is undesirable, a necessary evil we must endure to get to the “cool” parts of data science. The practitioners quoted in the Times article lament the countless hours they pour into data prep, and the author entices the reader with the possibility of automating the process. While anyone who works in predictive analytics would welcome the chance to cut down on prep work, we should consider the downsides of adopting this attitude in the practice of data science.

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Surf’s Up: Riding the Big Data Wave

Will Goodrum, Ph.D.

February 2, 2018

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Surfing requires a combination of skill, balance, strength, and awareness. A surfer only has so much control over where they are headed. It’s less about a specific destination, and more about catching the wave and seeing where it takes you.

Solving problems with data is (surprisingly) a lot like surfing. If the data and problem goal do not match, it is like trying to point a surfboard straight toward the shore — it won’t likely take you where you want to go. So, like deciding which wave to ride, how do you know if you’ve picked the right problem?

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Fraud Analytics: Tech Can Make Fraud Detection Affordable for SMEs

Harpreet Singh Dua

January 26, 2018

BLOG_Fraud Analytics-Tech Can Make Fraud Detection Affordable for SMEs-1.jpgFraud analytics is an emerging tool of the 21st century as it relates to detecting anomalies, red flags, and patterns within voluminous amounts of big data, which is quite challenging to analyze. The use of fraud analytics does not always have to be complex and costly for small and medium-sized enterprises (SMEs) to afford. While technology has played a key role in increasing opportunities to commit fraud, the good news is that it can also play a major role in developing new methods and strategies that can be used to detect and prevent fraud.

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Picking Favorites: A Brief Introduction to Selection Bias

Will Goodrum, Ph.D.

January 19, 2018

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In this series of short blog posts, we explore common biases that beset analytics projects. Bias can seriously impair the success of analytics in an organization, so understanding what to watch for is crucial. In this second post, we discuss a manifestation of one of the most prevalent and significant kinds of statistical biases: selection bias. We describe what it is, how it pervasive it may be, some specific examples of how it may manifest, and how to mitigate it.

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Why Every Business Needs Fraud Analytics

Paul Derstine

January 12, 2018

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From pharmaceutical, healthcare and financial claims, to insurance and product warranty claims, identifying and monitoring fraud is a priority for many organizations. Not only is a monetary loss at stake, but there is also potential damage to a company's brand, reputation, and trust. However, it can be a challenge for companies to understand and stay ahead of ever-evolving fraud risks and proactively identify and investigate active threats—let alone to navigate the new world of data analytics to assist in the process.

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Jump Start your Modeling with Random Forests

Evan Elg

January 5, 2018

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For a new data scientist, the first real project can be challenging. Real-world engagements aren’t as cleanly set up as academic assignments!  One usually experiences many pitfalls but can learn many valuable lessons. I’ve found that a sensible choice of modeling method can help alleviate many headaches, and strongly recommend considering Random Forests.

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Ensembles & Regularization – Analytics Super Heros

Jordan Barr, Ph.D.

December 15, 2017

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Ensemble algorithms and regularization techniques lie at the heart of many predictive analytics and forecasting projects. When should one be used in favor of the other?  Which technique wins -- ensembles or regularization?

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EBook: Leading a Data Analytics Initiative

Eric Siegel

December 8, 2017


Our latest EBook draws from Mining Your Own Business, A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics  written by industry experts Jeff Deal and Gerhard Pilcher.  The EBook includes Chapter 3 - Leading a Data Analytics Initiative which covers the key challenges and considerations for business leaders employing analytics to provide data-driven insight.

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Building Bridges: A Framework for Navigating Resistance To Analytics Results

Will Goodrum, Ph.D.

December 1, 2017

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Deploying advanced analytics is a transformative process in any organization. Sometimes, new findings upend long-held beliefs and disrupt established business processes. This can engender a hostile reaction to the changes introduced by advanced analytics. When deep-held worldviews are threatened, emotional responses to defend the status quo are to be expected, even though they run counter to the facts. Such emotional reactions by stakeholders may block the successful adoption of analytics throughout the organization. How can analytics practitioners successfully press the case for change when emotions trump facts?

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Changing the Curve: Women in Computing

Jennifer Dutcher

November 24, 2017

BLOG_Women in Computing.jpgWhat do the first computer programmer, the patent holder for spread spectrum wireless communications, and the author of the first assembly language have in common? All were women, as are 34 percent of today’s web developers and 23 percent of programmers.

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Analytics Help Identify the Early Stages of a Stroke

Nathan French

November 17, 2017

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In many medical emergencies, such as a stroke, survivability requires fast diagnosis and treatment. But diagnosis may depend on a test that uses bulky, expensive equipment, such as the radiological imaging test that serves as a “gold standard” stroke test. That test is impractical in the field though, so a reliable portable test would be of great value. Data science offers a solution. Through the information embedded in a biological quantity known as gene expression, a data model can efficiently classify whether a patient is currently undergoing a stroke. This blog will discuss, specifically, the use of k-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) to isolate a small number of genes whose combined expression levels might indicate a stroke is in progress. This can provide an alternative way to identify stroke victims, with lower equipment requirements than traditional radiological imaging. 

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Predictive Maintenance Optimizes Gas Well Production

Mike Thurber

November 13, 2017


To offset the fluctuations in the cost of oil, the oil and gas industry looks for improved efficiencies in all parts of its production chain. Predictive analytics leverages the large volumes and variety of historical well data to find critical patterns to improve performance, reduce losses, enable operators to be more proactive in field operations, and reduce operational costs.

This article describes how sensor analytics and predictive maintenance helped to prioritize gas well intervention to reduce downhole freeze events and reduce remediation cost.  

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COPS: A Data-driven Solution for Pharmacy Fraud Detection

Isaiah Goodall

November 3, 2017

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Prescription drug fraud is a costly problem for health insurance providers, but identifying perpetrators can be extremely difficult. Staying ahead of ever-evolving fraud risks and proactively identifying and investigating active threats can be a challenge for insurance providers, Pharmacy Benefit Managers, Managed Care Organizations, and regional pharmacies. The many different actors and schemes involved, varying state regulations and oversight, and compliance with privacy laws all contribute to the challenge of detecting and preventing prescription drug fraud.

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Made-to-Measure Analytics in the Automation Age

Will Goodrum, Ph.D.

October 27, 2017


In recent months, Artificial Intelligence (AI) has leapt off the pages of Science Fiction and into the headlines. The Economist  focused an entire Technology Quarterly on the dramatic advances made possible through Machine Learning. Recent articles by Tom Davenport in Harvard Business Review and Deloitte have pushed moving beyond the “artisanal,” human-driven analytics of the past toward a bountiful, automated future. With talented Data Scientists scarce but vital, the value proposition for AI seems to be clear. So, should companies hire Data Scientists (especially consultants) if the computer can do all the work?

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