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Elder Research is a recognized leader in the science, practice, and technology of advanced analytics. Topics on our blog cover analytics tips, analytical modeling, data and text mining tools, data visualization, analytics best practices, case studies, etc. to provide business leaders with actionable information on real world analytics problems.
Related URL: Elder Research Analytics & Data Science Whitepapers

Download white papers focused on providing information on the practical application of advanced analytics to uncover actionable insight to solve real-world business problems.
Related URL: Case Studies - Elder Research

Elder Research predictive analytics and data science consultancy case studies available for download.
Related URL: eBooks - Elder Research

Elder Research predictive analytics and data science consultancy eBooks available for download.
Blog Added: March 14, 2017 04:52:18 PM
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Blog Platform: WordPress
Blog Country: United-States/Virginia   United-States/Virginia
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Total Visits: 1,768
Blog Rating: 3.20
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Credit Models are Winning and I’m Keeping Score!

Classification scorecards are a great way to predict things because the techniques used in the banking industry specialize in interpretability, predictive power, and ease of deployment. The banking industry has long used credit scoring to determine credit risk—the likelihood a particular loan will be paid back.  A scorecard is a common way of displaying the patterns found in a classification model—typically a logistic regression model. However, to be useful the results of the...

BLOG_Credit Models are Winning and I’m Keeping Score.jpg

Classification scorecards are a great way to predict things because the techniques used in the banking industry specialize in interpretability, predictive power, and ease of deployment. The banking industry has long used credit scoring to determine credit risk—the likelihood a particular loan will be paid back.  A scorecard is a common way of displaying the patterns found in a classification model—typically a logistic regression model. However, to be useful the results of the scorecard must be easy to interpret. The main goal of a credit score and scorecard is to provide a clear and intuitive way of presenting regression model results. This article briefly discusses what scorecard analysis is and how it can be applied to score almost anything.



Top Mistakes when Backtesting Investment Strategies

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...

BLOG_Top Mistakes when Backtesting Investment Strategies.jpg

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?”



Be a Data Detective

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,...

BLOG_Be a Data Detective.jpg

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.



Surf’s Up: Riding the Big Data Wave

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...

BLOG_Surf’s Up-Riding the Data Wave-1.jpg

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?



Fraud Analytics: Tech Can Make Fraud Detection Affordable for SMEs

Fraud 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...

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.



Picking Favorites: A Brief Introduction to Selection Bias

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...

BLOG_Picking Favorites-A Brief Introduction to Selection Bias-1.jpg

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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