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Elder Research Data Science and Predictive Analytics Blog

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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.

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Website URL: http://www.elderresearch.com/company/blog

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Website Tags: analytics - data analysis - data science - data visualization - big data - analytics strategy - predictive analytics - data modeling

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Download white papers focused on providing information on the practical application of advanced analytics to uncover actionable insight to solve real-world business problems.

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

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

    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.



    Top 3 Objectives Before Starting an Analytics Project

    Understanding 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...

    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.



    Machine Learning: It is a Mistake to Lack Relevant Data

    In 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 #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...

    BLOG_It's a Mistake to Lack Relevant DataIn 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 #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.



    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...

    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?



    Hiring a Data Analytics Consultant

    In 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. ...

    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. 



    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....

    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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