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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Retailing Nears Holy Grail In 'Big Data', Jennifer Priestley
Retailing Nears Holy Grail In 'Big Data', Jennifer Priestley
Jennifer L. Priestley
(First paragraph) Yesterday I was online looking for a red, cotton wrap skirt, size 6 (OK, maybe size 8). After viewing several different retail sites, clicking through countless options, I found the perfect skirt. But I had to “abandon” my cart to take care of a minor household crisis. When I went back online, it seemed as if every ad included size 6 women, wearing red wrap skirts. Even more interesting, most came with an incentive for free shipping or 10 percent off.
Big Data Education: 3 Steps Universities Must Take, Jennifer Priestley
Big Data Education: 3 Steps Universities Must Take, Jennifer Priestley
Jennifer L. Priestley
By now, we all know that the "sexiest job of the 21st century" is the data scientist. A scan of articles and blogs describing data scientists and their raw material -- big data -- reveals several "sexy" themes. First, data is ubiquitous, big and coming at us with increasing velocity. Second, traditional tools that have been used to extract and analyze 20th century data don't work with big data. Third, incredibly few people have the skills necessary to translate this tsunami of data into meaningful information -- making them the hotshots in the job market.
Let's Come Together On Data Science, Jennifer Priestley
Let's Come Together On Data Science, Jennifer Priestley
Jennifer L. Priestley
We've all read the articles and blogs. Many of us have experienced the issues directly -- the demand for deep analytical skills is outpacing the supply. As evidence of this, in a period of economic slowdown, where we read that 50 percent of college graduates can't get a job, college graduates with degrees remotely aligned with applied analytics have multiple offers in advance of graduation. Academic training in applied (versus theoretical) statistics is helpful -- and mitigates some of this talent gap at the entry level. Nonetheless, we all know it's insufficient to meet the growing demand for what we …
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Jennifer L. Priestley
In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs …