Jennifer Lewis Priestley is a professor of applied statistics and data science at Kennesaw State, where she is also the director of the center for statistics and analytical services. She was recognized by the SAS Institute as the 2012 distinguished statistics professor of the year and regularly speaks at data and analytics conferences. She has authored many published articles on binary classification, sampling and application of statistical methodologies for problem solving, as well as several textbook manuals for Excel, SPSS, SAS and Minitab.
Prior to receiving a Ph.D. in statistics, Priestley worked in the financial services industry for 11 years. Her positions included vice president of business development for VISA EU; vice president for business development for MasterCard International; and additional positions with AT&T Universal Card and Andersen Consulting.
Priestley received an MBA from The Pennsylvania State University and a B.S. from Georgia Tech. She also received a certification from the ABA Bankcard School and a certification in Base SAS Programming and a Business Analyst Certification from the SAS Institute.
Industry Expertise (2)
Areas of Expertise (9)
Datanami's People to Watch in 2016 (professional)
Only female named to Datanami's inaugural selection of the best and brightest minds in Big Data.
2015 National Analytics Conference Chair (professional)
International conference presented by The SAS Institute
Distinguished Statistics Professor of the Year (professional)
Awarded by the SAS Institute.
Georgia State University: Ph.D., Decision Sciences 2004
ABA Bank School: Certificate, with Honors, Decision Sciences 1991
Pennsylvania State University: MBA., Quantitative Business Analysis 1989
Georgia Institute of Technology: B.S., Economics
Media Appearances (14)
Interview with Jennifer Priestley, KSU Professor of Statistics and Data Science
Pro Business Channel - Capital Club Radio
Dr. Priestley is a Professor of Applied Statistics and Data Science at Kennesaw State University, where she is the Director of the Center for Statistics and Analytical Services. . She oversees the Ph.D. Program in Advanced Analytics and Data Science.
How a Ph.D. in data science can fix the talent gap
Tech Republic online
There's no denying the exponential growth of data science as both a field of study and career choice. In 2012, the Harvard Business Review went as far as to call the role of data scientist "the sexiest job of the 21st century."
While there's been much said and written about the actual staying power of data science, it's difficult to deny the current buzz. At the time of this writing, there are roughly 30,000 job postings on LinkedIn and about 22,000 postings on Indeed for "data scientist."
Preparing for Big Data Careers
SAS Analytics U Blog online
Numerous studies and statistics point to the fact that in just a few short years the need for people with analytics skills could significantly outpace supply.
With so much talk around the analytics skills gap and the growing market for analytic talent, we wanted to highlight a variety of avenues students and learners of all ages can explore to prepare for big data jobs. This blog series features interviews with professors, department leads and other educators who are seeding the market with analytical talent and directly impacting the talent management pipeline in this area.
Exclusive – Jennifer Lewis Priestley Talks Analytics and Kennesaw State University – Part 1
icrunchdata News online
icrunchdata News speaks with business leaders about the progression of their careers in analytics, what they are focusing on in their current roles and their interests outside of data. We recently spoke to Jennifer Lewis Priestley about her role and the progressive Ph.D. program in Analytics and Data Science at Kennesaw State University.
Data Science: The Evolution or the Extinction of Statistics?
AMSTAT News online
As a discipline, I think statistics—and by association statisticians—are going through a midlife crisis. Just look around a typical university. Where is statistics housed? Mathematics? The business school? Engineering? Humanities? All of the above? Who are we? This crisis of identity has been accelerated by this new term “data science.” Is it a discipline? Is it an application of statistics? Is it an application of computer science? Is it a buzz word just having its moment?
I agree with Tommy Jones in his Amstat News article, “The Identity of Statistics in Data Science,” when he says the “… conversation around data science betrays an anxiety about our identity.”
Payday lending proposal squeezes low-income earners
Las Vegas Review-Journal
Another recently released academic study from Jennifer Lewis Priestley, a professor at Kennesaw State University, examined the impact a high number of payday loan rollovers had on borrowers’ credit scores. She found that borrowers with a high number of rollovers actually saw more positive impact on their credit than consumers with few rollovers. Borrowers who did experience a decline in credit scores were more likely to live in states that have laws restricting access to payday loans...
Mac users might pay more when shopping online
"Some are more sophisticated than others so if you go and shop at a particular retailer and you're seeing the exact same price no matter what piece of hardware, no matter what browser you use, they're probably a little less sophisticated. They haven't quite gotten to the point where they can customize those models," said Jennifer Lewis Priestley, Ph.D., professor of applied statistics and data science at Kennesaw State University...
Closing the Analytics Talent Gap
We spoke with Dr. Priestley about the country’s current (and growing) analytical talent gap and the rise of data science programs working to fill the void. In the interview, Dr. Priestley discussed the cause of the talent gap, what students should look for when exploring data science degree programs, and what schools can do to best prepare data science students to solve real-world problems.
Kennesaw State University
In 2006, professor Jennifer Priestley and her colleagues at Georgia's Kennesaw State University were charged with building a new undergraduate program in statistics. What they created is an applied statistics program that incorporates real data from large companies like CompuCredit, Southern Co. and SAS Institute...
Society’s demand for ‘big data’ creating shortage of skilled workers
Saporta Report online
Big Data has created a big employment problem for metro Atlanta – there are simply too many jobs in data science and not enough people. And the gap between supply and demand is getting bigger. Universities in metro Atlanta are filling that void, helping both employers and those who want to obtain those jobs.
A day does not go by that we don’t hear of, or read a news story related to, the topic of data. It seems that everyone is collecting data – everything from our Facebook posts to our energy consumption to the books we read. The data we generate, which someone else collects, has become a pervasive characteristic of our society.
As Data Proliferate, So Do Data-Related Graduate Programs
The Chronicle of Higher Education
Established programs are seeing their share of the action. When Kennesaw State University started its master’s program in applied statistics, in 2006, it attracted fewer than 20 students and was an “island of misfit toys,” says Jennifer Lewis Priestley, an associate professor of applied statistics. Today she and her colleagues receive as many as five applications for every slot. The program has a 100-percent job-placement rate, with salaries starting around $75,000, she says...
Data Science Doesn't Belong in Business Schools
Information Week online
For the last few years, major universities around the country have debated the issue of "data science" as a unique discipline. Specifically, the debate has centered around two positions: Either 1) universities just continue to teach the foundational topics of mathematics, statistics, and computer programming as they always have, and then leave the graduates to learn the rest on the job; or 2) data science is emerging as a unique discipline that deserves its own unique curriculum, and students should be allowed to obtain degrees in data science (and DS-esque areas like predictive analytics).
3 Key Ingredients to a Data Science Degree
However, in the long term, universities need to produce graduates who can fill these positions, said Jennifer Lewis Priestley, an associate professor of statistics and director of the Center for Statistics and Analytical Services at Kennesaw State University. She recommends three key ingredients to a data science degree and shares the reasons why no one's put them together...
Someone You Should Know
All Analytics online
Looking for a job in academics wasn't originally part of Priestley's grand plan. But after 11 years in the business world, mostly working for or in consumer credit card operations, she said she was ready for a change. For the last few years of that career, Priestley had been living in London but was constantly on the road between there, Edinburgh, and San Francisco. Back home in Georgia, she decided to get a PhD, and she chose statistics as her course of study. (She already had an MBA.)
Event Appearances (1)
Unicorn Farms: What You Don’t Know About Data Science Education in Atlanta
Data Science Education - KSU, Emory and Georgia Tech Perspectives Atlanta, Georgia
Recent Papers (7)
Using payday-lender administrative data matched to borrower credit attributes from a national credit bureau, I find that borrowers who engage in protracted refinancing (“rollover”) activity have better financial outcomes (measured by changes in credit scores) than consumers whose borrowing is limited to shorter periods. These results are robust to an alternative definition of a “rollover” that ignores out-of-debt periods of 14 days between successive loans. Also, exploiting interstate differences in rollover regulation, I find that, while regulation has a small effect on longer-term usage patterns, consumers whose borrowing is less restricted by regulation fare better than consumers in the most restrictive states, controlling for initial financial condition. These findings directly contradict key assumptions about this market, raise significant policy questions for federal regulators, and suggest the appropriateness of further study of actual consumer outcomes before the imposition of new regulatory rollover restrictions.
Most cross-cultural studies are sociologically based and assume intra-cultural homogeneity in mentality and logic among people. The application of cultural dimensions in many cross-cultural studies has inadvertently contributed to this oversight. Scores on these dimensions are supposed to indicate characteristics of national cultures. The apparent characteristics of cultures are extended to individuals as well. On that basis, we assume that all Americans are individualistic, ignoring those who might have more collectivist mentality and logic. Although some researchers have recognized the existence and importance of heterogeneity within cultures, these issues have not been fully addressed. Experience at the international level and research evidence indicate such a variation and heterogeneity. This research, conducted in four different countries, confirms the existence of individual heterogeneity in and among them.
Increasingly, knowledge is recognized as a critical asset, where a firm or an individual's competitive advantage flows from a unique knowledge base. The subsequent degree to which knowledge is then recognized and valued as a resource has been the theme of ...
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 ofNeural Network modeling. KS Tests and Classification Rates are ...
While most articles dealing with the issue of inter-organizational knowledge transfer take the position that some type of network alliance is superior to independent entities for the purposes of knowledge transfer, limited theory, or empirical research, addresses how ...
In this paper, we examine three common modeling techniques–Linear Discriminant Analysis, Logistic Regression Analysis and Neural Networks. Although a well established literature exists examining the relative performances of these techniques, ...
Organizations join multi-organizational networks, in part, to increase their access to both new and existing knowledge. However, knowledge access does not equate to knowledge transfer. In addition to the factors of absorptive capacity and causal ambiguity, ...