Exclusive - Jennifer Lewis Priestley Talks Analytics and Kennesaw State University - Part 2

Exclusive - Jennifer Lewis Priestley Talks Analytics and Kennesaw State University - Part 2

Jennifer Lewis Priestley is Professor of Statistics and Data Science at Kennesaw State University.

icrunchdata 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.

Thanks for speaking to us in Part 1, Jennifer. Let's get started with Part 2...

What is the balance between awareness of concepts and practical experience working with real business data? And how does your program work with companies to help tackle analytics issues?

This is a great question… and I think this gets to the heart of several issues simultaneously.

From our (academic) perspective, we have to have real business data to do what we do. This is true for two reasons. First, “theoretical” data science makes no sense. Data Science is a science of application. So while there is a theoretical dimension, working with real world data has to form the core of any Data Science curriculum.

Second, and I think this point gets lost on a lot of academics – data science is in its infancy. There are no academicians today in any university who have a Ph.D. in Data Science. We at Kennesaw State are trying to change that. But at present, there is a misalignment of the needs of the market for research, for new techniques and most notably for new talent in Data Science and the infrastructure in academia to support these needs. Until academia catches up, much of the discovery and invention in Data Science is coming from the private sector. Bringing skilled, knowledgeable practitioners and their data… along with their challenges and issues… into the classroom is an invaluable asset to any Data Science curriculum. It helps the professors better understand the challenges “on the ground” and shape their curricula in response.

From the practitioner’s perspective, integrating with a local university connects them with a “farm system” for new talent. Almost every organization across almost every sector of the economy has the need to translate massive amounts of structured and unstructured data into information to improve the decision making process. Finding talent in the open market is expensive. But “growing” the talent in partnership with a local university is substantively less expensive in the short and long term. It’s also an investment in the human capital of the local economy.

I believe that the business/university partnership is more critical to the evolution of the discipline of Data Science than likely to any other academic discipline in history.

In our own university, students at the undergraduate, MS and Ph.D. levels are all required to complete applied projects with real data. At the MS and Ph.D. levels they are required to work with real clients to satisfy the requirements of their degree.

What skills do you consider paramount for data science students to be equipped?

Data Scientists are not computer scientists, but they know how to program. Data Scientists are not statisticians, but they can build models, test hypotheses and create visualizations. Data Scientists are not mathematicians, but they can translate unstructured problems into mathematical expressions. Data Scientists are not marketers, but they can “tell the story” of their results.

To achieve this nexus of skills, our data science curriculum incorporates Machine Learning, Neural Networks, Natural Language Processing and Programming (SAS, R, SQL, Python, Spark, Hadoop) from Computer Science… Data Mining, Modeling, Visualization from Statistics… Graph Theory, Algorithm Design, Combinatorics from Mathematics.

What advice do you have for a potential student exploring data science programs?

First, I would like to know their background. Have they studied computer science and/or statistics and they are looking to fine tune their skills? Or are they currently a poet and looking to make a fundamental transition in their career? If they are in the first group, they might be well served by looking into some of the stronger online programs (but they need to do their research, because there is a lot of variation in the online space). If they are in the second group, I would suggest that they pick up a programming course and a statistics course…and then look into one of the “traditional” (not online) programs with an established track record of strong placement with an integration with the business community, where there are opportunities for projects/internships/co-ops. That is where much of the “real” learning takes place. Perhaps talk to a few HR professionals in the local market and ask where they recruit from. My opinion is that an online program is not going to take you from being a poet to a Data Scientist.

What has been a specific challenge for the program?

I find that I am frequently challenged by some colleagues in the academic community on two aspects of this program. The first is the central question of “Is data science a unique discipline?” I have engaged in lively conversations with some incredibly intelligent people who believe the answer to that question is “No”… that Data Science is simply an application of Computer Science… or Engineering… or Applied Statistics. It’s not a unique discipline, and therefore, does not justify a Ph.D. level degree (but using that logic, aren’t almost all disciplines just an application of mathematics?).

The second challenge that I get is aligned with the concept of an applied Ph.D. ”Traditional” academics really struggle with this one. The reality is that individuals with Ph.D.s are entering the private sector in record numbers. The degree is no longer the “union card” to become a professor. People are pursuing the degree for a wide variety of reasons that are indicative of a changing economy. The private sector heavily engages in research and development in the areas of Big Data and Advanced Analytics. Every major consultancy has a section on their website for white papers and publications.

Describe the future of the data marketplace in one word.


Any downsides of our digital, data-driven world?

Professionally, data drives everything I do. I live eat breathe sleep data. As the parent of an 11-year-old and a 13-year-old, I actually try to restrict their constant digital immersion. It’s not easy. We have a basket in our kitchen where we keep all of our mobile electronic devices (including mine and my husband’s). Periodically we have “electronics-free” periods where we go out as a family and walk to the park or we go to the mountains or the river for the day. I have to work to get my kids detached from data.

It makes me very aware that their long-term integration and connectedness with their friends, family, community will be very different from mine. I don’t know that I would consider this to be a “downside,” but the generation coming up behind us will be the first “digitally native” generation in history. This will shape their physical connectedness in new ways – creating new opportunities for dialogue nationally and globally. It’s interesting to consider the world of my grandchildren and great grandchildren as we have second and third generations of “digital natives.” I just hope we don’t forget how to talk to each other.

Have you encountered any knowledge recently that's shaping the courses taught?

Yes. In addition to the technical skills, it’s becoming evident that students in Data Science must have a course in Law and Ethics. Specifically, these students will always have the skills to “outpace” the regulators and the legislators. But there is a big difference between what they can do and what they should do; what their company (or country of operation) will allow and what their customers/clients will tolerate. There is a thin line that separates “insightful,” “creepy” and “illegal.” The legal dimensions as well as the ethical dimensions of Data Science need to be as integral to any curriculum as SQL and Data Mining.

If tomorrow someone asked you to start a business that is unrelated to data and tech, what would you create?

If I did not need any source of income, I would become a volunteer with Statistics without Borders and travel to remotes parts of the world helping to collect data on poverty, public health, infant mortality, access to education, etc.

What are your top three favorite go to bookmarks or apps?

I am an avid podcast listener. Every day I go to my HowStuffWorks app. My favorite is StuffYouShouldKnow with Josh and Chuck. After that, I enjoy playing 2048 (my high score is 34,520). I also listen to Pandora almost every day – my stations are Indie Pop and the Stone Roses (with a little bit of the Ramones thrown in).

What do you enjoy most in your free time, what really makes you happy?

I live in a very pedestrian-friendly neighborhood in Midtown Atlanta. One of the things I like best is waking up early on the weekends and walking up to the main street and going to the local coffee shop and getting a coffee and two cheddar biscuits with melted butter… and reading the paper.

Learn more about Kennesaw's Ph.D. in Analytics and Data Science >

Thanks for letting us all get to know you, Jennifer. You're a remarkable professor, person and champion for Kennesaw State University.

Article published by icrunchdata
Image credit by Kennesaw State University
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