As one of the few women in the country running a Ph.D. Program in Analytics and Data Science, I am frequently asked about my career path. I received an undergraduate degree in Economics (Georgia Tech) and then an MBA (Penn State). I then worked for some great companies like MasterCard, VISA EU, and Accenture. By the age of 32, I had over a million frequent flyer miles and felt 20 years older. I decided to hang up my suits and my briefcase and pull out my blue jeans and my backpack and go back to school and get a Ph.D. This was 2000 – we did not know these terms “Data Science” or “Analytics,” but during my years as a working professional, I had really been a Data Scientist – I just didn’t know it. For over ten years, I had been taking messy data and translating that data into information through different modeling techniques and then “telling the story” of the results to internal and external clients. Incredibly no universities had formal programs to train students to do this. In the end, I pursued a Ph.D. in Decision Sciences at Georgia State University – it was a great program that straddled the Business School and the Math Department.
Eighteen years later I work with primarily masters and Ph.D. students who are engaged in thought leadership and cutting-edge research in Data Science. Our own program straddles the intersection of Mathematics, Statistics and Computer Science.
My own path was anything but linear. When I am asked, particularly by young women, how they should get started in pursuing a career in Data Science, I almost always frame my response in the context of one of my favorite quotes – “The truth is one but the paths are many” (Buddha). My point is that there are a lot of different ways to become analytics professional. While everyone needs to learn how to code, you do not have to be a computer science major. In our Ph.D. program, we have very successful students who studied Finance, Anthropology, Engineering, Epidemiology as well as Statistics and Computer Science. All domains and disciplines increasingly need people who can translate massive amounts of structured and unstructured data into information to improve decision making. Mathematics, Statistics, and Computer Science are part of the story, but domain expertise, as well as creativity, are important skills for people in analytics careers.
Our students – like many analytics and data science students across the country – seek out opportunities to work on data-centric challenges that benefit the local community. Currently, our students are working on a project with the local county fire department to reduce response time for first responders (picture attached). We also regularly have a local high school intern who works with our graduate students to learn more about analytics and data science.
I (half) joke that the best training to run a Ph.D. program is being a mother. I frequently find myself saying many of the things to these students that I say to my own children. One of the more common phrases that I have said of late is “Do not let your heart be troubled.” Things are rarely as bad as they seem at first…with time and perspective most problems take care of themselves.
Meet Jennifer Priestley: Academic Dean, Professor of Data Science, Analytics Story Teller, and Doting Mother.