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April 30 - May 1, 2025
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The Data Talent Pipeline – Entry Points And Opportunities

Baby’s First Data Visualization

People who are passionate about data often remember fondly their first projects building data visualizations. The graphic associated with this article isn’t just seasonally appropriate, it was based on the first-month training materials from one of Salesforce’s top Tableau instructors, whose content is the foundation for academic and professional curriculums around the country teaching the next generation of data leaders. 

For nearly 15 years, University of Cincinnati Professor Jeffrey Schaffer has tracked the comings and goings of the spirits, aliens, superheroes and princesses that have rung his doorbell on Halloween and presented the data to young people. As a teacher of data visualization at the college level, he is one of thousands teaching a new generation of data scientists, analysts and engineers who will be contributing to an economy that is increasingly data driven.  His work has also served as an exceptional public resource for young people to hit the ground running in the workforce.

And while data visualization jobs offer excellent opportunities for those who are data and design literate, the opportunities in data science are even greater. According to the U.S. Bureau of Labor Statistics, the number of people filling the role of “data scientist” is expected to grow by 35 percent by 2032 (compared to an average growth rate across all professions of three percent), with the typical entry-level position in the field requiring at least a bachelor’s degree.  Roughly 80 percent of those in the field have an advanced degree, the agency said.

So what higher education is available to young people interested in this quickly growing occupation? And, what can older people do who are mid-career?

Preparing Young People for Data Science

There are more than 1,000 colleges and universities offering online and in-person data science programs in the U.S. alone, according to Formal programs usually begin with classes in algebra, calculus, geometry, statistics, and computer programming. They serve as a foundation for advanced coursework in data science, which typically covers topics such as database systems, data mining and analytics, data structures and algorithms, data visualization, and machine learning.

A look at the U.S. News and World Report rankings for data science reveals the names of familiar institutions including MIT, Stanford, Carnegie-Mellon, Caltech and many of the Ivy League schools. But a growing number of more affordable state universities are offering elite undergraduate data science programs as well, including the Universities of Washington, Illinois, Texas, and Michigan along with Purdue, UC San Diego and UCLA.

As students are finding out across all areas of study, gaining admission to top-rated universities is more difficult than ever. As a bachelor’s degree has become table stakes for young people interested in careers in data science, however, the options to acquire those qualifications at an institution employers respect are becoming more numerous. According to Adam Ross Nelson, a career services provider and author in the data science space, the foundational learning available at many of the hundreds of institutions offering data science degrees is positioning students well for jobs and future educational advancement.

“Many, or even perhaps most, data science degree programs…seem to be providing students with a valuable and widely applicable education in data science,” he says.

So, bachelor’s degrees in data science are available to a critical mass of students and are providing young people the conceptual understanding they need to embark on one of the hottest career options available today.

The general field, however, is relatively new and rounding out the managerial and executive-level ranks could be difficult for businesses currently employing mid-career workers whose early education wasn’t specifically geared toward data science.

Advancing with Advanced Degrees?

While the educational path for young people interested in data science careers has become clear, what about older workers? There is an enormous population in the workforce who are past entry-level, have an interest or aptitude for working with data and want to take advantage of the growth in data-related jobs to advance their careers into the mid- and late career stage.

People at that stage have choices. Pursuing a master’s degree or PhD in data science is one of them. According to a Stitch Data study of current professionals, nearly 80 percent hold advanced degrees.

The list of top graduate programs in the space is more diverse than the undergraduate list, comprising more large public schools (e.g., Universities of Michigan, Minnesota and Maryland, along with Oklahoma State, Texas Tech and UC Irvine) but also smaller private institutions that aren’t household names including the University of St. Thomas (Minn.), Maryville University (Mo.), Worcester Polytechnic Institute (Mass.), DePaul University (Chicago) and Willamette University (Ore.), among others.

For mid-career workers interested in pivoting to data related fields, graduate programs can provide the foundational knowledge they didn’t get in their original course of study. It’s also valuable for entry-level workers transitioning to the next stage of their career who have an interest or an opportunity to work in a specialized area (e.g., business analytics, data science with a computational track, data engineering and warehousing, database management and architecture, data mining and statistical analysis or machine learning). Pursuing these degrees, however, comes at the significant cost of time and tuition—often difficult for mid-career professionals to juggle.

For older workers, there is an even more fundamental choice to make—one that may not require additional education at all.

“Data science manager is a key role, and perhaps a more lucrative one, in ensuring data is done the right way within an organization.”

Data may be your passion, one that you feel competent in and that want to make your career. But do you want to engage in the actual work of a data scientist? According to Jonathan Tesser, Head of Product Marketing for Lotame, data enthusiast, and a mentor for young professionals in the data field,  there is dire need for managers who can sit between data scientists and an organization’s business units and facilitate communication between them.

“If you have a math or computer science background and you want to move into data science, a good graduate program will help you,” Tesser says. “But unless you really love the technical aspects of creating algorithms, decision trees and doing advanced mathematics, you have a better option.”

By and large, Tesser believes, data scientists are not good at explaining their work to business stakeholders. People that do understand them, but have also spent time in their careers building the people skills necessary to manage teams will be in great demand. And, he says, there are fewer barriers to success on that career path.

“Pivoting to data science mid-career is tough. You’re competing against 22-year-old hungry dudes who have way more technical skills than you do. Oh, and by the way, they cost half the price,” he says. “Organizations need data translators who can take the work of those geniuses in the back room and make it understandable to folks in the business who, quite frankly, break out in hives at the mention of data science. Data science manager is a key role, and perhaps a more lucrative one, in ensuring data is done the right way within an organization.”

Trick and Treat

The trick-or-treaters who show up in a few weeks at Prof. Schaffer’s door and contribute to the growing dataset he uses to teach data visualization will follow many paths as they grow. For the foreseeable future, data science and other data-related fields will offer them some of the best opportunities available to budding adults.

Whether they choose data science from the start, pivot to data science mid-career, or simply use a passion for the field to lead data teams and integrate them into the business units of an organization, there is a higher education path that’s a good fit. The trick is finding it, the treat is a fulfilling career.