Data science is one of the fastest-growing fields in the worlds of business and academia. In fact, data scientist is the third fastest growing occupation in the country, according to the U.S. Bureau of Labor Statistics.

Universities are tackling the need head-on by establishing new data science institutes and degree programs. For example, the University of Florida recently opened a $150 million building dedicated in part to the study of the field.

For those outside of tech, understanding what data science is all about can be challenging. But with the field involving in-demand skills subjects like Python, machine learning, and large language models, Fortune is here to help you discern what data science really means.

What is data science?

Data science is the study and use of data to solve real-world problems. From identifying disease trends and analyzing climate patterns to digesting athlete performance and social media advertisements, data science can be used across disciplines to generate insights for businesses and the world as a whole. 

Chris Wiggins is an associate professor of applied mathematics at Columbia University. He says that while data science has been around for a number of years, interest has grown hand-in-hand with its growing impact.

“Over the last five years, the change has been not so much that people have discovered that data-empowered algorithms exist, but that people have been really wanting to engage with how these algorithms impact society, and so it’s not just a concern for people who write code, it’s a concern for everybody because data-empowered algorithms are impacting everyone’s professional, political, and personal realities,” Wiggins says.

Wade Fagen-Ulmschneider, a teaching associate professor of computer science at the University of Illinois, sees data science as a big umbrella term that encompasses all things AI, machine learning, and algorithms at the intersection of computation, statistics, and basic business intelligence analysis.

“Data science is really combining everything,” Fagen-Ulmschneider tells Fortune. “So you’re actually making the world hopefully a better place by doing computational computations and analysis to actually solve real-world problems.”

Fagen-Ulmschneider teaches several data science courses at UIUC and has seen student interest grow exponentially. In fact, one of his classes has grown by a factor of 100x in just four years—thanks in part to the rise in interest of generative AI tools like ChatGPT.

“I see that data science is now more like what (Microsoft) Office was 20 years ago—where people knew that Excel and Word were really important tools in business and colleges started offering courses on how to use business tools. And I think AI is that next generation—that 10 years from now, you don’t want to walk away from college without knowing how to do basic data science,” Fagen-Ulmschneider says.

A basic data science course will often introduce students to elements and applications of machine learning, large language models, and Python as well as their interactions with ethics and privacy.

Where can you learn data science?

At many universities, data science programs are becoming a core offering. Dozens of top schools now have undergraduate degrees as well as master’s programs. And as data science becomes more of a norm, so likely will the interdisciplinary nature of the learning, especially for fields like business, finance, and information science.

It’s worth noting that Fortune ranks the best in-person master’s in data science as well as online master’s in data science.

If a traditional degree pathway is not best for you, bootcamps and certification programs are other ways to get ahead—especially for those with families or are looking to upskill. Fortune also ranks the best data science and analytics bootcamps.

But what’s clear is that learning data science is likely to only help your future career.

“I think if you’re a student, I think there’s no better way than starting to use data science (on) day one of your education in your field,” Fagen-Ulmschneider says. “There’s always going to be more value in having a blended experience—like doing a data science course in parallel with doing a basic accounting course.”

In 10 years, Fagen-Ulmschneider imagines the importance of having foundational knowledge of data skills will be critical for all students—the likes of engineering, humanities, and especially business.

“I just see it as a no-brainer,” he says. “There’s no universe where you’re not having data science tools helping you make business decisions in 10 years.”