What is the difference between data science and data analytics?

Occupations that deal with data are projected to have “strong” job growth by 2031, according to the U.S. Bureau of Labor Statistics. For context, on average all occupations are expected to grow by 5%. Data scientists, as just one example of data-related occupations, are growing by over seven times that amount—at 36%. 

Moreover, data-related occupations had a median annual wage above the median for all occupations in 2022, and data scientists in particular make double the median wage in the U.S.

The terms data science and data scientists have only been popularized within the past decade, according to Wade Fagen-Ulmschneider, a teaching professor of computer science at the University of Illinois. Data analytics, on the other hand, has been around for longer and is a field that many students of statistics, economics, and even some social sciences end up pursing. 

With the two areas often discussed in conjunction with one another, you may be wondering, what’s the real difference between data science and data analytics? Fortune is here to help.

What is data science?

Broadly speaking, data science is the study of using and applying data to solve real-world problems. It encompasses multiple areas, including AI machine learning, and algorithms and intersects closely with subjects like computer science, statistics, and business. It can also encompass data analytics itself.

“Data science is typically about estimation of unknown phenomena and prediction of future events,” Joel Shapiro, clinical associate professor of managerial economics and decision sciences at Northwestern University’s Kellogg School of Management tells Fortune. “Data science can include capturing and managing data, building algorithms, and articulating the implications of results.”

Fagen-Ulmschneider previously told Fortune that he believes data science skills will soon be as ubiquitous as knowing Microsoft Office skills.

What is data analytics?

Instead of looking at the future, data analytics focuses more on the past—as well as the now.

Data analytics uses historical data to identify trends and articulate the implications of those trends, Shapiro says. Experts in the field also tend to be adept at data visualization techniques.

The field is important, Shapiro adds, because it helps uncover the stories that otherwise may not be seen or found.

“There are so many things that can be measured, and it is impossible for any person to track all of them, let alone really understand how they relate to one another. Those trends and relationships enable us to understand and synthesize past events, which then can be used to make future-looking decisions,” Shapiro says.

How do data science and data analytics compare?

There’s no question that data science and data analytics are inherently similar. And from a business perspective, both can be critical components to decision-making. 

In terms of skills, those working in data science and data analytics will likely be working as part of a team of experts, so having effective communication and collaboration skills are important. 

On a more technical side, Fagen-Ulmschneider notes that data science and data analytics will benefit from learning skills in statistics, mathematics, and computer science. For those particularly interested in data science, he suggests students lean heavily on computer science, and for students wanting to become a consultant should focus on statistics or even actuarial/finance. 

Shapiro goes further and notes that data science requires a deeper knowledge of things like statistics, machine learning, coding, experimentation, and predictive modeling. Data science, he adds, is better at the individualized level like customized customer experiences, optimized pricing, and differentiated messaging for digital users.

On the other hand, data analytics, Shapiro says, typically requires knowledge of basic data management, some statistics and data visualization techniques and technologies. 

So, if things like AI, machine learning, and predictive models excite you, focusing on data science may be for you, whereas if using data to identify and visualize trends, you may want to take a closer look at the analytical side.

Overall, though, data science or data analytics lean on each other, and many of the skills and expertise needed to succeed in either area are similar. Neither data science or data analytics are mutually exclusive, and both play a major role in solving the biggest problems in today’s world.