What is the difference between a data scientist and data analyst?

CompTIA, an industry-respected information technology certification and training organization, predicted that in 2023, data scientists and data analysts would grow at a rate of 5.5%—faster than other expanding jobs in fields like cybersecurity and software development.

If that wasn’t enough of a sell, CompTIA’s State of the Tech Workforce report also predicts that over the next decade, employment of data scientists and data analysts is expected to grow at a rate of 266%.

Clearly, these data-related occupations are in-demand. However, for those interested in possibly pursuing a career as a data scientist or data analyst, a common question arises: what is the difference between the two? Fortune has you covered.

What is a data scientist?

A data scientist’s day-to-day schedule may differ depending on experience level and industry of employment (with the good news being that data scientists are being hired across many fields, from tech companies and consulting firms to government agencies and healthcare systems). 

But put simply, data scientists work with programming and algorithmic tools to make future predictions. 

“A common task a data scientist will do is using historical data to make a prediction about the future, while also adding parameters to predict how a change might change future sales,” says Wade Fagen-Ulmschneider, a teaching associate professor of computer science at the University of Illinois.

For example, an e-commerce story may run a “A/B test” on different signs of emails, webpages, or discounts to see how purchase likelihood is affected by the various factors, Fagen-Ulmschneider says.

Data scientists on average earn six-figure salaries. Data from the U.S. Bureau of Labor Statistics reports they earn $103,500 on average. Dice predicts a higher number of $117,241.

What is a data analyst?

Data analysts use well-established tools and processes to organize and present data; they may locate historical trends and visualize them with charts and graphs. Their data collection and interpretation plays a major role in business decision making. 

Experts in the field work may work heavily with tools relating to data mining. They may also be familiar with spreadsheet software like Google Sheets and Microsoft Excel. For data visualization, Tableau and Datawrapper are common. Being familiar with programming languages like SQL, R, and/or Python may be beneficial. 

Dice estimates data analysts earn about $81,000 in annual salary. 

How do data scientists and data analysts compare?

Data scientists and data analysts are admittedly very similar roles. If you have experience with—or were educated on—one field, it may be relatively simple to effectively tackle the other role. 

Both tend to be experts in areas of statistics, mathematics, and computer science. Though, data scientists may be more equipped to predict advanced statistical or computational outcomes and be more knowledgeable of AI and machine learning. Data analysts, on the other hand, may know how to best express trends.

If salary is an important factor, data scientists do tend to make more, with the average being over six figures. However, compensation depends heavily on one’s experience level, educational background, and industry of employment. 

Overall, both data science and data analyst professionals are likely to be effective communications, collaborators, and problem solvers. Because data continues to be an ubiquitous part of society, having experts who are well equipped to harness, analyze, and even predict data trends is likely to continue to be of the utmost importance.