What is data science?

JANUARY 9, 2023 — How much data do people generate? Forbes estimates humans create 2.5 quintillion bytes of new data every single day. That’s enough to fill about 3.7 billion CDs. Stack those discs on top of each other, and the pile would stretch nearly 3,000 miles, or the distance from New York City to Los Angeles.

Based on those stats, it is no surprise that the U.S. Bureau of Labor Statistics predicts careers in data science will grow as much as 36% over the next decade. But as UTSA prepares to open its own School of Data Science (SDS) at San Pedro I, the only school of its kind in the nation at a Hispanic Serving Institution, one question remains: Just what is data science?

Put simply, data science is the process of collecting, organizing, and analyzing data to gain knowledge and insight, says UTSA assistant professor of computer science and SDS core faculty member Kevin Desai.

“The idea is to use data and try to extract meaning out of it using algorithms and techniques which may involve artificial intelligence, machine learning, deep learning, et cetera,” Desai said.

“Data science touches on everything, so it’s easy for students to carry on with what they’re doing while adding on a new skillset in data science.”

One familiar example of data science at work is advertising and online shopping, says Rocky Slavin, also an assistant professor of computer science and SDS core faculty member.

“As we browse and purchase items online—or even in stores, thanks to services like Apple Pay and Google Pay—that data can be used to train machine learning models to predict what an arbitrary person may be interested in so it can make suggestions,” Slavin continued.

Basically, data science incorporates skills from statistics, computer science and other disciplines to extract knowledge from data sets that people can then use to make more informed decisions, explains Rebecca Schroeder, UTSA associate professor of instruction, assistant dean of University College and SDS core faculty member.

“Data science is the overarching field that combines these different techniques,” she said. “You’re using statistics, you’re using programming, you’re using domain knowledge, and all of those are helping you make sense of the vast amounts of numbers out there.”

In the example of online shopping, Slavin says that recommended purchases might have once been something a software programmer would have had to manually set up.

“The difference is that this can now all be done automatically by machine learning models,” he said. “Even better, the models continue to learn as they receive new data. So, as trends change, the models can keep up automatically.”

But data science doesn’t just allow Amazon to recommend new products to you, Tesla to find the optimal locations for their charging stations, or doctors to diagnose patients more accurately thanks to improved medical imaging. The professors acknowledge more of it has become a vital component in fields from scientific research to medicine, marketing, agriculture, finance and civic planning. Even construction is incorporating data science.

“It's commonly thought that creating big structures is simply a matter of designing, acquiring materials and building,” Slavin said. “But by incorporating data about previous builds, materials, weather, traffic, supply chains and various logistics, data science can help us plan out virtually every aspect of large-scale projects. As a result, time and cost can be minimized while maximizing safety.”

The rapid spread of data science is also a meaningful educational opportunity for students who are interested in the field but intend to pursue other career paths. For example, Ashwin Malshe, associate professor of marketing, teaches his students how to use sentiment analysis—using tools like artificial intelligence to analyze the emotional content of subjective information such as Twitter posts—to explore how businesses can improve their customer service.

“That’s what I want students to really understand,” Schroeder said. “Trying to find a way to incorporate data science training into your major to increase your marketable skills is really important, because there’s going to be a high demand for people who have baseline knowledge in data science.”

Even highly trained professionals in other fields can benefit from a background in data science. For example, with the increase of artificial intelligence and data analytics, UTSA now offers a program across multiple departments, namely Computer Science, Math, and Data Analytics, where students with different backgrounds, including medicine, can earn a master’s degree in artificial intelligence (MS in AI) and apply that knowledge to their corresponding fields. Desai says this is advantageous because experience in other fields often provides data scientists insight into challenges that need solving and how to find solutions.

The UTSA professor advises students interested in pursuing a degree in data science to take their other passions and interests with them.

“They can work toward their own field,” he said. “Data science touches on everything, so it’s easy for students to carry on with what they’re doing while adding on a new skillset in data science.”

— Christopher Reichert