The value of a data science degree, as told by Microsoft’s chief data scientist – Fortune

BY Meghan MalasMay 26, 2022, 7:08 PM

Courtesy Juan M. Lavista Ferres

Juan M. Lavista Ferres learned to code when he was 8 years old, and a few decades later his childhood interest in programming and technology evolved into a fruitful career—including a current stint at Microsoft of more than 13 years.

These days, Lavista Ferres wears a lot of hats at Microsoft. He’s the chief data scientist and a vice president at the technology giant, as well as a lab director overseeing Microsoft’s AI for Good. This program uses artificial intelligence and data science to help develop solutions to world issues ranging from the preservation of native languages to developing an emissions tracker to share the climate impacts caused by global shipping.

A data science veteran—or as much of a veteran as someone can be in this burgeoning field—Lavista Ferres has spent a majority of his 20-plus career in data roles. Prior to joining Microsoft in 2009 as a senior data scientist, he was the chief technology officer for and worked in software development at the InterAmerican Development Bank. Back then, there was no career path for data scientists, he says. That’s since changed; job openings for data scientists have grown 480% since 2016, according to Glassdoor data.

In 2005, Lavista Ferres completed a master’s degree in machine learning and data mining at the John Hopkins University. This program was the equivalent of a data science degree program, he says, as he attended graduate school before such programs existed.

Lavista Ferres has also contributed significantly to data science education: He’s a member of the American Statistical Association Committee on Data Science and Artificial Intelligence, which includes work in data science program accreditation, and he collaborates with University of Washington data science students as part of their data science curriculum. He is also partnering with Harvard University on the Harvard Data Science Initiative, and he works with Stanford University’s RegLab. 

Fortune spoke with Lavista Ferres about how the field of data science has developed and how data science degree programs fit into the picture. 

The following interview has been edited for brevity and clarity.

How data science careers have evolved over time

Fortune: How has the field of data science developed since you started your career?

Lavista Ferres: Data science is the combination of all the knowledge that every discipline out there had to create in order to manage data. From economists to statisticians to computer scientists or physicists—every single discipline had to work with data, and all of these disciplines independently created methods to work with data. Once you combine all of these, that’s basically what we consider data science today.

In the early- to mid-2000s, it became evident that working with data was a core function. And that’s what led to the idea of creating a data science discipline because we need people with expertise in dealing with data and trying to get value out of data. 

The methods that we use today are basically the same methods that have been used for 100 years. A lot of the machine learning algorithms that we use today were created 20 to 30 years ago—even some of the deep learning algorithms that were created are still pretty old. …….


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