The Value of a Data Science Degree, Told by Microsoft’s Chief Data Scientist
BY Meghan MalasMay 26, 2022, 7:08 PM
Courtesy of Juan M. Lavista Ferres
Juan M. Lavista Ferres learned to code at the age of eight and a few decades later his childhood interest in programming and technology blossomed into a successful career, including a current stint at Microsoft from over 13 years old.
Lavista Ferres wears a lot of hats at Microsoft these days. He is the tech giant’s chief data scientist and vice president, 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 global issues ranging from preserving native languages to developing an emissions tracker to share the climate impacts caused by global shipping..
A veteran of data science – or as much of a veteran as anyone can be in this burgeoning field – Lavista Ferres has spent the majority of his 20+ year career in data roles. Before joining Microsoft in 2009 as a senior data scientist, he was chief technology officer for Alerts.com and worked in software development at the Inter-American Development Bank. Back then, there was no career path for data scientists, he says. This has since changed; Job postings for data scientists have increased by 480% since 2016 according to data from Glassdoor.
In 2005, Lavista Ferres earned a master’s degree in machine learning and data mining from Johns Hopkins University. This program was the equivalent of a data science degree program, he says, because he attended graduate school before such programs existed.
Lavista Ferres has also made significant contributions to data science education: he is a member of the American Statistical Association’s committee on data science and artificial intelligence, which includes work on program accreditation data science; it collaborates with data science students at the University of Washington on their data science program; it partners 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 grown 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 Changed Over Time
Fortune: How has the field of data science evolved since you started your career?
Lavista Ferres: Data science is the combination of all the knowledge that each discipline had to create to manage data. From economists to statisticians to computer scientists to physicists, every discipline needed to work with data, and all of these disciplines independently created methods for working with data. Once you put all of that together, it’s essentially what we think of as data science today.
In the early to mid-2000s, it became clear that working with data was a critical function. And that’s what led to the idea of creating a data science discipline, because we need people who are good at dealing with data and trying to get value out of it.
The methods we use today are basically the same methods that have been used for 100 years. Many of the machine learning algorithms we use today were created 20-30 years ago, even some of the deep learning algorithms that were created are still quite old. But many of these algorithms were not used because there was not enough data. Today we have a lot more processing power and a lot more data than we had before.
Fortune: Of the data scientists you currently work with, do you see more data science graduates?
Lavista Ferres: Of course, but at least in my experience at Microsoft, the best teams are those with very diverse profiles. Currently on my team I have economists, statisticians, computer scientists, physicists, people with a background in electrical engineering – and there are those who come from new disciplines that are primarily data science and we work with them.
I work with academics who are creating these data science programs, but in general I would say that you don’t need to have studied data science to be a data scientist. To be transparent, the majority of data scientists working at Microsoft and in the tech industry today didn’t particularly study data science, but they did study a discipline that had a lot of data science elements.
How data science degree programs can advance this field
Fortune: Why do you think data science degree programs are valuable?
Lavista Ferres: I think there are so many problems that can and should be solved with data. Today, almost every organization that collects data needs data scientists. We have to remember that we have seen exponential data growth, there has been a huge reduction in the cost of storing data and a huge increase in processing power. All of these things are expected to continue to grow and provide incredible opportunities that did not exist before.
In our work for AI for Good, the issues we address are very diverse. We work on everything from a particular set of cancers to working with giraffes, to working with beluga whales to working on satellite imagery to understanding wildfires. The magic of data science is that even though these problems are really different, from a pure data science perspective, the solutions are the same.
It’s very powerful because you can work on all of these processes as long as you have the data and as long as you have a subject matter expert who understands the problem. For this reason, there is a huge need for data scientists to take advantage of this data. I think there’s an unmet demand, and a lot of these programs help people get the skills they need.
The majority of skills you learn on the job. However, there is a skill base you need. I learned to code very early in my career, but in my degree program I improved my coding and learned the basics of machine learning and algebra. I also think it’s very easy for us computer scientists to work with data without understanding what you can and cannot do with it. So you need a solid background in statistics.
The future of data science
Fortune: Given that data science is so widely applicable and in demand, where do you see the field going in the future?
Lavista Ferres: Whether you are a physician or a physicist, data science will continue to be an integral part of your work. Right now, many data scientists are filling a void because all of these disciplines have data and can do something with it, but lack the skills.
Eventually, I think all of these disciplines will evolve to the point where data science will be part of their curriculum. I think there’s huge value in data science in general, but you’ll see a lot of disciplines will experience something similar to what happened with coding. Originally, the only people who had coding skills were those studying computer science. Now, the majority of people we hire who haven’t studied computer science still have coding skills. So I think the same will happen with data science.
Fortune: What other skills are essential as a data scientist?
Lavista Ferres: The most important skill is curiosity, it is something that is definitely needed. Some people have a great capacity for curiosity and are just able to ask the right questions. It is also important to understand impact, which means being able to translate an important question into a data science task. Also, to have an impact, you need to be able to explain what the data says and doesn’t say in simple terms, in a way that other people can understand.
Doing complicated and intricate things is the easiest way to impress people. But if you really want to impact the world, your solutions must be simple. Building simple solutions is difficult, but it’s very powerful.