University of Minnesota Data Science M.S. Program Ranked Third in Nation
Original article by Meghan Malas, Fortune Education
Data science is one of the fastest growing fields today with job openings for this role growing 480% since 2016, according to Glassdoor. Companies and organizations of all sizes and industries are seeking data-savvy professionals that can create value from the massive volumes of data available. To meet this steep demand, schools are unveiling more degree programs in data science.
Drawing on a wide grange of perspectives—ranging from software development to story-telling—data scientists must be versatile to be successful. And data science students often land six-figure salaries after graduating from master’s degree programs. While getting a master’s degree isn’t the only way to break into this red-hot field, it is a direct and promising avenue.
Just ask Miguel Miguélez Díaz, a business intelligence engineer at Amazon who received his job offer months before he graduated in May 2022. At the University of Minnesota, where Miguélez pursued his master’s degree in data science, about 90% of data science program students find employment before they graduate. The university landed No.3 on Fortune’s ranking of the best master’s degree programs in data science.
To learn what makes a master’s degree program in data science worthwhile, Fortune spoke with Miguélez and two professors from the University of Minnesota.
Data scientists with in-depth knowledge are in high demand
In 2017, Miguélez was working as an intern at a consulting company, finding ways to reduce costs and maximize profits for different companies. It was during that experience that he started to utilize elements of data science in his analysis—and it proved to be so beneficial that he wanted to learn more.
While his bachelor’s degree in industrial engineering prepared him for his consulting job, if he wanted to do more in-depth analysis—and get more robust results—he knew he had to update his skill set. This is a common thread among students in a master’s degree program in data science.
“There is a very recent trend to create many undergraduate degree programs in data science to satisfy the demand for data scientists,” says Daniel Boley, a professor of computer science and engineering and director of the data science graduate program at the University of Minnesota. “But since data science is such a fast-evolving field with many new experimental methods proposed every year, many industries will still seek the maturity and deeper skills that come with a graduate degree.”
Even managers are seeking to understand the discipline so they can more effectively wield the power of data. A master’s degree program helps professionals focus on the capabilities and application of data and technology—which includes the use of artificial intelligence (AI) and machine learning.
“Many successful methods in AI and machine learning are data-driven and hence, inseparable from what could be thought of as data science,” Boley says. “The methods may evolve, but data will continue to be the basis for advances in machine learning, AI, and data science—practitioners who know how to handle and analyze data will continue to be in demand.”
An undergraduate degree program in data science can grant a student a highly in-demand skill set, but insight from the work experience a student has before pursuing a graduate degree in data science is immensely valuable.
“In a master’s program, students learn how to translate actual real-world problems into technical solutions much more efficiently,” says Jaideep Srivastava, professor of data science and director of undergraduate studies at the department of computer science and engineering at the University of Minnesota. “The demand for data scientists has been increasing, but nowadays, a data scientist’s depth of experience and knowledge is becoming more of a focus.”
High salaries are common for master’s in data science graduates
Whether students come into a master’s degree program in data science with a bachelor’s degree in a related field or they are looking to completely pivot careers, securing an advanced degree typically means a significant pay raise.
This fact was not lost on Miguélez when he made the decision to pursue his master’s degree in data science. Miguélez also considered pursuing an MBA, but ultimately decided on data science instead because the program was more affordable—and his salary would be comparable to that of an MBA grad, he says.
Obtaining data science skills also grants a promising path to working at big companies, or moving to a larger metropolitan area—both of which can make for higher pay.
The average annual wage for data science in the U.S. is $108,660, according to the Bureau of Labor Statistics. But in several cities—such as San Jose, San Francisco, Seattle, New York City, Washington D.C., Austin, Texas, and Charlotte, North Carolina—data scientists make an average salary of more than $120,000 a year. On average, graduates from University of California, Berkeley‘s highly-ranked online master’s degree program in data science earn more than $155,000 a year.
Additionally, while Miguélez worked at his previous job at a small consulting company in Spain, he saw the opportunities larger companies afforded their employees. He figured working at a larger company would open more doors for him, which also fueled his decision.
“When you already have a job, going back to school is not easy,” Miguélez says. “I kept thinking of the opportunities and I saw the potential salary, so I decided to go for it.”
A master’s degree in data science is versatile
The power of data is not limited to any one sector or industry, says Srivastava. In business, technology, health, and social sciences, there are a lot of opportunities for data-savvy professionals to find new solutions and gain a deeper understanding of what the capabilities and limitations of data science are in their professions.
Data scientists may go on to pursue a wide range of careers after receiving their master’s degrees, even if they aren’t sure what path they want to take when they begin.
“Most of our graduates go to work in a variety of industry jobs—our graduates have been employed at high-tech firms like Amazon, at retail stores like Target, and at business firms like Capital One Bank and CHS Industries,” says Boley. “Most but not all of our students do not have a specific employment goal in mind when they enter our degree program.”
Many master’s students are trying to fill in a skill or knowledge gap they’ve noticed in their careers, Srivastava adds. Some people who are used to working in the data-handling and computational side of a project may not be familiar with the real-world application of their analysis, and vice versa.
Additionally, some prospective students are interested in how the latest technical advancements can be utilized. Massive data sets can be overwhelming, and inspire many professionals to learn about machine learning techniques, which are typically covered in a master’s degree program in data science.
Those pursuing research may also seek out a master’s degree in data science to enhance their data skills for the technical field they are in. At the University of Minnesota, Ph.D. students who are also pursuing a master’s degree in data science come from varied fields of study.
“Chemistry, electrical engineering, bioinformatics, astronomy, economics, and in the health sciences such as radiology and surgery—these are all topics in which data plays a big role,” Boley says.