The Data-Driven World
Many people may not know it, but whenever we’re using popular social media sites like Facebook and Instagram, or search engines like Google, we are constantly dealing with data. The ‘cookies’ we accept or the personalized advertisements we see are all evidence of data working behind the scenes. Data is everywhere; it’s the new ubiquitous material that is now used in practically every walk of life for a multitude of purposes. And the field of data science has become extremely relevant for young academics to study.
The University of Minnesota introduced a master’s degree in data science nearly a decade ago, but it wasn’t until 2017 that an interdisciplinary, intercollegiate committee came together to develop a bachelor of science degree (BS) in data science. The degree recently opened for enrollment, with the first batch of students enrolling in the major during the fall 2020 semester. Currently, there are 33 students enrolled in the data science BS program.
The School of Statistics played a key role in the creation of this degree, along with several College of Science and Engineering (CSE) departments— math, computer science and engineering, electrical and computer engineering, and industrial and systems engineering.
Although the major is housed within CSE and the college handles all of the administrative and logistics work, a committee with members from all participating departments makes decisions about matters of policy. Professor Jaideep Srivastava, director of undergraduate studies in data science, chairs this committee.
The Emerging Field
Before data science started to gain relevance, statisticians were analyzing data using statistical methods, while computer scientists were developing techniques to analyze data using large-scale processing, and computation and artificial intelligence (AI). Students in other science and engineering departments were also using specialized knowledge from their fields to collect and analyze data, but it was difficult for these students to obtain data analysis jobs because they were not trained in all of the necessary skills.
Srivastava explains this problem using an analogy from mechanical engineering. “You can get a top view of a building, or you can get a side view of a building, or you can get a front view of a building. But what you really need is the three-dimensional view of how it’s all put together.”
The different departments realized this “three-dimensional view” was not happening in the case of data science. Statistics professor Adam Rothman, who helped co-create the BS degree, echoes this sentiment. “Before we created the degree, some students pursuing data science jobs would either double major in statistics and computer science, or major in one and minor in the other,” Rothman explains.
Creating a separate degree helped combine all of the required knowledge into one program and expanded the job market for students, who are now able to gain access to careers geared specifically toward data science.
“By designing this program, we made sure that [students would have] all the skill sets they need to be a data scientist from the get go,” says Srivastava. Some of the required skills are already housed within existing degrees, so it was sometimes simply a matter of combining different aspects of other degrees to create these data analyst skill sets. For example, one of the required skills comes from the core of the statistics degree. “There is a new course within the major called Regression and Statistical Computing that combines elements of two current undergraduate statistics courses,” Rothman comments.
Developers of the degree believe students enrolled in this major will gain a strong, foundational knowledge in many core subjects like statistics and computer science. Data science is emerging as a promising future for students, and the University can help cultivate that future through degree programs like this one.
This story was written by an undergraduate student in CLA.