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CIS 399: Foundations of Data Science is a new course created by a group of engineering students at Penn. 

Credit: Guyrandy Jean-GIlles , Guyrandy Jean-GIlles

Most computer science courses are born from a professor's research or are part of a prescribed track within the major. But CIS 399: Foundations of Data Science — newly offered this semester — began with a few students and a good idea. 

The idea for the course was sparked when a small group of engineering students of various majors approached professors teaching computer and information science and electrical and systems engineering. They had found an interesting textbook on data science and asked six or seven different professors to teach the course.

“The students’ enthusiasm for the subject was contagious and all of us agreed to team-teach the course, although this would be in addition to courses we were already assigned to teach,” said Sampath Kannan, department chair of Computer and Information Science. The professors managed to schedule a time for the course into each of their schedules, which led to the unorthodox seminar-style course meeting once a week for three hours on Thursday afternoons.

In the course, students learn about patterns, higher-level algorithms and how to apply them to situations that require computers to analyze “big data.”

The professor leading the course is ESE professor Victor Preciado, who teaches some similar classes such as ESE 635: Distributed and Networked Dynamical Systems.

Because it is a new course, students looking to enroll lack information on current students' opinions. When given information about the course, ESE freshman Scott Isett was skeptical. 

“The fact that it’s a new course does bother me, and I wouldn’t want to take it right now," Isett said. "The premise does sound pretty interesting, though, and I might want to take it a few years down the road when I know more about it."

Students have begun to study data science in the hopes that they can apply what they learn to other classes they are taking.

“So far, students seem to be loving the material. Their hope is to learn techniques that they hope they can apply to data sets they encounter," Kannan said. "I would also expect a number of students to become interested in research on the topics of the course."

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