One Course: Intro to Data Science

Cornell’s Introduction to Data Science course isn’t like other stats courses.

“Our Intro to Data Science course is about exposing students to reality. Data almost never comes to researchers in a format that is ready for analysis. Yet, nearly all statistics classes teach students using analysis-ready data,” says Watson M. Davis Professor of Mathematics and Statistics Ann Cannon,  who teaches the course with Professor of Computer Science and Mathematics Tony deLaubenfels.

Thanks to the flexibility of One Course At A Time, they were able to mimic a scenario that the students might encounter in the workforce. Students worked in groups—some with a background in statistics and some with a background in computer science—similar to the composition of teams within organizations with working knowledge in one of those two camps.

“Both skills are necessary to accomplish the goals of the class,” Cannon says. “This requires students to work together and communicate, effectively teaching each other. In the end, we hope that students have a much better understanding of the process of data science and have learned to work well in groups.”

“Everyone on the team had different strengths and weaknesses,” says Asher Muse ’21, member of the social media trends team. “Maddie had a statistical background but with minimal coding experience, while Cullen and Cole had never touched statistics but were strong coders. By combining our skill sets, we were able to create something that none of us would be able to complete alone.”

Sound familiar? If it sounds less like a class and more like what we all do every day at our jobs, that was the idea—preparing students for real jobs where they will be collaborating with colleagues with differing skill sets.

Once students cleaned the data, they analyzed it and created websites to visually explain the data and their conclusions. Students then presented both, along with the process decisions they made along the way.

Data science touches nearly all aspects of our lives, and its relevance across disciplines was on display when the students gathered on day 17 of the block to present their work, covering  Mediterranean shipwrecks, social media trends, Kickstarter usage, the Google Playstore apps, Olympics history, and Texas Death Row inmates.

One team investigated ancient shipwrecks and the trends that may have led to their sinking. In addition to data science techniques, they encountered Mediterranean geography, ancient classical timelines, and terminology in classical languages. You can see their findings and final report online.