Computational thinking is for everyone

As a freshman at Duke working toward a career in medicine, Benjamin Asomani was curious to learn more about computer science and coding, but feared diving into classes without first exposure to the field. At the suggestion of another student, he participated in the Code+ summer 2021 program, learning CSS and HTML skills in an applied group project, while confirming his interest in computer science. This semester, Asomani began classes for a computer science minor to complement her planned major in biology.

“Hands-on learning was a great way to get started with coding. I liked the process of creating and building something for a project and having instructors who could help us review our work and spot mistakes said Asomani, who completed the program feeling confident about her CSS and HTML abilities.

Duke’s Center for Computational Thinking (CCT) was launched in 2020 to support and coordinate campus-wide resources for faculty, students, and staff. Its main priority is to ensure that all Duke students can be exposed to computational approaches and learn how to use data to create new knowledge.

For students like Asomani, CCT’s network of programs can provide an introduction to computer science and make computer science majors more accessible by reducing real or perceived barriers to entry.

For undergraduate Harsha Srijay, the DataScience + Summer 2020 Advanced Research Program linked his math and data science majors with his interest in bioinformatics for a project exploring the use of predictive models to diagnose disease respiratory. “I’m more interested in applied work than theoretical modeling, and this project has allowed me to focus on using data science tools to solve real-world problems,” Srijay noted.

To support teaching, CCT works with Duke faculty and departments to integrate computer science-related content into their courses and provides learning modules to supplement faculty teaching.

“Learning to draw critical conclusions from data and adopt computational approaches to solving complex problems across all disciplines are important parts of a 21^st liberal arts education of the century,” said Provost Sally Kornbluth. “CCT connects existing resources at Duke — and addresses gaps in our current offerings — to ensure that all students and faculty have the opportunity to bring these approaches to their study and research.”

Kornbluth recently appointed Professor Matthew Hirschey of the Duke School of Medicine to lead the center, working closely with fellow computer scientists on campus. A molecular physiologist who embraced data science years ago to advance his own skills and his lab’s data analysis capabilities, Hirschey is committed to helping students and colleagues realize the benefits of approaches computers.

“As someone who’s come to calculus fairly recently, my take is that it’s something everyone should know,” Hirschey said. Its vision is to help students who are already steeped in computer science understand its intersections with ethics, politics, and other fields. And for liberal arts students and scholars, Hirschey wants CCT to help them become “comfortable and capable with computation and computational tools to extract meaning from data, regardless of their field of study.” , he noted. “Because today’s generation of liberal arts students should understand how computational approaches can be used to find patterns in literature, art, or dance.”

In a data science mini-course led by Hirschey, Ph.D. students Taylor Chavez and Jessica Portillo learned computing skills with immediate application to their research. “I come from a wet lab background, and this provided the foundation and building blocks for what I need, and made it clear that I want to do more computer work in the future,” Portillo said. .

Chavez is studying tissue engineering and has used class assignments to work with data from his experiments. “I have some coding knowledge, but really not enough,” she said. “It was the first time I took a course designed to teach me how to analyze and visualize experimental data in the context of my science. I brought my own data and was able to play around with different ways to visualize the results in a way that made sense for how I wanted to present my data.