Computational thinking is for everyone

As a freshman Duke student pursuing a career in medicine, Benjamin Asomani was curious about computer science and coding, but was wary of taking courses without prior exposure to the field.

At the suggestion of another student, he took part in the Summer 2021 Code+ program, while learning CSS and HTML skills as part of an applied group project, reaffirming his interest in computer science. This semester, Asomani begins courses for a minor in computer science to complement his planned major in biology.

“Hands-on learning was a great way to get familiar with coding. I liked the process of building and building something for the project and had instructors who could help us review our work and spot errors,” Asomani said Said, who completed the program confidently in his skills in CSS and HTML.

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

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

For undergraduate Harsh Sreejay, the +DataScience Advanced Research Program for the summer of 2020 recognized his interest in bioinformatics for his interest in bioinformatics for a project exploring the use of predictive models for respiratory disease diagnosis. Large companies added.

“I am more interested in practical work than theoretical modeling, and this project allowed me to focus on using the tools of data science to solve real-world problems,” Sreejay said.

To support education, CCT works with Duke faculty and departments to integrate computer-related material into its courses, and provides teaching modules to complement faculty teaching.

Provost Sally Kornbluth said, “Learning to draw important conclusions from data and use computational approaches to solve complex problems across disciplines are important elements of 21st century liberal arts education.”

“CCT adds to existing resources at Duke – and addresses gaps in our current offerings – to ensure that all students and faculty have the opportunity to apply these approaches in their studies and research.”

Kornbluth recently hired Professor Matthew Hirsch of Duke School of Medicine to lead the center to work closely with computer colleagues across campus.

Hirschi, a molecular physiologist who many years ago embraced data science to improve his own skills and the data analysis capabilities of his lab, is committed to helping students and colleagues realize the benefits of computational approaches. Is.

“As someone who has recently started math, my opinion is that this is something that everyone should know,” Hirschi said. His vision includes helping students already immersed in computer science understand ethics, policy and its interface with other fields.

And for liberal arts students and scholars, Hirschi wants CCTs to help “become comfortable and able with math and computing tools to extract meaning from data, regardless of their field,” he said. “Because students of the current generation of liberal arts must understand how to use computational approaches to find patterns in literature, art or dance.”

In a data science mini-course led by Hirschi, Ph.D. Students Taylor Chavez and Jessica Portillo learned computational skills with immediate application to their research. “I come from a wet lab background and it has provided the foundation and basic components of what I need, and made it clear that I want to do more computer work in the future,” Portillo said.

Chavez studied tissue engineering and based on course assignments began working with data from his experiments. “I have some coding background, but not really enough,” she said.

“It was the first time I had actually taken 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 used different-integrated methods to visualize the results.” Played with different ways, the way I wanted to present my data.”