Academic Scientists

We teach BIODS 253 at Stanford — a software engineering class designed to help scientists do better science. Hear Vijay talk below on why these skills are crucial for students today.

Sign up for BIODS 253

Software is everywhere in science

This 2-unit course makes it easy to master the skills you need to be a productive scientist in today’s world, including using git (source control), cloud computing (AWS/GCP/Sherlock/etc.) writing clean code, tests, managing dependencies, the best ways to collaborate with fellow researchers and how to conform to NIH’s new compulsory code and data sharing guidelines.

Designed based on 20 years of experience writing and maintaining complex systems, managing engineering and product teams at Google, Twitter and biotechs.

Improve the quality and velocity of your research

Help you publish more often

Make it easier to collaborate with peers

Equip you for a career in academia or industry.

Sign up for BIODS 235

"Having never learned software engineering skills and best practices before, I didn't know what I was missing. In this class, we learned foundational skills that all scientists working with code should understand. Best of all, it greatly improved my productivity and the quality of my research."

Ben Viggiano, Stanford University

non-Stanford students

Join the waitlist for this class being released online and in other universities.

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Who teaches this class?

At Stanford, this class is taught by Vijay Pandurangan, Adjunct Professor at Biomedical Data Science (Stanford School of Medicine) and Teri Klein, Professor Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics.

Is this class only taught at Stanford?

At the moment, yes. But we're launching an online course soon and would like to create satellite courses across other universities across the US, UK and Canada. If you'd like to see this course at your university, email us at

How will this class benefit me as a PI or a Professor?

This course will help your students and researchers collaborate more effectively, produce better science and iterate more quickly. We discuss how to meet the NIH data and code sharing guidelines required with all new grants, and prepare your students to be PIs or immediately hirable in industry.