Mentoring with Code: Best Practices for Data Science in Epidemiology (feat. Jade Benjamin-Chung) cover art

Mentoring with Code: Best Practices for Data Science in Epidemiology (feat. Jade Benjamin-Chung)

Mentoring with Code: Best Practices for Data Science in Epidemiology (feat. Jade Benjamin-Chung)

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“We're all used to tracking changes in Word, so why wouldn't we want to have something like that for our code? And we're all used to Google Docs where we can collaborate in real time, so why wouldn't we want to be doing that with our code too? So both for keeping track of changes and for facilitating collaboration, anyone who I work with, I mentor them in using GitHub”

Welcome to Season 10! To kickoff our new season, we sit down with Jade Benjamin-Chung, an Assistant Professor at Stanford University in the Department of Epidemiology and Population Health, to talk about her journey into public health and becoming a leader in reproducible data science practices. Throughout the episode, we discuss the creation of her lab manual outlining best practices in data science, mentoring in low-resource settings, and promoting ethical data practices.

“If a student isn't able to be part of data collection, then I really encourage them to build a relationship with a local collaborator who knows the data really deeply. For example, I'll have a student who is really bright with coding, but has less experience working with real world data sets. I'll have them pair up with someone from, say, Bangladesh, where I do a lot of research, and they'll kind of mentor them in coding…and the person working in Bangladesh will mentor them in the data”



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