Ensuring that the results of data analysis are both valid and reproducible is a fundamental responsibility of every data scientist, but both are increasingly difficult in the context of complex analysis workflows and big data.
Building off of ideas from software engineering, Russell A. Poldrack, PhD will argue that we need to embrace a culture of computational reproducibility. Poldrack will outline a set of values that motivate this work and principles that guide the work, and then focus on a set of practices that can help improve reproducibility in computational data science. Poldrack will conclude by addressing some potential concerns about the impacts of this cultural shift, particularly for early career researchers.
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Presenter
Russell A. Poldrack, PhD, Professor; Psychology
Stanford University