Rethinking Software Testing for Data Science


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Ensuring that data pipelines are reproducible at all times is primordial to trust our results. Drawing inspiration from software engineering, this talk describes a practical framework to make continuous testing feasible by tackling the four challenges that data-intensive software posits: effective test cases, structure, speed and upstream data changes.


Eduardo Blancas

Hi, this is Eduardo. I am broadly interested in developing tools that helps us deliver reliable data products. Towards that end, I developed Ploomber, an open-source Python library. I hold an M.S in Data Science from Columbia University, where I conducted research in computational neuroscience. I started my Data Science career in 2015 when I joined the Center for Data Science and Public Policy at The University of Chicago.