Chandrasekhar RamakrishnanSr. Software Engineer at Swiss Data Science Center
My background is in technology and the arts; my experience encompasses user interfaces, server-side logic, database models, and realtime DSP. I have built web-based, desktop, client-server, and mobile applications in HTML5, C, C++, Java, Objective-C, and Smalltalk. Most recently, I have been working on data analytics in Python.
I am a citizen of both USA and Switzerland, a native speaker of English, fluent in High German (written and spoken) and a Swiss German dialect (spoken).
About the talk
The expectation of reproducibility in scientific work has been long established, and, increasingly, communities and funding sources are actually demanding it. Within the Python ecosystem, there are a variety of tools available to support reproducible data science, but choosing and using one is not always straightforward. In this tutorial, we will take a closer look at the concept of _reproducibility_, and, we will examine the technologies that provide building blocks and survey the landscape of tools. We spend the majority of the time looking at two solutions in particular, Code Ocean and Renku, and work through end-to-end scenarios in both.
See tutorial materials here: https://www.scipy2019.scipy.org/tutorial-participant-instructions
See the full SciPy 2019 playlist at https://www.youtube.com/playlist?list=PLYx7XA2nY5GcDQblpQ_M1V3PQPoLWiDAC
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