Chandrasekhar Ramakrishnan studied mathematics at the University of California, Berkeley (B.A. 1997) and art and computer science at the University of California, Santa Barbara (M.A. 2003). He has worked as a software developer and data-science consultant for companies, research institutions, and NGOs in the US, Germany, and Switzerland. Since 2009, he has been at ETH Zürich supporting projects by developing software solutions for data management, analysis, and visualization.
Reproducibility should be a central consideration for data science processes, but it requires some support to achieve. In this talk, we present the Renku reproducibility platform and how to take advantage of it from Julia focusing two examples: 1. Using Renku to build reproducible workflows in Julia and 2. Facilitating the teaching of Julia-based courses with Renku.