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Despite the many amazing applications of statistics, machine learning, and visualization in industry, many attempts at doing "data science" are anything but scientific. Specifically, data science processes often lack reproducibility, a key tenet of science in general and a precursor to having true collaboration in a scientific (or engineering) community.
In this session, Daniel Whitenack (https://www.linkedin.com/in/danielwhitenack), Data Scientist and Lead Developer Advocate at Pachyderm, will discuss the importance of reproducibility and data provenance in any data science organization and will provide some practical steps to help data science organizations produce reproducible data analyses and maintain integrity in their data science applications. He will also demo a reproducible data science workflow that includes complete provenance explaining the entire process that produced specific results.
About the Speaker
Daniel (@dwhitena) is a Ph.D.-trained data scientist working with Pachyderm (@pachydermIO), where he develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He's spoken at conferences around the world (Datapalooza, DevFest Siberia, GopherCon, and more), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
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