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With Daniel Whitenack (Data Scientist & Lead Developer Advocate, Pachyderm).
Fri, Jun 23, 2017 @ 12:30 PM   FREE   USF, 101 Howard St
 
     
 
 
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Speaker: Daniel Whitenack,Data Scientist, Lead Developer Advocate at Pachyderm

Despite the many amazing applications of statistics & machine learning in industry, many attempts at doing "data science" are anything but reproducible. This can be particularly alarming in industries that require processes to be audited or in light of recent government regulations giving users a "right to an explanation" for algorithmic decisions. In this session, I will discuss the importance of reproducibility & data provenance in any data science organization, & I will provide some practical steps to help scientists build reproducible data analyses & maintain integrity in their data science applications. I will also demo a reproducible data science workflow that includes complete provenance explaining the entire process that produced specific results.

Daniel(@dwhitena) is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO).Danieldevelops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, & more. He has spoken at conferences around the world (Datapalooza, DevFest Siberia, GopherCon, & more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, & is actively helping to organize contributions to various open source data science projects.

 
 
 
 
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