This event will be Livestreamed here:
https://livestream.com/metis/events/8565420
Python is the de facto language for data science & machine learning, but the PyData stack (Pandas & SKLearn) can limit projects.
Apache Spark is a world-class engine for transforming & modeling arbitrarily large data sets, & is largely based on the Pandas & SKLearn workflow. Even better, the PySpark interface allows for complex, big data workflows while still coding in Python.
Join us to learn more about expanding your toolset, & learning about when big data on Spark might make sense for your workflow.
Brendan Herger enjoys bridging the gap between data science & engineering, to build & deploy data products. He is a Senior Data Scientist at Metis.
Brendan brings a unique combination of machine learning, deep learning, & software engineering skills. In his previous work at Capital One & startups, he has built authorization fraud, insider threat, & legal discovery automation platforms. In each of these cases he's lead a team of data scientists & data engineers to enable & elevate his client's business workflow (and capture some amazing data).
When he's not knee deep in a code base, Brendan can be found traveling, sharing his collection of Japanese teas, & playing board games with his partner in Seattle.
About Metis
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, & corporate programs based in Seattle, New York, Chicago, & San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, & more. Students & 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.
Learn more about us at
https://thisismetis.com/
Join our Metis Community Slack channel!Apply here:
http://bit.ly/MetisCommunitySlack
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