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Python is the de facto language for data science and machine learning, but the PyData stack (Pandas & SKLearn) can limit projects.
Apache Spark is a world-class engine for transforming and modeling arbitrarily large data sets, and is largely based on the Pandas and 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, and learning about when big data on Spark might make sense for your workflow.
Brendan Herger enjoys bridging the gap between data science and engineering, to build and deploy data products. He is a Senior Data Scientist at Metis.
Brendan brings a unique combination of machine learning, deep learning, and software engineering skills. In his previous work at Capital One and startups, he has built authorization fraud, insider threat, and legal discovery automation platforms. In each of these cases he's lead a team of data scientists and data engineers to enable and 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, and playing board games with his partner in Seattle.
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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