Building a Data Science Team

Data Science teams can provide immense value to an organization if built or it can provide no value at all. Sometime the difference in success comes down to the simple fact that you didn’t actually need a Data Science team to begin with. Other times it comes down to how you hire, manage, grow and nurture the team. In this post we’ll cover all these topics and more.

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Wikipedia Data in Apache Spark and Scala (Updated)

More than you possibly ever wanted to know about parsing various Wikipedia data sources in Spark and Scala.

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The Flavors of Data Science and Engineering

Data Science means something different to everyone and is more of a marketing terms than a job description nowadays. That said, certain definition for it and the related disciplines are starting to emerge from what I've seen so I wanted to write down my perceptions.

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ONC Patient Matching Challenge: Part 2

This is the second of a two part series on tackling the ONC Patient Matching Challenge In the first part we went over the background and high level approach while in this part we cover the matching engine that was built (and how to use it).

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ONC Patient Matching Challenge: Part 1

This is the first of a two part series on tackling the ONC Patient Matching Challenge. The first part reviews background and high level topics while the second part covers the matching engine that was built (and how to use it). 

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Peapod: A Scala and Spark Data Pipeline and Dependency Manager

Peapod is a new dependency and data pipeline management framework for Spark and Scala. The goals is to provide a framework that is simple to use, automatically saves/loads the output of tasks, and provides support for versioning.

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Data Pipeline and Task Management: The Unsolvable Problem?

There’s probably more well known data pipeline dependency management and scheduling frameworks than you can say in one breath. Is there a reason for that beyond mere not invented here syndrome?

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In the Spirit of Thanksgiving

We don't take ourselves seriously but are two curious folks passionate about applying Machine Learning and Deep Learning to industry and sharing that knowledge with the broader engineering community.

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