Data Science in the Enterprise: Insights from eBay, Stitch Fix, Teleon Health, and RISELab

We recently hosted a panel discussion with several data science leaders about organizational design and tooling for enterprise data science. Watch the full video:   The MIT Club of Northern California is running a year-long, monthly series of data science and AI events for alumni and guests. Domino hosted “Data Science in the Enterprise,” a […]

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Programming collective intelligence for financial trading

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. […]

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Creating large training data sets quickly

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, […]

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Improving Zillow’s Zestimate with 36 Lines of Code

Zillow and Kaggle recently started a $1 million competition to improve the Zestimate. We are releasing a public Domino project that uses H2O’s AutoML to generate a solution. The new Kaggle Zillow Price competition received a significant amount of press, and for good reason. Zillow has put $1 million on the line if you can […]

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Data Scientists are Analysts are Software Engineers

In this Data Science Popup session, W. Whipple Neely, Director of Data Science at Electronic Arts, explains why data scientists have responsibilities beyond just data science.   Video Transcript I wanted to talk to you a little bit about something that I’m surprised to find is apparently so current for everyone, which is this engineering/data […]

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Horizontal Scaling for Parallel Experimentation

The amount of time data scientists spend waiting for experiment results is the difference between making incremental improvements and making significant advances. With parallel experimentation, data scientists can run more experiments faster, leaving more time to try novel and unorthodox approaches—the kind that lead to exponential improvements and discoveries. In a previous article we demonstrated […]

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What Data Scientists Should Know About Hiring, Sharing, and Collaborating

In this post we summarize some of our most recent and favorite answers on Quora to questions from the community about hiring junior data scientists, sharing work with the public, and collaborating. Much is said in the data science and machine learning space about new and exciting methods, tools, and advancements (like deep learning, automl, […]

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Data science and deep learning in retail

[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, […]

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