Subject archive for "data," page 9

This is a Data Science Popup session by Hristo Spassimirov Paskov, Founder & CEO of ThinkFast.

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.

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 leads to exponential improvements and discoveries.

Every day, 100,000 new escort ads are posted online. That is according to Thorn, a nonprofit that fights child sexual exploitation through technology innovation. Other studies by Thorn on the underage sex trafficking situations of survivors have shown that 63% of them had been advertised online at some point. The massive online commercial sex market is extremely difficult to fight, which has inspired the start of an annual hackathon to bring cross-industry experts together to work on child safety.

Perhaps the biggest insight in feature engineering in the last decade was the realization that you could predict a person's behavior by understanding the behavior of their social network. This use of non-traditional social data has driven a significant amount of economic growth as it revolutionized the accuracy and applicability of models.

It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our understanding, then we're probably not smart enough to validate those models (“debug”) effectively.