Data Science

5 lessons we learned about data science in 2013

Most people know what marketing executives do every day. They try to catch people’s attention through email, ads, tweets, and press releases. As for data scientists, well, their work is not nearly as well understood.

That’s been slowly changing this year as companies slowly loosen up about letting their hard-won data scientists talk about their work.

This year, VentureBeat has learned a lot about these fawned-over specimens. But our knowledge isn’t always delivered at once. That’s why we’ve brought together some of the lessons we’ve picked up in 2013:

Data scientists should be creative

This point became clear as Jeremy Howard, the former president of data science competition-holder Kaggle, spoke with fellow luminaries in the field at VentureBeat’s 2013 DataBeat/Data Science Summit event a few weeks ago.

While popular algorithms such as Random Forests often have a hand in helping people win Kaggle competitions, “the best applied real-life data scientists … are extremely creative,” Howard said. They pore over the raw data for the competitions they enter and discern patterns. Howard prefers to just jump into the data without any context about it.

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