Archive of posts with the tag

  • NICAR 2015: Machine learning lessons for journalists

    Machine learning is certainly not a new concept in journalism, but it seemed to enjoy plenty of prominence at NICAR this year — fantastic news for newbies to the field like me. I attended several sessions on it, both theoretical and technical, and a few key concepts came up repeatedly. Whether this year’s conference was your first exposure to machine learning, or you’re a seasoned pro, here are four takeaways worth reviewing: Machine learning is...

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  • NICAR15: 10 tips to avoid data mistakes in the newsroom

    A big mistake when dealing with data can ruin your day. Luckily there are simple ways to avoid big mistakes and maintain credibility with your colleagues and your audience. At NICAR 2015, a panel of data journalists from The New York Times, Wall Street Journal and Atlanta Journal-Constitution discussed the road blocks they've encountered when working with data for a story. The panel, moderated by MedPage Today reporter Coulter Jones, featured advice and cautions to keep in mind when dealing with numbers,...

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