Hilary mason what is a data scientist

Short description : American data scientist. New York City. Fast Company. Crain's New York blog. Grinnell College. One-time aspiring taxi driver. Your nerd best friend. What Science Lovers Link to Most.

Category : Data scientists. Navigation Navigation Add a new article Search in all topics Search in namespaces Search in categories Search using prefix. Wiki tools Wiki tools Special pages. Page tools Page tools. Userpage tools. Other projects In other languages Add links. Corporations, unsurprisingly, tend to be the most locked-down and opaque about their use of tools and their findings. One of the great promises of the new crop of data-exploration software is that the inflection point of value in the data chain moves toward the right—out of heavy-duty processing systems that are expensive, complicated and must be maintained by IT, and into lightweight solutions almost anyone can use.

Cultivating an organizational and recruiting culture that will support data science is an essential task. That will involve finding people who are driven to solve problems and who can use a multitude of skills to build an infrastructure for handling large amounts of data, Mason says.

Closing the skills bottleneck around data science will be the combined job of easier-to-use tools, educators, and employers seeking out people with the right skills combination, Mason says. Mason also sees a lot of self-education, where people have two of the three essential components for conducting a productive inquiry and teach themselves the third on their own, once they sense how close they can get—and the fact that there is a market for that combination of skills. Organizations that are invested in building a data-driven culture do well to avoid two common pitfalls, Mason says.

And, just as importantly, data is useful only in the context of the problem that needs to be solved. This is why finding people who can ask the right questions, and supporting them, is more important than finding people who seem to have all the answers, or limiting oneself to the latest technology. Another core problem that holds back the data-driven culture is assuming that advanced algorithms will solve everything without skilled human contextual interpretation, or that data will speak for itself.

Mason cites the recommendation engine of Netflix as a limited technology that could be vastly improved. Additionally, data scientists are responsible for effectively communicating the things that they learn. That might be creating visualizations or telling the story of the question, the answer, and the context beautifully. Follow Dan Woods on Twitter. He consults for many of the companies he writes about. This is a BETA experience. You may opt-out by clicking here.

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